datawizard/0000755000176200001440000000000014174271374012416 5ustar liggesusersdatawizard/NAMESPACE0000644000176200001440000000746314174263303013640 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(as.double,parameters_kurtosis) S3method(as.double,parameters_skewness) S3method(as.double,parameters_smoothness) S3method(as.numeric,parameters_kurtosis) S3method(as.numeric,parameters_skewness) S3method(as.numeric,parameters_smoothness) S3method(center,character) S3method(center,data.frame) S3method(center,factor) S3method(center,grouped_df) S3method(center,logical) S3method(center,numeric) S3method(convert_data_to_numeric,character) S3method(convert_data_to_numeric,data.frame) S3method(convert_data_to_numeric,double) S3method(convert_data_to_numeric,factor) S3method(convert_data_to_numeric,logical) S3method(convert_data_to_numeric,numeric) S3method(data_extract,data.frame) S3method(data_rescale,data.frame) S3method(data_rescale,factor) S3method(data_rescale,grouped_df) S3method(data_rescale,numeric) S3method(describe_distribution,character) S3method(describe_distribution,data.frame) S3method(describe_distribution,factor) S3method(describe_distribution,grouped_df) S3method(describe_distribution,numeric) S3method(kurtosis,data.frame) S3method(kurtosis,default) S3method(kurtosis,matrix) S3method(kurtosis,numeric) S3method(normalize,data.frame) S3method(normalize,factor) S3method(normalize,grouped_df) S3method(normalize,matrix) S3method(normalize,numeric) S3method(plot,visualisation_recipe) S3method(print,parameters_distribution) S3method(print,parameters_kurtosis) S3method(print,parameters_skewness) S3method(print,visualisation_recipe) S3method(ranktransform,data.frame) S3method(ranktransform,factor) S3method(ranktransform,grouped_df) S3method(ranktransform,numeric) S3method(skewness,data.frame) S3method(skewness,default) S3method(skewness,matrix) S3method(skewness,numeric) S3method(smoothness,data.frame) S3method(smoothness,default) S3method(smoothness,numeric) S3method(standardize,AsIs) S3method(standardize,Date) S3method(standardize,character) S3method(standardize,data.frame) S3method(standardize,double) S3method(standardize,factor) S3method(standardize,grouped_df) S3method(standardize,integer) S3method(standardize,logical) S3method(standardize,matrix) S3method(standardize,numeric) S3method(summary,parameters_kurtosis) S3method(summary,parameters_skewness) S3method(unstandardize,array) S3method(unstandardize,character) S3method(unstandardize,data.frame) S3method(unstandardize,factor) S3method(unstandardize,grouped_df) S3method(unstandardize,matrix) S3method(unstandardize,numeric) S3method(winsorize,character) S3method(winsorize,data.frame) S3method(winsorize,factor) S3method(winsorize,logical) S3method(winsorize,numeric) export(adjust) export(center) export(centre) export(change_scale) export(compact_character) export(compact_list) export(convert_data_to_numeric) export(data_addprefix) export(data_addsuffix) export(data_adjust) export(data_extract) export(data_findcols) export(data_match) export(data_partition) export(data_relocate) export(data_remove) export(data_rename) export(data_rename_rows) export(data_reorder) export(data_rescale) export(data_restoretype) export(data_to_long) export(data_to_numeric) export(data_to_wide) export(data_transpose) export(degroup) export(demean) export(describe_distribution) export(detrend) export(extract) export(format_text) export(is_empty_object) export(kurtosis) export(normalize) export(ranktransform) export(rescale_weights) export(reshape_ci) export(reshape_longer) export(reshape_wider) export(skewness) export(smoothness) export(standardise) export(standardize) export(text_concatenate) export(text_fullstop) export(text_lastchar) export(text_paste) export(text_remove) export(text_wrap) export(to_numeric) export(unstandardise) export(unstandardize) export(visualisation_recipe) export(winsorize) datawizard/README.md0000644000176200001440000002743614173025777013714 0ustar liggesusers # `datawizard`: Easy Data Wrangling [![publication](https://img.shields.io/badge/Cite-Unpublished-yellow)](https://github.com/easystats/datawizard/blob/master/inst/CITATION) [![downloads](http://cranlogs.r-pkg.org/badges/datawizard)](https://cran.r-project.org/package=datawizard) [![total](https://cranlogs.r-pkg.org/badges/grand-total/datawizard)](https://cranlogs.r-pkg.org/) [![status](https://tinyverse.netlify.com/badge/datawizard)](https://CRAN.R-project.org/package=datawizard) ***Hockety pockety wockety wack, prepare this data forth and back*** :sparkles: `datawizard` is a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. # Installation | Type | Source | Command | |-------------|------------|------------------------------------------------------------------------------| | Release | CRAN | `install.packages("datawizard")` | | Development | r-universe | `install.packages("datawizard", repos = "https://easystats.r-universe.dev")` | | Development | GitHub | `remotes::install_github("easystats/datawizard")` | # Citation To cite the package, run the following command: ``` r citation("datawizard") To cite datawizard in publications use: Makowski, Lüdecke, Patil, Ben-Shachar, & Wiernik (2021). datawizard: Easy Data Wrangling. CRAN. Available from https://easystats.github.io/datawizard/ A BibTeX entry for LaTeX users is @Article{, title = {datawizard: Easy Data Wrangling}, author = {Dominique Makowski and Daniel Lüdecke and Indrajeet Patil and Mattan S. Ben-Shachar and Brenton M. Wiernik}, journal = {CRAN}, year = {2021}, note = {R package}, url = {https://easystats.github.io/datawizard/}, } ``` # Features ## Data wrangling ### Select and filter The package provides helpers to select columns or filter rows meeting certain conditions: ``` r matching_rows <- data_match(mtcars, data.frame(vs = 0, am = 1)) mtcars[matching_rows, ] #> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 ``` Or do other manipulations: ``` r head(data_addprefix(iris, "NEW_")) #> NEW_Sepal.Length NEW_Sepal.Width NEW_Petal.Length NEW_Petal.Width NEW_Species #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3.0 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5.0 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa ``` ### Transform The packages also contains multiple functions to help transform data. For example, to standardize (*z*-score) data: ``` r # before summary(swiss) #> Fertility Agriculture Examination Education #> Min. :35.00 Min. : 1.20 Min. : 3.00 Min. : 1.00 #> 1st Qu.:64.70 1st Qu.:35.90 1st Qu.:12.00 1st Qu.: 6.00 #> Median :70.40 Median :54.10 Median :16.00 Median : 8.00 #> Mean :70.14 Mean :50.66 Mean :16.49 Mean :10.98 #> 3rd Qu.:78.45 3rd Qu.:67.65 3rd Qu.:22.00 3rd Qu.:12.00 #> Max. :92.50 Max. :89.70 Max. :37.00 Max. :53.00 #> Catholic Infant.Mortality #> Min. : 2.150 Min. :10.80 #> 1st Qu.: 5.195 1st Qu.:18.15 #> Median : 15.140 Median :20.00 #> Mean : 41.144 Mean :19.94 #> 3rd Qu.: 93.125 3rd Qu.:21.70 #> Max. :100.000 Max. :26.60 # after summary(standardize(swiss)) #> Fertility Agriculture Examination Education #> Min. :-2.81327 Min. :-2.1778 Min. :-1.69084 Min. :-1.0378 #> 1st Qu.:-0.43569 1st Qu.:-0.6499 1st Qu.:-0.56273 1st Qu.:-0.5178 #> Median : 0.02061 Median : 0.1515 Median :-0.06134 Median :-0.3098 #> Mean : 0.00000 Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 #> 3rd Qu.: 0.66504 3rd Qu.: 0.7481 3rd Qu.: 0.69074 3rd Qu.: 0.1062 #> Max. : 1.78978 Max. : 1.7190 Max. : 2.57094 Max. : 4.3702 #> Catholic Infant.Mortality #> Min. :-0.9350 Min. :-3.13886 #> 1st Qu.:-0.8620 1st Qu.:-0.61543 #> Median :-0.6235 Median : 0.01972 #> Mean : 0.0000 Mean : 0.00000 #> 3rd Qu.: 1.2464 3rd Qu.: 0.60337 #> Max. : 1.4113 Max. : 2.28566 ``` To winsorize data: ``` r # before anscombe #> x1 x2 x3 x4 y1 y2 y3 y4 #> 1 10 10 10 8 8.04 9.14 7.46 6.58 #> 2 8 8 8 8 6.95 8.14 6.77 5.76 #> 3 13 13 13 8 7.58 8.74 12.74 7.71 #> 4 9 9 9 8 8.81 8.77 7.11 8.84 #> 5 11 11 11 8 8.33 9.26 7.81 8.47 #> 6 14 14 14 8 9.96 8.10 8.84 7.04 #> 7 6 6 6 8 7.24 6.13 6.08 5.25 #> 8 4 4 4 19 4.26 3.10 5.39 12.50 #> 9 12 12 12 8 10.84 9.13 8.15 5.56 #> 10 7 7 7 8 4.82 7.26 6.42 7.91 #> 11 5 5 5 8 5.68 4.74 5.73 6.89 # after winsorize(anscombe) #> x1 x2 x3 x4 y1 y2 y3 y4 #> [1,] 10 10 10 8 8.04 9.13 7.46 6.58 #> [2,] 8 8 8 8 6.95 8.14 6.77 5.76 #> [3,] 12 12 12 8 7.58 8.74 8.15 7.71 #> [4,] 9 9 9 8 8.81 8.77 7.11 8.47 #> [5,] 11 11 11 8 8.33 9.13 7.81 8.47 #> [6,] 12 12 12 8 8.81 8.10 8.15 7.04 #> [7,] 6 6 6 8 7.24 6.13 6.08 5.76 #> [8,] 6 6 6 8 5.68 6.13 6.08 8.47 #> [9,] 12 12 12 8 8.81 9.13 8.15 5.76 #> [10,] 7 7 7 8 5.68 7.26 6.42 7.91 #> [11,] 6 6 6 8 5.68 6.13 6.08 6.89 ``` To grand-mean center data ``` r center(anscombe) #> x1 x2 x3 x4 y1 y2 y3 y4 #> 1 1 1 1 -1 0.53909091 1.6390909 -0.04 -0.9209091 #> 2 -1 -1 -1 -1 -0.55090909 0.6390909 -0.73 -1.7409091 #> 3 4 4 4 -1 0.07909091 1.2390909 5.24 0.2090909 #> 4 0 0 0 -1 1.30909091 1.2690909 -0.39 1.3390909 #> 5 2 2 2 -1 0.82909091 1.7590909 0.31 0.9690909 #> 6 5 5 5 -1 2.45909091 0.5990909 1.34 -0.4609091 #> 7 -3 -3 -3 -1 -0.26090909 -1.3709091 -1.42 -2.2509091 #> 8 -5 -5 -5 10 -3.24090909 -4.4009091 -2.11 4.9990909 #> 9 3 3 3 -1 3.33909091 1.6290909 0.65 -1.9409091 #> 10 -2 -2 -2 -1 -2.68090909 -0.2409091 -1.08 0.4090909 #> 11 -4 -4 -4 -1 -1.82090909 -2.7609091 -1.77 -0.6109091 ``` To rank-transform data: ``` r # before head(trees) #> Girth Height Volume #> 1 8.3 70 10.3 #> 2 8.6 65 10.3 #> 3 8.8 63 10.2 #> 4 10.5 72 16.4 #> 5 10.7 81 18.8 #> 6 10.8 83 19.7 # after head(ranktransform(trees)) #> Girth Height Volume #> 1 1 6.0 2.5 #> 2 2 3.0 2.5 #> 3 3 1.0 1.0 #> 4 4 8.5 5.0 #> 5 5 25.5 7.0 #> 6 6 28.0 9.0 ``` To rescale a numeric variable to a new range: ``` r change_scale(c(0, 1, 5, -5, -2)) #> [1] 50 60 100 0 30 ``` ### Reshape A common data wrangling task is to reshape data. Either to go from wide/Cartesian to long/tidy format ``` r library(datawizard) wide_data <- data.frame(replicate(5, rnorm(10))) data_to_long(wide_data) #> Name Value #> 1 X1 -0.08281164 #> 2 X2 -1.12490028 #> 3 X3 -0.70632036 #> 4 X4 -0.70278946 #> 5 X5 0.07633326 #> 6 X1 1.93468099 #> 7 X2 -0.87430362 #> 8 X3 0.96687656 #> 9 X4 0.29986416 #> 10 X5 -0.23035595 #> 11 X1 -2.05128979 #> 12 X2 0.04386162 #> 13 X3 -0.71016648 #> 14 X4 1.14946968 #> 15 X5 0.31746484 #> 16 X1 0.27773897 #> 17 X2 -0.58397514 #> 18 X3 -0.05917365 #> 19 X4 -0.30164149 #> 20 X5 -1.59268440 #> 21 X1 -1.52596060 #> 22 X2 -0.82329858 #> 23 X3 -0.23094342 #> 24 X4 -0.54733935 #> 25 X5 -0.18194062 #> 26 X1 -0.26916362 #> 27 X2 0.11059280 #> 28 X3 0.69200045 #> 29 X4 -0.38540415 #> 30 X5 1.75614174 #> 31 X1 1.23305388 #> 32 X2 0.36472778 #> 33 X3 1.35682290 #> 34 X4 0.27637203 #> 35 X5 0.11394932 #> 36 X1 0.63360774 #> 37 X2 0.05370100 #> 38 X3 1.78872284 #> 39 X4 0.15186081 #> 40 X5 -0.29216508 #> 41 X1 0.35271746 #> 42 X2 1.36867235 #> 43 X3 0.41071582 #> 44 X4 -0.43138079 #> 45 X5 1.75409316 #> 46 X1 -0.56048248 #> 47 X2 -0.38045724 #> 48 X3 -2.18785470 #> 49 X4 -1.87050006 #> 50 X5 1.80958455 ``` or the other way ``` r long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number data_to_wide(long_data, colnames_from = "Name", values_from = "Value", rows_from = "Row_ID" ) #> Row_ID Value_X1 Value_X2 Value_X3 Value_X4 Value_X5 #> 1 1 -0.08281164 -1.12490028 -0.70632036 -0.7027895 0.07633326 #> 2 2 1.93468099 -0.87430362 0.96687656 0.2998642 -0.23035595 #> 3 3 -2.05128979 0.04386162 -0.71016648 1.1494697 0.31746484 #> 4 4 0.27773897 -0.58397514 -0.05917365 -0.3016415 -1.59268440 #> 5 5 -1.52596060 -0.82329858 -0.23094342 -0.5473394 -0.18194062 #> 6 6 -0.26916362 0.11059280 0.69200045 -0.3854041 1.75614174 #> 7 7 1.23305388 0.36472778 1.35682290 0.2763720 0.11394932 #> 8 8 0.63360774 0.05370100 1.78872284 0.1518608 -0.29216508 #> 9 9 0.35271746 1.36867235 0.41071582 -0.4313808 1.75409316 #> 10 10 -0.56048248 -0.38045724 -2.18785470 -1.8705001 1.80958455 ``` ## Data proprties `datawizard` provides a way to provide comprehensive descriptive summary for all variables in a dataframe: ``` r data(iris) describe_distribution(iris) #> Variable | Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing #> ------------------------------------------------------------------------------------- #> Sepal.Length | 5.8 | 0.83 | 1.30 | 4.3 | 7.9 | 0.31 | -0.55 | 150 | 0 #> Sepal.Width | 3.1 | 0.44 | 0.52 | 2.0 | 4.4 | 0.32 | 0.23 | 150 | 0 #> Petal.Length | 3.8 | 1.77 | 3.52 | 1.0 | 6.9 | -0.27 | -1.40 | 150 | 0 #> Petal.Width | 1.2 | 0.76 | 1.50 | 0.1 | 2.5 | -0.10 | -1.34 | 150 | 0 ``` Or even just a variable ``` r describe_distribution(mtcars$wt) #> Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing #> -------------------------------------------------------------------- #> 3.2 | 0.98 | 1.2 | 1.5 | 5.4 | 0.47 | 0.42 | 32 | 0 ``` There are also some additional data properties that can be computed using this package. ``` r x <- (-10:10)^3 + rnorm(21, 0, 100) smoothness(x, method = "diff") #> [1] 1.821163 #> attr(,"class") #> [1] "parameters_smoothness" "numeric" ``` # Contributing and Support In case you want to file an issue or contribute in another way to the package, please follow [this guide](https://easystats.github.io/datawizard/CONTRIBUTING.html). For questions about the functionality, you may either contact us via email or also file an issue. # Code of Conduct Please note that this project is released with a [Contributor Code of Conduct](https://easystats.github.io/datawizard/CODE_OF_CONDUCT.html). 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Per \emph{easystats} convention, confidence or credible intervals are named \code{CI_low} and \code{CI_high}, and the related \code{ci_type} would be \code{"CI"}. If column names for other intervals differ, \code{ci_type} can be used to indicate the name, e.g. \code{ci_type = "SI"} can be used for support intervals, where the column names in the data frame would be \code{SI_low} and \code{SI_high}.} } \value{ A dataframe with columns corresponding to confidence intervals reshaped either to wide or long format. } \description{ Reshape CI between wide/long formats. } \examples{ x <- data.frame( Parameter = c("Term 1", "Term 2", "Term 1", "Term 2"), CI = c(.8, .8, .9, .9), CI_low = c(.2, .3, .1, .15), CI_high = c(.5, .6, .8, .85), stringsAsFactors = FALSE ) reshape_ci(x) reshape_ci(reshape_ci(x)) } datawizard/man/data_restoretype.Rd0000644000176200001440000000157214113652110017023 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_restoretype.R \name{data_restoretype} \alias{data_restoretype} \title{Restore the type of columns according to a reference data frame} \usage{ data_restoretype(data, reference = NULL, ...) } \arguments{ \item{data}{A data frame to pivot.} \item{reference}{A reference data frame from which to find the correct column types.} \item{...}{Additional arguments passed on to methods.} } \value{ A dataframe with columns whose types have been restored based on the reference dataframe. } \description{ Restore the type of columns according to a reference data frame } \examples{ data <- data.frame( Sepal.Length = c("1", "3", "2"), Species = c("setosa", "versicolor", "setosa"), New = c("1", "3", "4") ) fixed <- data_restoretype(data, reference = iris) summary(fixed) } datawizard/man/describe_distribution.Rd0000644000176200001440000000632514113652110020025 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/describe_distribution.R \name{describe_distribution} \alias{describe_distribution} \alias{describe_distribution.numeric} \alias{describe_distribution.factor} \alias{describe_distribution.data.frame} \title{Describe a distribution} \usage{ describe_distribution(x, ...) \method{describe_distribution}{numeric}( x, centrality = "mean", dispersion = TRUE, iqr = TRUE, range = TRUE, quartiles = FALSE, ci = NULL, iterations = 100, threshold = 0.1, verbose = TRUE, ... ) \method{describe_distribution}{factor}(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...) \method{describe_distribution}{data.frame}( x, centrality = "mean", dispersion = TRUE, iqr = TRUE, range = TRUE, quartiles = FALSE, include_factors = FALSE, ci = NULL, iterations = 100, threshold = 0.1, verbose = TRUE, ... ) } \arguments{ \item{x}{A numeric vector.} \item{...}{Additional arguments to be passed to or from methods.} \item{centrality}{The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: \code{"median"}, \code{"mean"}, \code{"MAP"} or \code{"all"}.} \item{dispersion}{Logical, if \code{TRUE}, computes indices of dispersion related to the estimate(s) (\code{SD} and \code{MAD} for \code{mean} and \code{median}, respectively).} \item{iqr}{Logical, if \code{TRUE}, the interquartile range is calculated (based on \code{\link[stats:IQR]{stats::IQR()}}, using \code{type = 6}).} \item{range}{Return the range (min and max).} \item{quartiles}{Return the first and third quartiles (25th and 75pth percentiles).} \item{ci}{Confidence Interval (CI) level. Default is \code{NULL}, i.e. no confidence intervals are computed. If not \code{NULL}, confidence intervals are based on bootstrap replicates (see \code{iterations}). If \code{centrality = "all"}, the bootstrapped confidence interval refers to the first centrality index (which is typically the median).} \item{iterations}{The number of bootstrap replicates for computing confidence intervals. Only applies when \code{ci} is not \code{NULL}.} \item{threshold}{For \code{centrality = "trimmed"} (i.e. trimmed mean), indicates the fraction (0 to 0.5) of observations to be trimmed from each end of the vector before the mean is computed.} \item{verbose}{Toggle warnings and messages.} \item{include_factors}{Logical, if \code{TRUE}, factors are included in the output, however, only columns for range (first and last factor levels) as well as n and missing will contain information.} } \value{ A data frame with columns that describe the properties of the variables. } \description{ This function describes a distribution by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis). } \note{ There is also a \href{https://easystats.github.io/see/articles/parameters.html}{\code{plot()}-method} implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}. } \examples{ describe_distribution(rnorm(100)) data(iris) describe_distribution(iris) describe_distribution(iris, include_factors = TRUE, quartiles = TRUE) } datawizard/man/data_to_long.Rd0000644000176200001440000000645514113652110016104 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_reshape.R \name{data_to_long} \alias{data_to_long} \alias{data_to_wide} \alias{reshape_longer} \alias{reshape_wider} \title{Reshape (pivot) data from wide to long} \usage{ data_to_long( data, cols = "all", colnames_to = "Name", values_to = "Value", rows_to = NULL, ..., names_to = colnames_to ) data_to_wide( data, values_from = "Value", colnames_from = "Name", rows_from = NULL, sep = "_", ..., names_from = colnames_from ) reshape_longer( data, cols = "all", colnames_to = "Name", values_to = "Value", rows_to = NULL, ..., names_to = colnames_to ) reshape_wider( data, values_from = "Value", colnames_from = "Name", rows_from = NULL, sep = "_", ..., names_from = colnames_from ) } \arguments{ \item{data}{A data frame to pivot.} \item{cols}{A vector of column names or indices to pivot into longer format.} \item{colnames_to}{The name of the new column that will contain the column names.} \item{values_to}{The name of the new column that will contain the values of the pivoted variables.} \item{rows_to}{The name of the column that will contain the row-number from the original data. If \code{NULL}, will be removed.} \item{...}{Additional arguments passed on to methods.} \item{names_to, names_from}{Same as \code{colnames_to}, is there for compatibility with \code{tidyr::pivot_longer()}.} \item{values_from}{The name of the column that contains the values of the put in the columns.} \item{colnames_from}{The name of the column that contains the levels to be used as future columns.} \item{rows_from}{The name of the column that identifies the rows. If \code{NULL}, will use all the unique rows.} \item{sep}{The indicating a separating character in the variable names in the wide format.} } \value{ data.frame } \description{ This function "lengthens" data, increasing the number of rows and decreasing the number of columns. This is a dependency-free base-R equivalent of \code{tidyr::pivot_longer()}. } \examples{ wide_data <- data.frame(replicate(5, rnorm(10))) # From wide to long # ------------------ # Default behaviour (equivalent to tidyr::pivot_longer(wide_data, cols = 1:5)) data_to_long(wide_data) # Customizing the names data_to_long(wide_data, cols = c(1, 2), colnames_to = "Column", values_to = "Numbers", rows_to = "Row" ) # From long to wide # ----------------- long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number data_to_wide(long_data, colnames_from = "Name", values_from = "Value", rows_from = "Row_ID" ) # Full example # ------------------ if (require("psych")) { data <- psych::bfi # Wide format with one row per participant's personality test # Pivot long format long <- data_to_long(data, cols = "\\\\d", # Select all columns that contain a digit colnames_to = "Item", values_to = "Score", rows_to = "Participant" ) # Separate facet and question number long$Facet <- gsub("\\\\d", "", long$Item) long$Item <- gsub("[A-Z]", "", long$Item) long$Item <- paste0("I", long$Item) wide <- data_to_wide(long, colnames_from = "Item", values_from = "Score" ) head(wide) } } datawizard/man/data_transpose.Rd0000644000176200001440000000143214113652110016447 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_transpose.R \name{data_transpose} \alias{data_transpose} \title{Transpose a dataframe} \usage{ data_transpose(data, verbose = TRUE, ...) } \arguments{ \item{data}{A data frame, or an object that can be coerced to a data frame.} \item{verbose}{Silence warnings and/or messages by setting it to \code{FALSE}.} \item{...}{Other arguments passed to or from other functions.} } \description{ Transpose a dataframe. It's the equivalent of using \code{t()} but restores the data.frame class, and prints a warning if the data type is modified (see example). } \examples{ transposed <- data_transpose(iris) transposed[1:5] transposed <- data_transpose(iris[1:4]) # Only numeric = no warning } datawizard/man/data_match.Rd0000644000176200001440000000141214113652110015523 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_match.R \name{data_match} \alias{data_match} \title{Find row indices of a data frame matching a specific condition} \usage{ data_match(x, to, ...) } \arguments{ \item{x}{A data frame.} \item{to}{A data frame matching the specified conditions.} \item{...}{Other arguments passed to or from other functions.} } \value{ A dataframe containing rows that match the specified configuration. } \description{ Find row indices of a data frame that match a specific condition. } \examples{ matching_rows <- data_match(mtcars, data.frame(vs = 0, am = 1)) mtcars[matching_rows, ] matching_rows <- data_match(mtcars, data.frame(vs = 0, am = c(0, 1))) mtcars[matching_rows, ] } datawizard/man/visualisation_recipe.Rd0000644000176200001440000000145314141441413017667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/visualisation_recipe.R \name{visualisation_recipe} \alias{visualisation_recipe} \title{Prepare objects for visualisation} \usage{ visualisation_recipe(x, ...) } \arguments{ \item{x}{An \code{easystats} object.} \item{...}{Other arguments passed to other functions.} } \description{ This function prepares objects for visualisation by returning a list of layers with data and geoms that can be easily plotted using for instance \code{ggplot2}. See the documentation for your object's class: \itemize{ \item{\link[modelbased:visualisation_recipe.estimate_predicted]{modelbased} (\code{estimate_means}, \code{estimate_contrasts}, \code{estimate_slopes}, \code{estimate_predicted}, \code{estimate_grouplevel})} } } datawizard/man/data_relocate.Rd0000644000176200001440000000214514113652110016231 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_relocate.R \name{data_relocate} \alias{data_relocate} \title{Relocate (reorder) columns of a data frame} \usage{ data_relocate(data, cols, before = NULL, after = NULL, safe = TRUE, ...) } \arguments{ \item{data}{A data frame to pivot.} \item{cols}{A character vector indicating the names of the columns to move.} \item{before, after}{Destination of columns. Supplying neither will move columns to the left-hand side; specifying both is an error.} \item{safe}{If \code{TRUE}, will disregard non-existing columns.} \item{...}{Other arguments passed to or from other functions.} } \value{ A data frame with reordered columns. } \description{ Relocate (reorder) columns of a data frame } \examples{ # Reorder columns head(data_relocate(iris, cols = "Species", before = "Sepal.Length")) head(data_relocate(iris, cols = "Species", before = "Sepal.Width")) head(data_relocate(iris, cols = "Sepal.Width", after = "Species")) head(data_relocate(iris, cols = c("Species", "Petal.Length"), after = "Sepal.Width")) } datawizard/man/normalize.Rd0000644000176200001440000000462014113652110015442 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/normalize.R \name{normalize} \alias{normalize} \alias{normalize.numeric} \alias{normalize.grouped_df} \alias{normalize.data.frame} \title{Normalize numeric variable to 0-1 range} \usage{ normalize(x, ...) \method{normalize}{numeric}(x, include_bounds = TRUE, verbose = TRUE, ...) \method{normalize}{grouped_df}( x, select = NULL, exclude = NULL, include_bounds = TRUE, verbose = TRUE, ... ) \method{normalize}{data.frame}( x, select = NULL, exclude = NULL, include_bounds = TRUE, verbose = TRUE, ... ) } \arguments{ \item{x}{A numeric vector, data frame, or matrix. See details.} \item{...}{Arguments passed to or from other methods.} \item{include_bounds}{Logical, if \code{TRUE}, return value may include 0 and 1. If \code{FALSE}, the return value is compressed, using Smithson and Verkuilen's (2006) formula \code{(x * (n - 1) + 0.5) / n}, to avoid zeros and ones in the normalized variables. This can be useful in case of beta-regression, where the response variable is not allowed to include zeros and ones.} \item{verbose}{Toggle warnings and messages on or off.} \item{select}{Character vector of column names. If \code{NULL} (the default), all variables will be selected.} \item{exclude}{Character vector of column names to be excluded from selection.} } \value{ A normalized object. } \description{ Performs a normalization of data, i.e., it scales variables in the range 0 - \enumerate{ \item This is a special case of \code{\link[=data_rescale]{data_rescale()}}. } } \details{ \itemize{ \item If \code{x} is a matrix, normalization is performed across all values (not column- or row-wise). For column-wise normalization, convert the matrix to a data.frame. \item If \code{x} is a grouped data frame (\code{grouped_df}), normalization is performed separately for each group. } } \examples{ normalize(c(0, 1, 5, -5, -2)) normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE) head(normalize(trees)) } \references{ Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(1), 54–71. } \seealso{ Other transform utilities: \code{\link{data_rescale}()}, \code{\link{ranktransform}()}, \code{\link{standardize}()} } \concept{transform utilities} datawizard/man/data_extract.Rd0000644000176200001440000000302114141441413016102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_extract.R \name{data_extract} \alias{data_extract} \alias{extract} \title{Extract a single column or element from an object} \usage{ data_extract(data, select, name = NULL, ...) extract(data, select, name = NULL, ...) } \arguments{ \item{data}{The object to subset. Methods are currently available for data frames and data frame extensions (e.g., tibbles).} \item{select}{A variable specified as: \itemize{ \item a literal variable name (e.g., \code{column_name}) \item a character vector with the variable name (e.g., \code{"column_name"}) \item a positive integer, giving the position counting from the left \item a negative integer, giving the position counting from the right. } The default returns the last column. If the special value \code{0} or \code{"row.names"} is given, the row names of the object (if any) are extracted.} \item{name}{An optional argument that specifies the column to be used as names for for the vector after extraction. Specified in the same way as \code{select}.} \item{...}{For use by future methods.} } \value{ A vector containing the extracted element. } \description{ \code{extract()} is similar to \code{$}. It extracts a single column or element from an object (e.g., a data frame, list, ) } \examples{ extract(mtcars, cyl, name = gear) extract(mtcars, "cyl", name = gear) extract(mtcars, -1, name = gear) extract(mtcars, cyl, name = 0) extract(mtcars, cyl, name = "row.names") } datawizard/man/ranktransform.Rd0000644000176200001440000000361514113652110016334 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ranktransform.R \name{ranktransform} \alias{ranktransform} \alias{ranktransform.numeric} \alias{ranktransform.grouped_df} \alias{ranktransform.data.frame} \title{(Signed) rank transformation} \usage{ ranktransform(x, ...) \method{ranktransform}{numeric}(x, sign = FALSE, method = "average", verbose = TRUE, ...) \method{ranktransform}{grouped_df}( x, select = NULL, exclude = NULL, sign = FALSE, method = "average", ... ) \method{ranktransform}{data.frame}( x, select = NULL, exclude = NULL, sign = FALSE, method = "average", ... ) } \arguments{ \item{x}{Object.} \item{...}{Arguments passed to or from other methods.} \item{sign}{Logical, if \code{TRUE}, return signed ranks.} \item{method}{Treatment of ties. Can be one of \code{"average"} (default), \code{"first"}, \code{"last"}, \code{"random"}, \code{"max"} or \code{"min"}. See \code{\link[=rank]{rank()}} for details.} \item{verbose}{Toggle warnings and messages on or off.} \item{select}{Character vector of column names. If \code{NULL} (the default), all variables will be selected.} \item{exclude}{Character vector of column names to be excluded from selection.} } \value{ A rank-transformed object. } \description{ Transform numeric values with the integers of their rank (i.e., 1st smallest, 2nd smallest, 3rd smallest, etc.). Setting the \code{sign} argument to \code{TRUE} will give you signed ranks, where the ranking is done according to absolute size but where the sign is preserved (i.e., 2, 1, -3, 4). } \examples{ ranktransform(c(0, 1, 5, -5, -2)) ranktransform(c(0, 1, 5, -5, -2), sign = TRUE) head(ranktransform(trees)) } \seealso{ Other transform utilities: \code{\link{data_rescale}()}, \code{\link{normalize}()}, \code{\link{standardize}()} } \concept{transform utilities} datawizard/man/center.Rd0000644000176200001440000001060714141513700014726 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/center.R \name{center} \alias{center} \alias{centre} \alias{center.numeric} \alias{center.data.frame} \title{Centering (Grand-Mean Centering)} \usage{ center(x, ...) centre(x, ...) \method{center}{numeric}( x, robust = FALSE, weights = NULL, verbose = TRUE, reference = NULL, center = NULL, ... ) \method{center}{data.frame}( x, robust = FALSE, weights = NULL, verbose = TRUE, reference = NULL, select = NULL, exclude = NULL, remove_na = c("none", "selected", "all"), force = FALSE, append = FALSE, center = NULL, ... ) } \arguments{ \item{x}{A data frame, a (numeric or character) vector or a factor.} \item{...}{Currently not used.} \item{robust}{Logical, if \code{TRUE}, centering is done by subtracting the median from the variables. If \code{FALSE}, variables are centered by subtracting the mean.} \item{weights}{Can be \code{NULL} (for no weighting), or: \itemize{ \item For data frames: a numeric vector of weights, or a character of the name of a column in the \code{data.frame} that contains the weights. \item For numeric vectors: a numeric vector of weights. }} \item{verbose}{Toggle warnings and messages.} \item{reference}{A data frame or variable from which the centrality and deviation will be computed instead of from the input variable. Useful for standardizing a subset or new data according to another data frame.} \item{center}{Numeric value, which can be used as alternative to \code{reference} to define a reference centrality. If \code{center} is of length 1, it will be recycled to match the length of selected variables for centering. Else, \code{center} must be of same length as the number of selected variables. Values in \code{center} will be matched to selected variables in the provided order, unless a named vector is given. In this case, names are matched against the names of the selected variables.} \item{select}{Character vector of column names. If \code{NULL} (the default), all variables will be selected.} \item{exclude}{Character vector of column names to be excluded from selection.} \item{remove_na}{How should missing values (\code{NA}) be treated: if \code{"none"} (default): each column's standardization is done separately, ignoring \code{NA}s. Else, rows with \code{NA} in the columns selected with \code{select} / \code{exclude} (\code{"selected"}) or in all columns (\code{"all"}) are dropped before standardization, and the resulting data frame does not include these cases.} \item{force}{Logical, if \code{TRUE}, forces centering of factors as well. Factors are converted to numerical values, with the lowest level being the value \code{1} (unless the factor has numeric levels, which are converted to the corresponding numeric value).} \item{append}{Logical or string. If \code{TRUE}, centered variables get new column names (with the suffix \code{"_c"}) and are appended (column bind) to \code{x}, thus returning both the original and the centered variables. If \code{FALSE}, original variables in \code{x} will be overwritten by their centered versions. If a character value, centered variables are appended with new column names (using the defined suffix) to the original data frame.} } \value{ The centered variables. } \description{ Performs a grand-mean centering of data. } \note{ \strong{Difference between centering and standardizing}: Standardized variables are computed by subtracting the mean of the variable and then dividing it by the standard deviation, while centering variables involves only the subtraction. } \examples{ data(iris) # entire dataframe or a vector head(iris$Sepal.Width) head(center(iris$Sepal.Width)) head(center(iris)) head(center(iris, force = TRUE)) # only the selected columns from a dataframe center(anscombe, select = c("x1", "x3")) center(anscombe, exclude = c("x1", "x3")) # centering with reference center and scale d <- data.frame( a = c(-2, -1, 0, 1, 2), b = c(3, 4, 5, 6, 7) ) # default centering at mean center(d) # centering, using 0 as mean center(d, center = 0) # centering, using -5 as mean center(d, center = -5) } \seealso{ If centering within-clusters (instead of grand-mean centering) is required, see \code{\link[=demean]{demean()}}. For standardizing, see \code{\link[=standardize]{standardize()}}. } datawizard/DESCRIPTION0000644000176200001440000000426014174271374014126 0ustar liggesusersType: Package Package: datawizard Title: Easy Data Wrangling Version: 0.2.3 Authors@R: c( person("Dominique", "Makowski", , "dom.makowski@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-5375-9967", Twitter = "@Dom_Makowski")), person("Daniel", "Lüdecke", , "d.luedecke@uke.de", role = "aut", comment = c(ORCID = "0000-0002-8895-3206", Twitter = "@strengejacke")), person("Indrajeet", "Patil", , "patilindrajeet.science@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1995-6531", Twitter = "@patilindrajeets")), person("Mattan S.", "Ben-Shachar", , "matanshm@post.bgu.ac.il", role = "aut", comment = c(ORCID = "0000-0002-4287-4801")), person("Brenton M.", "Wiernik", , "brenton@wiernik.org", role = "aut", comment = c(ORCID = "0000-0001-9560-6336", Twitter = "@bmwiernik")) ) Maintainer: Indrajeet Patil Description: A lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It also forms the data wrangling backend for the packages in the 'easystats' ecosystem. License: GPL-3 URL: https://easystats.github.io/datawizard/ BugReports: https://github.com/easystats/datawizard/issues Depends: R (>= 3.4) Imports: insight (>= 0.14.4), stats, utils Suggests: bayestestR, boot, dplyr, effectsize, gamm4, ggplot2, knitr, lfe, lme4, MASS, modelbased, parameters, performance, poorman, psych, rmarkdown, rstanarm, see, spelling, testthat (>= 3.0.0), tidyr VignetteBuilder: knitr Config/testthat/edition: 3 Encoding: UTF-8 Language: en-US RoxygenNote: 7.1.2 NeedsCompilation: no Packaged: 2022-01-26 15:24:20 UTC; IndrajeetPatil Author: Dominique Makowski [aut] (, @Dom_Makowski), Daniel Lüdecke [aut] (, @strengejacke), Indrajeet Patil [aut, cre] (, @patilindrajeets), Mattan S. Ben-Shachar [aut] (), Brenton M. Wiernik [aut] (, @bmwiernik) Repository: CRAN Date/Publication: 2022-01-26 16:12:44 UTC datawizard/build/0000755000176200001440000000000014174263642013514 5ustar liggesusersdatawizard/build/vignette.rds0000644000176200001440000000040114174263642016046 0ustar liggesusersu=O1 sZh30? 6d5D9(IU_wMN\+8m"E"/8-Fn>QX#VQ) t콦|a4+:B#+ym._yב3&Jg]sJ;ggu9NgG/[-/j9bӁWXUf/$;^4vv7Oݴd?9 D>ñ/?|datawizard/tests/0000755000176200001440000000000014113652110013540 5ustar liggesusersdatawizard/tests/spelling.R0000644000176200001440000000023314164750351015512 0ustar liggesusersif (requireNamespace("spelling", quietly = TRUE)) { spelling::spell_check_test( vignettes = TRUE, error = FALSE, skip_on_cran = TRUE ) } datawizard/tests/testthat/0000755000176200001440000000000014174271374015420 5ustar liggesusersdatawizard/tests/testthat/test-winsorization.R0000644000176200001440000000051014174263210021421 0ustar liggesusers test_that("testing Winsorization of factors", { expect_equal(winsorize(as.factor(mtcars$am)), as.factor(mtcars$am)) }) test_that("with missing values", { expect_snapshot(suppressWarnings(head(winsorize(na.omit(ggplot2::msleep$brainwt))))) expect_equal(length(winsorize(as.factor(ggplot2::msleep$vore))), 83) }) datawizard/tests/testthat/test-compact-list.R0000644000176200001440000000101114171777711021113 0ustar liggesuserstest_that("compact_list works as expected", { expect_equal(compact_list(list(NULL, 1, c(NA, NA))), list(1, c(NA, NA))) expect_equal(compact_list(c(1, NA, NA)), c(1, NA, NA)) expect_equal(compact_list(list(NULL, 1, list(NULL, NULL))), list(1)) expect_equal(compact_list(c(1, NA, NA), remove_na = TRUE), 1) expect_equal(compact_list(c(1, 2, 3), remove_na = TRUE), c(1, 2, 3)) expect_equal(compact_list(""), "") expect_null(compact_list(NULL)) expect_equal(compact_list(logical(0)), logical(0)) }) datawizard/tests/testthat/test-smoothness.R0000644000176200001440000000035314113652110020703 0ustar liggesuserstest_that("smoothness", { set.seed(123) x <- (-10:10)^3 + rnorm(21, 0, 100) expect_equal(smoothness(x)[[1]], 0.9030014, tolerance = 0.001) expect_equal(smoothness(x, method = "auto")[[1]], 1.750452, tolerance = 0.001) }) datawizard/tests/testthat/test-std_center.R0000644000176200001440000000400614164744652020655 0ustar liggesusersif (require("testthat")) { d <- iris[1:4, ] # standardize ----------------------------------------------------- test_that("standardize.data.frame", { x <- standardise(d, select = c("Sepal.Length", "Sepal.Width")) expect_equal(as.vector(x$Sepal.Length), as.vector(scale(d$Sepal.Length)), tolerance = 0.001) expect_equal(as.vector(x$Petal.Length), as.vector(d$Petal.Length), tolerance = 0.001) expect_equal(colnames(x), c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")) x <- standardise(d, select = c("Sepal.Length", "Sepal.Width"), append = TRUE) expect_equal(as.vector(x$Sepal.Length_z), as.vector(scale(d$Sepal.Length)), tolerance = 0.001) expect_equal(as.vector(x$Sepal.Length), as.vector(d$Sepal.Length), tolerance = 0.001) expect_equal(as.vector(x$Petal.Length), as.vector(d$Petal.Length), tolerance = 0.001) expect_equal(colnames(x), c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species", "Sepal.Length_z", "Sepal.Width_z")) }) # standardize ----------------------------------------------------- test_that("center.data.frame", { x <- center(d, select = c("Sepal.Length", "Sepal.Width")) expect_equal(as.vector(x$Sepal.Length), as.vector(d$Sepal.Length - mean(d$Sepal.Length)), tolerance = 0.001) expect_equal(as.vector(x$Petal.Length), as.vector(d$Petal.Length), tolerance = 0.001) expect_equal(colnames(x), c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species")) x <- center(d, select = c("Sepal.Length", "Sepal.Width"), append = TRUE) expect_equal(as.vector(x$Sepal.Length_c), as.vector(d$Sepal.Length - mean(d$Sepal.Length)), tolerance = 0.001) expect_equal(as.vector(x$Sepal.Length), as.vector(d$Sepal.Length), tolerance = 0.001) expect_equal(as.vector(x$Petal.Length), as.vector(d$Petal.Length), tolerance = 0.001) expect_equal(colnames(x), c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species", "Sepal.Length_c", "Sepal.Width_c")) }) } datawizard/tests/testthat/test-distributions.R0000644000176200001440000000072214113652110021403 0ustar liggesusersif (require("testthat") && require("bayestestR") && require("parameters")) { test_that("distributions", { x <- bayestestR::distribution_normal(100) expect_equal(kurtosis(x)$Kurtosis, -0.3204763, tolerance = 0.01) expect_equal(skewness(x)$Skewness, -5.050428e-16, tolerance = 0.01) expect_equal(as.numeric(smoothness(x, "diff")), 0.919, tolerance = 0.01) expect_equal(as.numeric(smoothness(x, "cor")), 0.998, tolerance = 0.01) }) } datawizard/tests/testthat/test-skewness.R0000644000176200001440000000220014113652110020334 0ustar liggesusersif (require("testthat")) { test_that("skewness", { data(iris) expect_equal(skewness(iris$Sepal.Length)[[1]], 0.314911, tolerance = 1e-3) expect_equal(skewness(iris$Sepal.Length, type = 1)[[1]], 0.3117531, tolerance = 1e-3) expect_equal(skewness(iris$Sepal.Length, type = 3)[[1]], 0.3086407, tolerance = 1e-3) }) test_that("kurtosis", { data(iris) expect_equal(kurtosis(iris$Sepal.Length)[[1]], -0.552064, tolerance = 1e-3) expect_equal(kurtosis(iris$Sepal.Length, type = 1)[[1]], -0.5735679, tolerance = 1e-3) expect_equal(kurtosis(iris$Sepal.Length, type = 3)[[1]], -0.6058125, tolerance = 1e-3) }) test_that("skewness", { data(iris) expect_equal( skewness(iris[, 1:4])[[2]], c( 0.314910956636973, 0.318965664713603, -0.274884179751012, -0.10296674764898 ), tolerance = 1e-3 ) }) test_that("kurtosis", { data(iris) expect_equal( kurtosis(iris[, 1:4])[[2]], c( -0.552064041315639, 0.228249042468194, -1.40210341552175, -1.34060399661265 ), tolerance = 1e-3 ) }) } datawizard/tests/testthat/test-is-empty-object.R0000644000176200001440000000074214172027360021526 0ustar liggesuserstest_that("is_empty_object works as expected", { # not empty expect_false(is_empty_object(list(NULL, 1, c(NA, NA)))) expect_false(is_empty_object(c(1, NA, NA))) expect_false(is_empty_object("")) # empty expect_true(is_empty_object(logical(0))) expect_true(is_empty_object(NULL)) expect_true(is_empty_object(list(NULL, list(NULL, NULL)))) expect_true(is_empty_object(NA)) expect_true(is_empty_object(c())) expect_true(is_empty_object(list())) }) datawizard/tests/testthat/test-ranktransform.R0000644000176200001440000000066214113652110021373 0ustar liggesusersif (require("testthat")) { test_that("signed rank", { x <- c(-1, 2, -3, 4) sr <- ranktransform(x, sign = TRUE) r <- ranktransform(x, sign = FALSE) expect_equal(sr, x) # unchanged expect_equal(r, c(2, 3, 1, 4)) x <- c(1, -2, -2, 4, 0, 3, -14, 0) expect_warning(ranktransform(x, sign = TRUE)) expect_true(all(is.na(suppressWarnings(ranktransform(x, sign = TRUE)[c(5, 8)])))) }) } datawizard/tests/testthat/test-compact-character.R0000644000176200001440000000051414174253064022074 0ustar liggesuserstest_that("compact character works as expected", { expect_equal(compact_character(c("x", "y", NA)), c("x", "y")) expect_equal(compact_character(c("x", "y", NA_real_)), c("x", "y")) expect_equal(compact_character(c("x", "NULL", "", "y")), c("x", "y")) # scalar expect_equal(compact_character(""), character(0)) }) datawizard/tests/testthat/test-standardize-data.R0000644000176200001440000002016314151374205021731 0ustar liggesusersif (require("testthat") && require("poorman")) { # standardize.numeric ----------------------------------------------------- test_that("standardize.numeric", { x <- standardize(seq(0, 1, length.out = 100)) expect_equal(mean(x), 0, tolerance = 0.01) x <- standardize(seq(0, 1, length.out = 100), two_sd = TRUE) expect_equal(sd(x), 0.5, tolerance = 0.01) x <- standardize(seq(0, 1, length.out = 100), robust = TRUE) expect_equal(median(x), 0, tolerance = 0.01) x <- standardize(seq(0, 1, length.out = 100), robust = TRUE, two_sd = TRUE) expect_equal(mad(x), 0.5, tolerance = 0.01) expect_message(standardize(c(0, 0, 0, 1, 1))) x <- standardize(c(-1, 0, 1), reference = seq(3, 4, length.out = 100)) expect_equal(mean(x), -11.943, tolerance = 0.01) }) # standardize factor / Date ----------------------------------------------- test_that("standardize.numeric", { f <- factor(c("c", "a", "b")) expect_equal(standardize(f), f) expect_equal(standardize(f, force = TRUE), c(1, -1, 0), ignore_attr = TRUE) d <- as.Date(c("1989/08/06", "1989/08/04", "1989/08/05")) expect_equal(standardize(d), d) expect_equal(standardize(d, force = TRUE), c(1, -1, 0), ignore_attr = TRUE) }) # standardize.data.frame -------------------------------------------------- test_that("standardize.data.frame", { data(iris) x <- standardize(iris) expect_equal(mean(x$Sepal.Length), 0, tolerance = 0.01) expect_length(levels(x$Species), 3) expect_equal(mean(subset(x, Species == "virginica")$Sepal.Length), 0.90, tolerance = 0.01) x2 <- standardize(x = iris[1, ], reference = iris) expect_true(all(x2[1, ] == x[1, ])) skip_if_not_installed("poorman") x <- standardize(group_by(iris, Species)) expect_equal(mean(x$Sepal.Length), 0, tolerance = 0.01) expect_length(levels(x$Species), 3) expect_equal(mean(subset(x, Species == "virginica")$Sepal.Length), 0, tolerance = 0.01) }) test_that("standardize.data.frame, NAs", { data(iris) iris$Sepal.Width[c(148, 65, 33, 58, 54, 93, 114, 72, 32, 23)] <- NA iris$Sepal.Length[c(11, 30, 141, 146, 13, 149, 6, 8, 48, 101)] <- NA x <- standardize(iris) expect_equal(head(x$Sepal.Length), c(-0.9163, -1.1588, -1.4013, -1.5226, -1.0376, NA), tolerance = 0.01) expect_equal(head(x$Sepal.Width), c(1.0237, -0.151, 0.3189, 0.0839, 1.2586, 1.9635), tolerance = 0.01) expect_equal(mean(x$Sepal.Length), as.numeric(NA)) x <- standardize(iris, two_sd = TRUE) expect_equal(head(x$Sepal.Length), c(-0.4603, -0.5811, -0.7019, -0.7623, -0.5207, NA), tolerance = 0.01) expect_equal(head(x$Sepal.Width), c(0.5118, -0.0755, 0.1594, 0.042, 0.6293, 0.9817), tolerance = 0.01) expect_equal(mean(x$Sepal.Length), as.numeric(NA)) skip_if_not_installed("poorman") x <- standardize(group_by(iris, .data$Species)) expect_equal(head(x$Sepal.Length), c(0.2547, -0.3057, -0.8661, -1.1463, -0.0255, NA), tolerance = 0.01) expect_equal(head(x$Sepal.Width), c(0.2369, -1.0887, -0.5584, -0.8235, 0.502, 1.2974), tolerance = 0.01) expect_equal(mean(x$Sepal.Length), as.numeric(NA)) }) test_that("standardize.data.frame, apend", { data(iris) iris$Sepal.Width[c(26, 43, 56, 11, 66, 132, 23, 133, 131, 28)] <- NA iris$Sepal.Length[c(32, 12, 109, 92, 119, 49, 83, 113, 64, 30)] <- NA x <- standardize(iris, append = TRUE) expect_equal(colnames(x), c( "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species", "Sepal.Length_z", "Sepal.Width_z", "Petal.Length_z", "Petal.Width_z" )) expect_equal(head(x$Sepal.Length_z), c(-0.8953, -1.1385, -1.3816, -1.5032, -1.0169, -0.5306), tolerance = 0.01) expect_equal(head(x$Sepal.Width_z), c(1.04, -0.1029, 0.3543, 0.1257, 1.2685, 1.9542), tolerance = 0.01) expect_equal(mean(x$Sepal.Length_z), as.numeric(NA)) x <- standardize(iris, two_sd = TRUE, append = TRUE) expect_equal(head(x$Sepal.Length_z), c(-0.4477, -0.5692, -0.6908, -0.7516, -0.5084, -0.2653), tolerance = 0.01) expect_equal(head(x$Sepal.Width_z), c(0.52, -0.0514, 0.1771, 0.0629, 0.6343, 0.9771), tolerance = 0.01) expect_equal(mean(x$Sepal.Length_z), as.numeric(NA)) skip_if_not_installed("poorman") x <- standardize(group_by(iris, .data$Species), append = TRUE) expect_equal(head(x$Sepal.Length_z), c(0.2746, -0.2868, -0.8483, -1.129, -0.0061, 1.1168), tolerance = 0.01) expect_equal(head(x$Sepal.Width_z), c(0.1766, -1.1051, -0.5924, -0.8487, 0.4329, 1.2019), tolerance = 0.01) expect_equal(mean(x$Sepal.Length_z), as.numeric(NA)) }) test_that("standardize.data.frame, weights", { x <- rexp(30) w <- rpois(30, 20) + 1 expect_equal( sqrt(cov.wt(cbind(x, x), w)$cov[1, 1]), attr(standardize(x, weights = w), "scale") ) expect_equal( standardize(x, weights = w), standardize(data.frame(x), weights = w)$x ) # name and vector give same results expect_equal( standardize(mtcars, exclude = "cyl", weights = mtcars$cyl), standardize(mtcars, weights = "cyl") ) skip_if_not_installed("poorman") d <- group_by(mtcars, am) expect_warning(standardize(d, weights = d$cyl)) }) # Unstandardize ----------------------------------------------------------- test_that("unstandardize, numeric", { data(iris) x <- standardize(iris$Petal.Length) rez <- unstandardize(x) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) rez <- unstandardize(x, reference = iris$Petal.Length) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) rez <- unstandardize(x, center = mean(iris$Petal.Length), scale = stats::sd(iris$Petal.Length)) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) rez <- unstandardize(0, center = mean(iris$Petal.Length), scale = stats::sd(iris$Petal.Length)) expect_equal(rez, mean(iris$Petal.Length), tolerance = 1e-3) x <- standardize(iris$Petal.Length, robust = TRUE, two_sd = TRUE) rez <- unstandardize(x, robust = TRUE, two_sd = TRUE) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) x <- scale(iris$Petal.Length) rez <- unstandardize(x) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) x <- scale(iris$Petal.Length, center = 3, scale = 2) rez <- unstandardize(x) expect_equal(rez, iris$Petal.Length, tolerance = 1e-3, ignore_attr = TRUE) }) test_that("unstandardize, dataframe", { data(iris) x <- standardize(iris) rez <- unstandardize(x) expect_equal(rez, iris, tolerance = 0.1, ignore_attr = TRUE) x <- standardize(iris, select = "Petal.Length") rez <- unstandardize(x) expect_equal(rez, iris, tolerance = 0.1, ignore_attr = TRUE) x <- standardize(iris, select = "Petal.Length") rez <- unstandardize(x, center = c(Petal.Length = mean(iris$Petal.Length)), scale = c(Petal.Length = stats::sd(iris$Petal.Length)) ) expect_equal(rez, iris, tolerance = 0.1, ignore_attr = TRUE) expect_error(unstandardize(x, center = mean(iris$Petal.Length), scale = stats::sd(iris$Petal.Length) )) x <- standardize(iris) rez <- unstandardize(x, center = rep(0, 4), scale = rep(1, 4)) expect_equal(rez, x, tolerance = 0.1, ignore_attr = TRUE) data(iris) x <- standardize(iris, robust = TRUE, two_sd = TRUE) rez <- unstandardize(x, robust = TRUE, two_sd = TRUE) expect_equal(rez, iris, tolerance = 0.1, ignore_attr = TRUE) skip_if_not_installed("poorman") d <- group_by(mtcars, am) x <- standardize(d) expect_error(unstandardize(x)) }) test_that("un/standardize, matrix", { set.seed(4) x <- matrix(sample(8), nrow = 4) colnames(x) <- letters[1:2] rownames(x) <- LETTERS[1:4] x expect_equal(z1 <- standardize(x), z2 <- scale(x), ignore_attr = TRUE) expect_equal(unstandardize(z1), x) expect_equal(unstandardize(z2), unstandardize(z1)) }) } datawizard/tests/testthat/_snaps/0000755000176200001440000000000014174263210016671 5ustar liggesusersdatawizard/tests/testthat/_snaps/winsorization.md0000644000176200001440000000026114174263210022131 0ustar liggesusers# with missing values Code suppressWarnings(head(winsorize(na.omit(ggplot2::msleep$brainwt)))) Output [1] 0.0155 0.0024 0.1750 0.0700 0.0982 0.1150 datawizard/tests/testthat/test-adjust.R0000644000176200001440000000116214113652110017772 0ustar liggesusersif (require("testthat")) { data(iris) test_that("adjust multilevel", { skip_if_not_installed("lme4") adj <- adjust(iris[c("Sepal.Length", "Species")], multilevel = TRUE, bayesian = FALSE) expect_equal( head(adj$Sepal.Length), c(0.08698, -0.11302, -0.31302, -0.41302, -0.01302, 0.38698), tolerance = 1e-3 ) }) test_that("adjust", { adj <- adjust(iris[c("Sepal.Length", "Species")], multilevel = FALSE, bayesian = FALSE) expect_equal( head(adj$Sepal.Length), c(0.094, -0.106, -0.306, -0.406, -0.006, 0.394), tolerance = 1e-3 ) }) } datawizard/tests/testthat/test-describe_distribution.R0000644000176200001440000000025114113652110023055 0ustar liggesusersif (require("testthat")) { test_that("describe_distribution", { x <- describe_distribution(rnorm(100)) expect_equal(c(nrow(x), ncol(x)), c(1, 9)) }) } datawizard/tests/testthat/test-std_center_scale_args.R0000644000176200001440000000411314164744654023041 0ustar liggesusersif (require("testthat")) { d <- data.frame(a = 1:5, b = 21:25, c = 41:45) test_that("standardize", { x <- standardize(d) expect_equal(as.vector(x$a), as.vector(scale(d$a)), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = 5, scale = 2) expect_equal(as.vector(x$a), c(-2, -1.5, -1, -0.5, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(5, 25, 45), scale = c(3, 3, 3)) expect_equal(as.vector(x$a), c(-1.33333, -1, -0.66667, -0.33333, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-1.33333, -1, -0.66667, -0.33333, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(c = 45, a = 5, b = 25), scale = c(3, 3, 3)) expect_equal(as.vector(x$a), c(-1.33333, -1, -0.66667, -0.33333, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-1.33333, -1, -0.66667, -0.33333, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(c = 45, a = 5, b = 25), scale = c(1, 2, 3)) expect_equal(as.vector(x$a), c(-4, -3, -2, -1, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-2, -1.5, -1, -0.5, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(c = 45, a = 5, b = 25), scale = c(a = 1, b = 2, c = 3)) expect_equal(as.vector(x$a), c(-4, -3, -2, -1, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-2, -1.5, -1, -0.5, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(c = 45, a = 5, b = 25), scale = c(c = 3, b = 2, a = 1)) expect_equal(as.vector(x$a), c(-4, -3, -2, -1, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-2, -1.5, -1, -0.5, 0), tolerance = 0.001) }) test_that("standardize", { x <- standardize(d, center = c(c = 45, a = 5, b = 25), scale = c(c = 1, b = 2, a = 3)) expect_equal(as.vector(x$a), c(-1.33333, -1, -0.66667, -0.33333, 0), tolerance = 0.001) expect_equal(as.vector(x$b), c(-2, -1.5, -1, -0.5, 0), tolerance = 0.001) }) } datawizard/tests/testthat/test-extract.R0000644000176200001440000000071714141441413020162 0ustar liggesuserstest_that("extract from data frame", { expect_equal( extract(mtcars, cyl, name = gear), setNames(mtcars$cyl, mtcars$gear) ) expect_equal( extract(mtcars, -1, name = gear), setNames(mtcars[[11]], mtcars$gear) ) expect_equal( extract(mtcars, cyl, name = 0), setNames(mtcars$cyl, rownames(mtcars)) ) expect_equal( extract(mtcars, cyl, name = "row.names"), setNames(mtcars$cyl, rownames(mtcars)) ) }) datawizard/tests/testthat/test-center.R0000644000176200001440000000210614141167105017765 0ustar liggesusersif (require("testthat")) { data(iris) data(mtcars) z <- center(iris$Sepal.Width) test_that("center", { expect_equal(as.vector(z), iris$Sepal.Width - mean(iris$Sepal.Width), tolerance = 1e-4, ignore_attr = TRUE) }) z <- center(mtcars$hp, robust = TRUE) test_that("center, robust", { expect_equal(as.vector(z), mtcars$hp - median(mtcars$hp), tolerance = 1e-4, ignore_attr = TRUE) }) z <- center(iris, select = "Sepal.Width") test_that("center, select", { expect_equal(as.vector(z$Sepal.Width), iris$Sepal.Width - mean(iris$Sepal.Width), tolerance = 1e-4, ignore_attr = TRUE) }) z <- center(iris, select = "Species") test_that("center, factors", { expect_equal(z$Species, iris$Species) }) z <- center(iris, select = "Species", force = TRUE) v <- as.numeric(iris$Species) test_that("center, force factors", { expect_equal(as.vector(z$Species), v - median(v), tolerance = 1e-4, ignore_attr = TRUE) }) test_that("center, all na", { z <- center(c(NA, NA, NA)) expect_equal(z, c(NA, NA, NA)) }) } datawizard/tests/testthat.R0000644000176200001440000000010414113652110015516 0ustar liggesuserslibrary(testthat) library(datawizard) test_check("datawizard") datawizard/vignettes/0000755000176200001440000000000014174263644014427 5ustar liggesusersdatawizard/vignettes/demean.Rmd0000644000176200001440000004651614141234536016330 0ustar liggesusers--- title: "Analysing Longitudinal or Panel Data" output: rmarkdown::html_vignette: toc: true fig_width: 10.08 fig_height: 6 vignette: > %\VignetteIndexEntry{Analysing Longitudinal or Panel Data} \usepackage[utf8]{inputenc} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console bibliography: bibliography.bib --- ```{r , include=FALSE} library(knitr) knitr::opts_chunk$set( echo = TRUE, collapse = TRUE, warning = FALSE, message = FALSE, comment = "#>", out.width = "100%" ) pkgs <- c( "datawizard", "poorman", "tidyr", "see", "ggplot2", "parameters", "performance", "lme4", "lfe" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("parameters") >= "0.14.1") { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("performance") >= "0.7.4") { knitr::opts_chunk$set(eval = FALSE) } set.seed(333) ``` This vignette explains the rational behind the `demean()` function. We give recommendations how to analyze multilevel or hierarchical data structures, when macro-indicators (or level-2 predictors, or higher-level units, or more general: _group-level predictors_) are used as covariates and the model suffers from **heterogeneity bias** [@bell_explaining_2015]. # Sample data used in this vignette ```{r} library(parameters) data("qol_cancer") ``` * Variables: * `QoL` : Response (quality of life of patient) * `phq4` : Patient Health Questionnaire, **time-varying** variable * `hospital` : Location of treatment, **time-invariant** variable, co-variate * `education`: Educational level, **time-invariant** variable, co-variate * `ID` : patient ID * `time` : time-point of measurement # Heterogeneity bias Heterogeneity bias occurs when group-level predictors vary within and across groups, and hence fixed effects may correlate with group (or random) effects. This is a typical situation when analyzing longitudinal or panel data: Due to the repeated measurements of persons, the "person" (or subject-ID) is now a level-2 variable. Predictors at level-1 ("fixed effects"), e.g. self-rated health or income, now have an effect at level-1 ("within"-effect) and at higher-level units (level-2, the subject-level, which is the "between"-effect) (see also [this posting](https://shouldbewriting.netlify.app/posts/2019-10-21-accounting-for-within-and-between-subject-effect/)). This inevitably leads to correlating fixed effects and error terms - which, in turn, results in biased estimates, because both the within- *and* between-effect are captured in *one* estimate. You can check if your model may suffer from heterogeneity bias using the `check_heterogeneity_bias()` function: ```{r} library(performance) check_heterogeneity_bias(qol_cancer, select = c("phq4", "education"), group = "ID") ``` # Adressing heterogeneity bias: the Fixed Effects Regression (FE) approach Fixed effects regression models (FE) are a popular approach for panel data analysis in particular in econometrics and considered as gold standard. To avoid the problem of heterogeneity bias, in FE all higher-level variance (and thus, any between-effects), "are controlled out using the higher-level entities themselves, included in the model as dummy variables" [@bell_explaining_2015]. As a consequence, FE models are only able estimate _within-effects_. To remove between-effects and only model within-effects, the data needs some preparation: _de-meaning_. De-meaning, or _person-mean centering_, or _centering within clusters_, takes away the higher-level mean from the regression equation, and as such, FE avoids estimating a parameter for each higher-level unit. ## Computing the de-meaned and group-meaned variables ```{r} qol_cancer <- cbind( qol_cancer, datawizard::demean(qol_cancer, select = c("phq4", "QoL"), group = "ID") ) ``` Now we have: * `phq4_between`: time-varying variable with the mean of `phq4` across all time-points, for each patient (ID). * `phq4_within`: the de-meaned time-varying variable `phq4`. A FE model is a classical linear model, where * Intercept is removed * time-invariant predictors are not allowed to be included * the group-level factor is included as predictor * time-varying predictors are de-meaned ("person-mean centered", indicating the "within-subject" effect) ```{r} fe_model1 <- lm( QoL ~ 0 + time + phq4_within + ID, data = qol_cancer ) # we use only the first two rows, because the remaining rows are # the estimates for "ID", which is not of interest here... model_parameters(fe_model1)[1:2, ] # instead of removing the intercept, we could also use the # de-meaned response... fe_model2 <- lm( QoL_within ~ time + phq4_within + ID, data = qol_cancer ) model_parameters(fe_model2)[2:3, ] # we compare the results with those from the "lfe"-package for panel data library(lfe) fe_model3 <- felm( QoL ~ time + phq4 | ID, data = qol_cancer ) model_parameters(fe_model3) ``` As we can see, the _within-effect_ of PHQ-4 is `-3.66`, hence the mean of the change for an average individual case in our sample (or, the "net" effect), is `-3.66`. But what about the between-effect? How do people with higher PHQ-4 score differ from people with lower PHQ-4 score? Or what about educational inequalities? Do higher educated people have a higher PHQ-4 score than lower educated people? This question cannot be answered with FE regression. But: "Can one fit a multilevel model with varying intercepts (or coefficients) when the units and predictors correlate? The answer is yes. And the solution is simple." [@bafumi_fitting_2006] # Adressing heterogeneity bias: the Mixed Model approach Mixed models include different levels of sources of variability (i.e. error terms at each level). Predictors used at level-1 that are varying across higher-level units will thus have residual errors at both level-1 and higher-level units. "Such covariates contain two parts: one that is specific to the higher-level entity that does not vary between occasions, and one that represents the difference between occasions, within higher-level entities" [@bell_explaining_2015]. Hence, the error terms will be correlated with the covariate, which violates one of the assumptions of mixed models (iid, independent and identically distributed error terms) - also known and described above as _heterogeneity bias_. But how can this issue be addressed outside the FE framework? There are several ways how to address this using a mixed models approach: * Correlated group factors and predictors are no problem anyway, because [partial pooling](https://www.tjmahr.com/plotting-partial-pooling-in-mixed-effects-models/) allows estimates of units o borrow strength from the whole sample and shrink toward a common mean (@shor_bayesian_2007). * If predictor and group factors correlate, one can remove this correlation by group-meaning [or "mean within clusters", @bafumi_fitting_2006; @gelman_data_2007, Chap. 12.6.]. * When time-varying predictors are "decomposed" into their time-varying and time-invariant components (demeaning), then mixed models can model **both** within- and between-subject effects [@bell_fixed_2019] - this approach is essentially a further development of a long-known recommendation by Mundlak [@mundlak_pooling_1978]. For now, we will follow the last recommendation and use the within- and between-version of `phq4`. ```{r} library(lme4) mixed_1 <- lmer( QoL ~ time + phq4_within + phq4_between + (1 | ID), data = qol_cancer ) model_parameters(mixed_1) # compare to FE-model model_parameters(fe_model1)[1:2, ] ``` As we can see, the estimates and standard errors are identical. The argument _against_ the use of mixed models, i.e. that using mixed models for panel data will yield biased estimates and standard errors, is based on an incorrect model specification [@mundlak_pooling_1978]. As such, when the (mixed) model is properly specified, the estimator of the mixed model is identical to the 'within' (i.e. FE) estimator. As a consequence, we cannot only use the above specified mixed model for panel data, we can even specify more complex models including within-effects, between-effects or random effects variation. A mixed models approach can model the causes of endogeneity explicitly by including the (separated) within- and between-effects of time-varying fixed effects and including time-constant fixed effects. ```{r} mixed_2 <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID), data = qol_cancer ) # effects = "fixed" will not display random effects, but split the # fixed effects into its between- and within-effects components. model_parameters(mixed_2, effects = "fixed") ``` For more complex models, within-effects will naturally change slightly and are no longer identical to simpler FE models. This is no "bias", but rather the result of building more complex models: FE models lack information of variation in the group-effects or between-subject effects. Furthermore, FE models cannot include random slopes, which means that fixed effects regressions are neglecting "cross-cluster differences in the effects of lower-level controls (which) reduces the precision of estimated context effects, resulting in (...) low statistical power" [@heisig_costs_2017]. # Conclusion: Complex Random Effects Within-Between Models Depending on the structure of the data, the best approach to analyzing panel data would be a so called "complex random effects within-between" model [@bell_fixed_2019]: ```{r echo=FALSE} f <- "yit = β0 + β1W (xit - ͞xi) + β2B ͞xi + β3 zi + υi0 + υi1 (xit - ͞xi) + εit" knitr::asis_output(f) ``` ```{r echo=FALSE} f <- "

" knitr::asis_output(f) ``` In R-code, the model is written down like this: ```{r} rewb <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID) + (1 + phq4_within | ID), data = qol_cancer ) ``` **What about time-constant predictors?** After demeaning time-varying predictors, "at the higher level, the mean term is no longer constrained by Level 1 effects, so it is free to account for all the higher-level variance associated with that variable" [@bell_explaining_2015]. Thus, _time-constant **categorical**_ predictors, that only have a between-effect, can be simply included as fixed effects predictor (since they’re not constrained by level-1 effects). Time-constant _continuous_ **group-level predictors** (for instance, GDP of countries) should be group-meaned, to have a proper "between"-effect [@gelman_data_2007, Chap. 12.6.]. The benefit of this kind of model is that you have information on within-, between- and other time-constant (i.e. between) effects or group-level predictors... ```{r} model_parameters(rewb, effects = "fixed") ``` ... but you can also model the variation of (group) effects across time (and probably space), and you can even include more higher-level units (e.g. nested design or cross-classified design with more than two levels): ```{r} random_parameters(rewb) ``` **What about imbalanced groups, i.e. large differences in N per group?** See little example after this visual example... # A visual example First, we generate some fake data that implies a linear relationship between outcome and independent variable. The objective is that the amount of typing errors depends on how fast (typing speed) you can type, however, the more typing experience you have, the faster you can type. Thus, the outcome measure is "amount of typing errors", while our predictor is "typing speed". Furthermore, we have repeated measurements of people with different "typing experience levels". The results show that we will have two sources of variation: Overall, more experienced typists make less mistakes (group-level pattern). When typing faster, typists make more mistakes (individual-level pattern). ```{r} library(ggplot2) library(poorman) library(see) set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) ``` Let's look at the raw data... ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` ## Model 1: Linear relationship between typing errors and typing speed We can now assume a (linear) relationship between typing errors and typing speed. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` Looking at the coefficients, we have following model with a coefficient of `-1.92`. ```{r} m1 <- lm(y ~ x, data = d) model_parameters(m1) ``` However, we have ignored the clustered structure in our data, in this example due to repeated measurements. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` ## Model 2: Within-subject effect of typing speed A fixed effects regression (FE-regression) would now remove all between-effects and include only the within-effects as well as the group-level indicator. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` This returns the coefficient of the "within"-effect, which is `1.2`, with a standard error of `0.07`. Note that the FE-model does *not* take the variation *between* subjects into account, thus resulting in (possibly) biased estimates, and biased standard errors. ```{r} m2 <- lm(y ~ 0 + x_within + grp, data = d) model_parameters(m2)[1, ] ``` ## Model 3: Between-subject effect of typing speed To understand, why the above model 1 (`m1`) returns a biased estimate, which is a "weighted average" of the within- and between-effects, let us look at the between-effect now. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` As we can see, the between-effect is `-2.93`, which is different from the `-1.92` estimated in the model `m1`. ```{r} m3 <- lm(y ~ x_between, data = d) model_parameters(m3) ``` ## Model 4: Mixed model with within- and between-subjects Since FE-models can only model within-effects, we now use a mixed model with within- and between-effects. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` We see, the estimate for the within-effects is *not* biased. Furthermore, we get the correct between-effect as well (standard errors differ, because the variance in the grouping structure is more accurately taken into account). ```{r} m4 <- lmer(y ~ x_between + x_within + (1 | grp), data = d) model_parameters(m4) ``` ## Model 5: Complex Random-Effects Within-Between Model Finally, we can also take the variation between subjects into account by adding a random slope. This model can be called a complex "REWB" (random-effects within-between) model. Due to the variation between subjects, we get larger standard errors for the within-effect. ```{r} m5 <- lmer(y ~ x_between + x_within + (1 + x_within | grp), data = d) model_parameters(m5) ``` # Balanced versus imbalanced groups The "simple" linear slope of the between-effect (and also from the within-effect) is (almost) identical in "classical" linear regression compared to linear mixed models when the groups are balanced, i.e. when the number of observation per group is similar or the same. Whenever group size is imbalanced, the "simple" linear slope will be adjusted. This leads to different estimates for between-effects between classical and mixed models regressions due to shrinkage - i.e. for larger variation of group sizes we find stronger regularization of estimates. Hence, for mixed models with larger differences in number of observation per random effects group, the between-effect will differ from the between-effect calculated by "classical" regression models. However, this shrinkage is a desired property of mixed models and usually improves the estimates. ```{r} set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) # create imbalanced groups d$grp[sample(which(d$grp == 8), 10)] <- 6 d$grp[sample(which(d$grp == 4), 8)] <- 2 d$grp[sample(which(d$grp == 10), 9)] <- 6 d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) # Between-subject effect of typing speed m1 <- lm(y ~ x_between, data = d) model_parameters(m1) # Between-subject effect of typing speed, accounting for group structure m2 <- lmer(y ~ x_between + (1 | grp), data = d) model_parameters(m2) ``` # References datawizard/vignettes/bibliography.bib0000644000176200001440000001135514113652110017544 0ustar liggesusers@Manual{revelle2018, title = {psych: Procedures for Psychological, Psychometric, and Personality Research}, author = {William Revelle}, organization = { Northwestern University}, address = { Evanston, Illinois}, year = {2018}, note = {R package version 1.8.12}, url = {https://CRAN.R-project.org/package=psych} } @article{makowski2018psycho, title={The psycho package: An efficient and publishing-oriented workflow for psychological science}, author={Makowski, Dominique}, journal={Journal of Open Source Software}, volume={3}, number={22}, pages={470}, year={2018} } @article{menard2011standards, title={Standards for standardized logistic regression coefficients}, author={Menard, Scott}, journal={Social Forces}, volume={89}, number={4}, pages={1409--1428}, year={2011}, publisher={The University of North Carolina Press} } @article{schielzeth2010simple, title={Simple means to improve the interpretability of regression coefficients}, author={Schielzeth, Holger}, journal={Methods in Ecology and Evolution}, volume={1}, number={2}, pages={103--113}, year={2010}, publisher={Wiley Online Library} } @article{gelman2008scaling, title={Scaling regression inputs by dividing by two standard deviations}, author={Gelman, Andrew}, journal={Statistics in medicine}, volume={27}, number={15}, pages={2865--2873}, year={2008}, publisher={Wiley Online Library} } @article{menard2004six, title={Six approaches to calculating standardized logistic regression coefficients}, author={Menard, Scott}, journal={The American Statistician}, volume={58}, number={3}, pages={218--223}, year={2004}, publisher={Taylor \& Francis} } @article{bring1994standardize, title={How to standardize regression coefficients}, author={Bring, Johan}, journal={The American Statistician}, volume={48}, number={3}, pages={209--213}, year={1994}, publisher={Taylor \& Francis} } @article{neter1989applied, title={Applied linear regression models}, author={Neter, John and Wasserman, William and Kutner, Michael H}, year={1989}, publisher={Irwin Homewood, IL} } @book{gelman_data_2007, address = {Cambridge; New York}, series = {Analytical methods for social research}, title = {Data analysis using regression and multilevel/hierarchical models}, isbn = {978-0-521-86706-1 978-0-521-68689-1}, publisher = {Cambridge University Press}, author = {Gelman, Andrew and Hill, Jennifer}, year = {2007} } @inproceedings{bafumi_fitting_2006, address = {Philadelphia, PA}, title = {Fitting Multilevel Models When Predictors and Group Effects Correlate.}, author = {Bafumi, Joseph and Gelman, Andrew}, year = {2006} } @article{bell_fixed_2019, title = {Fixed and random effects models: making an informed choice}, issn = {1573-7845}, url = {https://doi.org/10.1007/s11135-018-0802-x}, doi = {10.1007/s11135-018-0802-x}, journal = {Quality \& Quantity}, author = {Bell, Andrew and Fairbrother, Malcolm and Jones, Kelvyn}, volume = {53}, year = {2019}, pages = {1051--1074} } @article{bell_explaining_2015, title = {Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data}, volume = {3}, issn = {2049-8470, 2049-8489}, url = {https://www.cambridge.org/core/product/identifier/S2049847014000077/type/journal_article}, doi = {10.1017/psrm.2014.7}, number = {1}, journal = {Political Science Research and Methods}, author = {Bell, Andrew and Jones, Kelvyn}, month = jan, year = {2015}, pages = {133--153} } @article{heisig_costs_2017, title = {The Costs of Simplicity: Why Multilevel Models May Benefit from Accounting for Cross-Cluster Differences in the Effects of Controls}, volume = {82}, issn = {0003-1224, 1939-8271}, url = {http://journals.sagepub.com/doi/10.1177/0003122417717901}, doi = {10.1177/0003122417717901}, number = {4}, journal = {American Sociological Review}, author = {Heisig, Jan Paul and Schaeffer, Merlin and Giesecke, Johannes}, month = aug, year = {2017}, pages = {796--827} } @article{shor_bayesian_2007, title = {A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data}, volume = {15}, issn = {1047-1987, 1476-4989}, doi = {10.1093/pan/mpm006}, number = {2}, journal = {Political Analysis}, author = {Shor, Boris and Bafumi, Joseph and Keele, Luke and Park, David}, year = {2007}, pages = {165--181} } @article{mundlak_pooling_1978, title = {On the Pooling of Time Series and Cross Section Data}, volume = {46}, number = {1}, journal = {Econometrica}, author = {Mundlak, Yair}, month = jan, year = {1978}, pages = {69} } datawizard/vignettes/standardize_data.Rmd0000644000176200001440000003132014174263411020363 0ustar liggesusers--- title: "Data Standardization" output: rmarkdown::html_vignette: toc: true fig_width: 10.08 fig_height: 6 vignette: > \usepackage[utf8]{inputenc} %\VignetteIndexEntry{Data Standardization} %\VignetteEngine{knitr::rmarkdown} --- ```{r message=FALSE, warning=FALSE, include=FALSE} library(knitr) options(knitr.kable.NA = "") knitr::opts_chunk$set( comment = ">", message = FALSE, warning = FALSE, dpi = 300 ) pkgs <- c( "datawizard", "poorman", "see", "ggplot2", "parameters", "lme4" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } ``` # Introduction To make sense of their data and effects, scientists might want to standardize (Z-score) their variables. This makes the data unitless, expressed only in terms of deviation from an index of centrality (e.g., the mean or the median). However, aside from some benefits, standardization also comes with challenges and issues, that the scientist should be aware of. ## Methods of Standardization The `datawizard` package offers two methods of standardization via the `standardize()` function: - **Normal standardization**: center around the *mean*, with *SD* units (default). - **Robust standardization**: center around the *median*, with *MAD* (median absolute deviation) units (`robust = TRUE`). Let's look at the following example: ```{r} library(datawizard) library(effectsize) # for data # let's have a look at what the data look like data("hardlyworking", package = "effectsize") head(hardlyworking) # let's use both methods of standardization hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours) hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE) ``` We can see that different methods give different central and variation values: ```{r, eval=FALSE} library(dplyr) library(tidyr) hardlyworking %>% select(starts_with("xtra_hours")) %>% pivot_longer(everything()) %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) ``` ```{r, echo=FALSE} library(poorman) hardlyworking %>% select(starts_with("xtra_hours")) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ``` `standardize()` can also be used to standardize a full data frame - where each numeric variable is standardized separately: ```{r} hardlyworking_z <- standardize(hardlyworking) ``` ```{r, eval=FALSE} hardlyworking_z %>% select(-xtra_hours_z, -xtra_hours_zr) %>% pivot_longer(everything()) %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) ``` ```{r, echo=FALSE} hardlyworking_z %>% select(-xtra_hours_z, -xtra_hours_zr) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ``` Weighted standardization is also supported via the `weights` argument, and factors can also be standardized (if you're into that kind of thing) by setting `force = TRUE`, which converts factors to treatment-coded dummy variables before standardizing. ## Variable-wise *vs.* Participant-wise Standardization is an important step and extra caution is required in **repeated-measures designs**, in which there are three ways of standardizing data: - **Variable-wise**: The most common method. A simple scaling of each column. - **Participant-wise**: Variables are standardized "within" each participant, *i.e.*, for each participant, by the participant's mean and SD. - **Full**: Participant-wise first and then re-standardizing variable-wise. Unfortunately, the method used is often not explicitly stated. This is an issue as these methods can generate important discrepancies (that can in turn contribute to the reproducibility crisis). Let's investigate these 3 methods. ### The Data We will take the `emotion` dataset in which participants were exposed to negative pictures and had to rate their emotions (**valence**) and the amount of memories associated with the picture (**autobiographical link**). One could make the hypothesis that for young participants with no context of war or violence, the most negative pictures (mutilations) are less related to memories than less negative pictures (involving for example car crashes or sick people). In other words, **we expect a positive relationship between valence** (with high values corresponding to less negativity) **and autobiographical link**. Let's have a look at the data, averaged by participants: ```{r, eval=FALSE} # Download the 'emotion' dataset load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == "Negative") # Summary emotion %>% drop_na(Subjective_Valence, Autobiographical_Link) %>% group_by(Participant_ID) %>% summarise( n_Trials = n(), Valence_Mean = mean(Subjective_Valence), Valence_SD = sd(Subjective_Valence) ) ``` ```{r, echo=FALSE} load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == "Negative") # Summary emotion %>% subset(!(is.na(Subjective_Valence) | is.na(Autobiographical_Link))) %>% group_by(Participant_ID) %>% summarise( n_Trials = n(), Valence_Mean = mean(Subjective_Valence), Valence_SD = sd(Subjective_Valence) ) ``` As we can see from the means and SDs, there is a lot of variability **between** participants both in their means and their individual *within*-participant SD. ### Effect of Standardization We will create three data frames standardized with each of the three techniques. ```{r} Z_VariableWise <- emotion %>% standardize() Z_ParticipantWise <- emotion %>% group_by(Participant_ID) %>% standardize() Z_Full <- emotion %>% group_by(Participant_ID) %>% standardize() %>% ungroup() %>% standardize() ``` Let's see how these three standardization techniques affected the **Valence** variable. ### Across Participants We can calculate the mean and SD of *Valence* across all participants: ```{r, eval=FALSE} # Create a convenient function to print summarise_Subjective_Valence <- function(data) { df_name <- deparse(substitute(data)) data %>% ungroup() %>% summarise( DF = df_name, Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) } # Check the results rbind( summarise_Subjective_Valence(Z_VariableWise), summarise_Subjective_Valence(Z_ParticipantWise), summarise_Subjective_Valence(Z_Full) ) ``` ```{r, echo=FALSE} # Create a convenient function to print summarise_Subjective_Valence <- function(data) { df_name <- deparse(substitute(data)) data <- data %>% ungroup() %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) cbind(DF = df_name, data) } # Check the results rbind( summarise_Subjective_Valence(Z_VariableWise), summarise_Subjective_Valence(Z_ParticipantWise), summarise_Subjective_Valence(Z_Full) ) %>% knitr::kable(digits = 2) ``` The **means** and the **SD** appear as fairly similar (0 and 1)... ```{r, fig.width=7, fig.height=4.5, results='markup', fig.align='center'} library(see) library(ggplot2) ggplot() + geom_density(aes(Z_VariableWise$Subjective_Valence, color = "Z_VariableWise" ), size = 1) + geom_density(aes(Z_ParticipantWise$Subjective_Valence, color = "Z_ParticipantWise" ), size = 1) + geom_density(aes(Z_Full$Subjective_Valence, color = "Z_Full" ), size = 1) + see::theme_modern() + labs(color = "") ``` and so do the marginal distributions... ### At the Participant Level However, we can also look at what happens in the participant level. Let's look at the first 5 participants: ```{r, eval=FALSE} # Create convenient function print_participants <- function(data) { df_name <- deparse(substitute(data)) data %>% group_by(Participant_ID) %>% summarise( DF = df_name, Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) %>% head(5) %>% select(DF, everything()) } # Check the results rbind( print_participants(Z_VariableWise), print_participants(Z_ParticipantWise), print_participants(Z_Full) ) ``` ```{r, echo=FALSE} # Create convenient function print_participants <- function(data) { df_name <- deparse(substitute(data)) data %>% group_by(Participant_ID) %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) %>% cbind(DF = df_name, .) %>% head(5) %>% select(DF, everything()) } # Check the results rbind( print_participants(Z_VariableWise), print_participants(Z_ParticipantWise), print_participants(Z_Full) ) %>% knitr::kable(digits = 2) ``` Seems like *full* and *participant-wise* standardization give similar results, but different ones than *variable-wise* standardization. ### Compare Let's do a **correlation** between the **variable-wise and participant-wise methods**. ```{r, fig.width=7, fig.height=4.5, results='markup', fig.align='center'} r <- cor.test( Z_VariableWise$Subjective_Valence, Z_ParticipantWise$Subjective_Valence ) data.frame( Original = emotion$Subjective_Valence, VariableWise = Z_VariableWise$Subjective_Valence, ParticipantWise = Z_ParticipantWise$Subjective_Valence ) %>% ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) + geom_point(alpha = 0.75, shape = 16) + geom_smooth(method = "lm", color = "black") + scale_color_distiller(palette = 1) + ggtitle(paste0("r = ", round(r$estimate, 2))) + see::theme_modern() ``` While the three standardization methods roughly present the same characteristics at a general level (mean 0 and SD 1) and a similar distribution, their values are not exactly the same! Let's now answer the original question by investigating the **linear relationship between valence and autobiographical link**. We can do this by running a mixed-effects model with participants entered as random effects. ```{r} library(lme4) m_raw <- lmer( formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID), data = emotion ) m_VariableWise <- update(m_raw, data = Z_VariableWise) m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise) m_Full <- update(m_raw, data = Z_Full) ``` We can extract the parameters of interest from each model, and find: ```{r} # Convenient function get_par <- function(model) { mod_name <- deparse(substitute(model)) parameters::model_parameters(model) %>% mutate(Model = mod_name) %>% select(-Parameter) %>% select(Model, everything()) %>% .[-1, ] } # Run the model on all datasets rbind( get_par(m_raw), get_par(m_VariableWise), get_par(m_ParticipantWise), get_par(m_Full) ) ``` As we can see, **variable-wise** standardization only affects **the coefficient** (which is expected, as it changes the unit), but not the test statistic or statistical significance. However, using **participant-wise** standardization *does* affect the coefficient **and** the significance. **No method is better or more justified, and the choice depends on the specific case, context, data and goal.** ### Conclusion 1. **Standardization can be useful in *some* cases and should be justified**. 2. **Variable and Participant-wise standardization methods *appear* to produce similar data**. 3. **Variable and Participant-wise standardization can lead to different results**. 4. **The chosen method can strongly influence the results and should therefore be explicitly stated and justified to enhance reproducibility of results**. We showed here yet another way of **sneakily tweaking the data** that can change the results. To prevent its use as a bad practice, we can only highlight the importance of open data, open analysis/scripts, and preregistration. # See also - `datawizard::demean()`: - `standardize_parameters(method = "pseudo")` for mixed-effects models # References datawizard/R/0000755000176200001440000000000014174263210012605 5ustar liggesusersdatawizard/R/data_findcols.R0000644000176200001440000000200714141067100015513 0ustar liggesusers#' @rdname data_rename #' @examples #' # Find columns names by pattern #' data_findcols(iris, starts_with = "Sepal") #' data_findcols(iris, ends_with = "Width") #' data_findcols(iris, pattern = "\\.") #' data_findcols(iris, c("Petal.Width", "Sepal.Length")) #' @export data_findcols <- function(data, pattern = NULL, starts_with = NULL, ends_with = NULL, ...) { # TODO: Need to extend this to work with NSE so that the following shoud work: # - data_findcols(iris, Sepal.Length) # - data_findcols(iris, starts_with("Sepal")) # - data_findcols(iris, contains("Sepal")) n <- names(data) if (!is.null(pattern)) { match <- c() for (i in c(pattern)) { match <- c(match, n[grepl(i, n)]) } } if (!is.null(starts_with)) { match <- n[grepl(paste0("^", starts_with), n)] } if (!is.null(ends_with)) { match <- n[grepl(paste0(ends_with, "$"), n)] } match } datawizard/R/data_rename.R0000644000176200001440000000425414113652110015167 0ustar liggesusers#' Convenient dataframe manipulation functionalities #' #' Safe and intuitive functions to manipulate dataframes. #' #' @param data A data frame, or an object that can be coerced to a data frame. #' @param pattern,replacement,starts_with,ends_with Character strings. #' @param cols,rows Vector of column or row names. #' @param safe Do not throw error if for instance the variable to be #' renamed/removed doesn't exist. #' @param ... Other arguments passed to or from other functions. #' #' @return A modified data frame. #' #' @examples #' # Rename columns #' head(data_rename(iris, "Sepal.Length", "length")) #' # data_rename(iris, "FakeCol", "length", safe=FALSE) # This fails #' head(data_rename(iris, "FakeCol", "length")) # This doesn't #' head(data_rename(iris, c("Sepal.Length", "Sepal.Width"), c("length", "width"))) #' #' # Reset names #' head(data_rename(iris, NULL)) #' #' # Change all #' head(data_rename(iris, paste0("Var", 1:5))) #' @export data_rename <- function(data, pattern = NULL, replacement = NULL, safe = TRUE, ...) { # Sanity checks if (is.null(replacement) && is.null(pattern)) { names(data) <- c(1:ncol(data)) return(data) } else if (is.null(replacement) && !is.null(pattern)) { names(data) <- pattern return(data) } else if (!is.null(replacement) && is.null(pattern)) { names(data) <- replacement return(data) } if (length(pattern) != length(replacement)) { stop("The 'replacement' names must be of the same length than the variable names.") } for (i in 1:length(pattern)) { data <- .data_rename(data, pattern[i], replacement[i], safe) } data } #' @keywords internal .data_rename <- function(data, pattern, replacement, safe = TRUE) { if (isFALSE(safe) & !pattern %in% names(data)) { stop(paste0("Variable '", pattern, "' is not in your dataframe :/")) } names(data) <- replace(names(data), names(data) == pattern, replacement) data } # Row.names ---------------------------------------------------------------- #' @rdname data_rename #' @export data_rename_rows <- function(data, rows = NULL) { row.names(data) <- rows data } datawizard/R/utils_get_dots.R0000644000176200001440000000643714113652110015764 0ustar liggesusers# function to evaluate dots in a tidyselect-style and return # the variable names as character vector .get_dot_data <- function(dat, dots, verbose = TRUE) { if (!is.data.frame(dat) || length(dots) == 0) { return(dat) } columns <- colnames(dat) x <- unlist(lapply(dots, function(i) { # contains-token if (grepl("^contains\\(", i)) { pattern <- gsub("contains\\(\"(.*)\"\\)", "\\1", i) columns[string_contains(pattern, columns)] # starts-with token } else if (grepl("^starts\\(", i) || grepl("^starts_with\\(", i)) { pattern <- gsub("(.*)\\(\"(.*)\"\\)", "\\2", i) columns[string_starts_with(pattern, columns)] # ends-with token } else if (grepl("^ends\\(", i) || grepl("^ends_with\\(", i)) { pattern <- gsub("(.*)\\(\"(.*)\"\\)", "\\2", i) columns[string_ends_with(pattern, columns)] # one-of token } else if (grepl("^one_of\\(", i)) { pattern <- gsub("(\"|\\s)", "", unlist(strsplit(gsub("one_of\\(\"(.*)\"\\)", "\\1", i), ","))) columns[string_one_of(pattern, columns)] # num_range token } else if (grepl("^num_range\\(", i)) { columns[match(.get_num_range(i), columns)] # from-to token } else if (grepl(":", i, fixed = TRUE)) { tmp <- unlist(strsplit(i, ":", fixed = TRUE)) start <- if (.is_num_chr(tmp[1])) { as.numeric(tmp[1]) } else { which(columns == tmp[1]) } end <- if (.is_num_chr(tmp[2])) { as.numeric(tmp[2]) } else { which(columns == tmp[2]) } columns[start:end] # simple name } else { i } })) x <- unlist(lapply(x, function(i) { if (.is_num_chr(i)) { columns[as.numeric(i)] } else if (.is_num_fac(i)) { columns[as.numeric(as.character(i))] } else { i } })) not_found <- setdiff(x, columns) if (length(not_found) && isTRUE(verbose)) { insight::print_color(sprintf( "%i variables were not found in the dataset: %s\n", length(not_found), paste0(not_found, collapse = ", ") ), color = "red" ) } dat[, intersect(x, columns), drop = FALSE] } .is_num_chr <- function(x) { is.character(x) && !anyNA(suppressWarnings(as.numeric(stats::na.omit(x)))) } .is_num_fac <- function(x) { is.factor(x) && !anyNA(suppressWarnings(as.numeric(levels(x)))) } .get_num_range <- function(i) { r1 <- trimws(unlist(strsplit(gsub("num_range\\((.*)\\)", "\\1", i), ","))) r2 <- gsub("\"", "", trimws(gsub("(.*)(=)(.*)", "\\3", r1)), fixed = TRUE) es <- grepl("=", r1) if (any(es)) { names(r2)[es] <- trimws(gsub("(.*)(=)(.*)", "\\1", r1[es])) } args <- c("prefix", "range", "width") if (is.null(names(r2))) { names(r2) <- args[1:length(r2)] } na_names <- which(is.na(names(r2))) if (length(na_names)) { names(r2)[na_names] <- args[na_names] } if (length(r2) > 3) { r2 <- r2[1:3] } from <- as.numeric(gsub("(\\d):(.*)", "\\1", r2["range"])) to <- as.numeric(gsub("(.*):(\\d)", "\\2", r2["range"])) width <- as.numeric(r2["width"]) if (is.na(width)) { sprintf("%s%i", r2["prefix"], from:to) } else { sprintf("%s%.*i", r2["prefix"], width, from:to) } } datawizard/R/reshape_ci.R0000644000176200001440000000763114131371650015042 0ustar liggesusers#' Reshape CI between wide/long formats #' #' Reshape CI between wide/long formats. #' #' @param x A data frame containing columns named `CI_low` and `CI_high` (or #' similar, see `ci_type`). #' @param ci_type String indicating the "type" (i.e. prefix) of the interval #' columns. Per *easystats* convention, confidence or credible intervals are #' named `CI_low` and `CI_high`, and the related `ci_type` would be `"CI"`. #' If column names for other intervals differ, `ci_type` can be used to #' indicate the name, e.g. `ci_type = "SI"` can be used for support intervals, #' where the column names in the data frame would be `SI_low` and `SI_high`. #' #' @return #' #' A dataframe with columns corresponding to confidence intervals reshaped #' either to wide or long format. #' #' @examples #' x <- data.frame( #' Parameter = c("Term 1", "Term 2", "Term 1", "Term 2"), #' CI = c(.8, .8, .9, .9), #' CI_low = c(.2, .3, .1, .15), #' CI_high = c(.5, .6, .8, .85), #' stringsAsFactors = FALSE #' ) #' #' reshape_ci(x) #' reshape_ci(reshape_ci(x)) #' @export reshape_ci <- function(x, ci_type = "CI") { # define interval type ci_type <- match.arg(ci_type, choices = c("CI", "SI", "HDI", "ETI")) ci_low <- paste0(ci_type, "_low") ci_high <- paste0(ci_type, "_high") # Long to wide ---------------- if (ci_low %in% names(x) & ci_high %in% names(x) & "CI" %in% names(x)) { ci_position <- which(names(x) == "CI") # Reshape if (length(unique(x$CI)) > 1) { if (!"Parameter" %in% names(x)) { x$Parameter <- NA remove_parameter <- TRUE } else { remove_parameter <- FALSE } x <- stats::reshape( x, idvar = "Parameter", timevar = "CI", direction = "wide", v.names = c(ci_low, ci_high), sep = "_" ) row.names(x) <- NULL if (remove_parameter) x$Parameter <- NULL } # Replace at the right place ci_colname <- names(x)[c(grepl(paste0(ci_low, "_*"), names(x)) | grepl(paste0(ci_high, "_*"), names(x)))] colnames_1 <- names(x)[0:(ci_position - 1)][!names(x)[0:(ci_position - 1)] %in% ci_colname] colnames_2 <- names(x)[!names(x) %in% c(ci_colname, colnames_1)] x <- x[c(colnames_1, ci_colname, colnames_2)] # Wide to long -------------- } else { if (!"Parameter" %in% names(x)) { x$Parameter <- 1:nrow(x) remove_parameter <- TRUE } else { remove_parameter <- FALSE } lows <- grepl(paste0(ci_low, "_*"), names(x)) highs <- grepl(paste0(ci_high, "_*"), names(x)) ci <- as.numeric(gsub(paste0(ci_low, "_"), "", names(x)[lows])) if (paste0(ci, collapse = "-") != paste0(gsub(paste0(ci_high, "_"), "", names(x)[highs]), collapse = "-")) { stop("Something went wrong in the CIs reshaping.") return(x) } if (sum(lows) > 1 & sum(highs) > 1) { low <- stats::reshape( x[!highs], direction = "long", varying = list(names(x)[lows]), sep = "_", timevar = "CI", v.names = ci_low, times = ci ) high <- stats::reshape( x[!lows], direction = "long", varying = list(names(x)[highs]), sep = "_", timevar = "CI", v.names = ci_high, times = ci ) x <- merge(low, high) x$id <- NULL x <- x[order(x$Parameter), ] row.names(x) <- NULL if (remove_parameter) x$Parameter <- NULL } # Replace at the right place ci_position <- which(lows)[1] ci_colname <- c("CI", ci_low, ci_high) colnames_1 <- names(x)[0:(ci_position - 1)][!names(x)[0:(ci_position - 1)] %in% ci_colname] colnames_2 <- names(x)[!names(x) %in% c(ci_colname, colnames_1)] x <- x[c(colnames_1, ci_colname, colnames_2)] } class(x) <- intersect(c("data.frame", "numeric"), class(x)) x } datawizard/R/data_extract.R0000644000176200001440000000454514164744510015411 0ustar liggesusers#' Extract a single column or element from an object #' #' `extract()` is similar to `$`. It extracts a single column or element from an object #' (e.g., a data frame, list, ) #' #' @param data The object to subset. Methods are currently available for data frames #' and data frame extensions (e.g., tibbles). #' @param select A variable specified as: #' #' - a literal variable name (e.g., `column_name`) #' - a character vector with the variable name (e.g., `"column_name"`) #' - a positive integer, giving the position counting from the left #' - a negative integer, giving the position counting from the right. #' #' The default returns the last column. #' If the special value `0` or `"row.names"` is given, the row names of the #' object (if any) are extracted. #' @param name An optional argument that specifies the column to be used as #' names for for the vector after extraction. #' Specified in the same way as `select`. #' @param ... For use by future methods. #' #' @return A vector containing the extracted element. #' @export #' #' @examples #' extract(mtcars, cyl, name = gear) #' extract(mtcars, "cyl", name = gear) #' extract(mtcars, -1, name = gear) #' extract(mtcars, cyl, name = 0) #' extract(mtcars, cyl, name = "row.names") data_extract <- function(data, select, name = NULL, ...) { UseMethod("data_extract") } #' @export #' @rdname data_extract extract <- data_extract #' @export data_extract.data.frame <- function(data, select, name = NULL, ...) { nl <- as.list(seq_along(data)) names(nl) <- names(data) select <- eval(substitute(select), nl, parent.frame()) if (is.numeric(select) && select < 0) { select <- length(data) + select + 1 } else if (is.numeric(select) && select == 0) { select <- rownames(data) } else if (is.character(select) && select == "row.names") { select <- rownames(data) } name <- eval(substitute(name), nl, parent.frame()) if (is.numeric(name) && name < 0) { name <- length(data) + name + 1 } else if (is.numeric(name) && name == 0) { name <- rownames(data) } else if (is.character(name) && name == "row.names") { name <- rownames(data) } if (length(name) == 1) { stats::setNames(data[, select, drop = TRUE], data[, name, drop = TRUE]) } else { stats::setNames(data[, select, drop = TRUE], name) } } datawizard/R/ranktransform.R0000644000176200001440000001050014141236110015604 0ustar liggesusers#' (Signed) rank transformation #' #' Transform numeric values with the integers of their rank (i.e., 1st smallest, #' 2nd smallest, 3rd smallest, etc.). Setting the `sign` argument to `TRUE` will #' give you signed ranks, where the ranking is done according to absolute size #' but where the sign is preserved (i.e., 2, 1, -3, 4). #' #' @inheritParams standardize.data.frame #' #' @param x Object. #' @param sign Logical, if `TRUE`, return signed ranks. #' @param method Treatment of ties. Can be one of `"average"` (default), #' `"first"`, `"last"`, `"random"`, `"max"` or `"min"`. See [rank()] for #' details. #' @param ... Arguments passed to or from other methods. #' #' @examples #' ranktransform(c(0, 1, 5, -5, -2)) #' ranktransform(c(0, 1, 5, -5, -2), sign = TRUE) #' #' head(ranktransform(trees)) #' @return A rank-transformed object. #' #' @family transform utilities #' #' @export ranktransform <- function(x, ...) { UseMethod("ranktransform") } #' @rdname ranktransform #' @export ranktransform.numeric <- function(x, sign = FALSE, method = "average", verbose = TRUE, ...) { # Warning if all NaNs if (all(is.na(x))) { return(x) } # Warning if only one value if (length(unique(x)) == 1) { if (is.null(names(x))) { name <- deparse(substitute(x)) } else { name <- names(x) } if (verbose) { warning(paste0("Variable `", name, "` contains only one unique value and will not be normalized.")) } return(x) } # Warning if logical vector if (length(unique(x)) == 2) { if (is.null(names(x))) { name <- deparse(substitute(x)) } else { name <- names(x) } if (verbose) { warning(paste0("Variable `", name, "` contains only two different values. Consider converting it to a factor.")) } } if (sign) { ZEROES <- x == 0 if (any(ZEROES) && verbose) warning("Zeros detected. These cannot be sign-rank transformed.") out <- rep(NA, length(x)) out[!ZEROES] <- sign(x[!ZEROES]) * rank(abs(x[!ZEROES]), ties.method = method, na.last = "keep") } else { out <- rank(x, ties.method = method, na.last = "keep") } out } #' @export ranktransform.factor <- function(x, ...) { x } #' @rdname ranktransform #' @export ranktransform.grouped_df <- function(x, select = NULL, exclude = NULL, sign = FALSE, method = "average", ...) { info <- attributes(x) # dplyr >= 0.8.0 returns attribute "indices" grps <- attr(x, "groups", exact = TRUE) # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } # dplyr < 0.8.0? if (is.null(grps)) { grps <- attr(x, "indices", exact = TRUE) grps <- lapply(grps, function(x) x + 1) } else { grps <- grps[[".rows"]] } x <- as.data.frame(x) for (rows in grps) { x[rows, ] <- ranktransform( x[rows, ], select = select, exclude = exclude, sign = sign, method = method, ... ) } # set back class, so data frame still works with dplyr attributes(x) <- info x } #' @rdname ranktransform #' @export ranktransform.data.frame <- function(x, select = NULL, exclude = NULL, sign = FALSE, method = "average", ...) { # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } if (is.null(select)) { select <- names(x) } if (!is.null(exclude)) { select <- setdiff(select, exclude) } x[select] <- lapply(x[select], ranktransform, sign = sign, method = method) x } datawizard/R/describe_distribution.R0000644000176200001440000002603014113652110017302 0ustar liggesusers#' Describe a distribution #' #' This function describes a distribution by a set of indices (e.g., measures of #' centrality, dispersion, range, skewness, kurtosis). #' #' @param x A numeric vector. #' @param range Return the range (min and max). #' @param quartiles Return the first and third quartiles (25th and 75pth #' percentiles). #' @param include_factors Logical, if `TRUE`, factors are included in the #' output, however, only columns for range (first and last factor levels) as #' well as n and missing will contain information. #' @param ci Confidence Interval (CI) level. Default is `NULL`, i.e. no #' confidence intervals are computed. If not `NULL`, confidence intervals #' are based on bootstrap replicates (see `iterations`). If #' `centrality = "all"`, the bootstrapped confidence interval refers to #' the first centrality index (which is typically the median). #' @param iterations The number of bootstrap replicates for computing confidence #' intervals. Only applies when `ci` is not `NULL`. #' @param iqr Logical, if `TRUE`, the interquartile range is calculated #' (based on [stats::IQR()], using `type = 6`). #' @param verbose Toggle warnings and messages. #' @inheritParams bayestestR::point_estimate #' #' @note There is also a #' [`plot()`-method](https://easystats.github.io/see/articles/parameters.html) #' implemented in the #' \href{https://easystats.github.io/see/}{\pkg{see}-package}. #' #' @return A data frame with columns that describe the properties of the variables. #' @examples #' describe_distribution(rnorm(100)) #' #' data(iris) #' describe_distribution(iris) #' describe_distribution(iris, include_factors = TRUE, quartiles = TRUE) #' @export describe_distribution <- function(x, ...) { UseMethod("describe_distribution") } #' @rdname describe_distribution #' @export describe_distribution.numeric <- function(x, centrality = "mean", dispersion = TRUE, iqr = TRUE, range = TRUE, quartiles = FALSE, ci = NULL, iterations = 100, threshold = .1, verbose = TRUE, ...) { out <- data.frame(.temp = 0) # Missing n_missing <- sum(is.na(x)) x <- stats::na.omit(x) # Point estimates out <- cbind( out, bayestestR::point_estimate( x, centrality = centrality, dispersion = dispersion, threshold = threshold, ... ) ) # interquartile range, type same as minitab and SPSS if (iqr) { out$IQR <- stats::IQR(x, na.rm = TRUE, type = 6) } # Confidence Intervals if (!is.null(ci)) { insight::check_if_installed("boot") results <- boot::boot( data = x, statistic = .boot_distribution, R = iterations, centrality = centrality ) out_ci <- bayestestR::ci(results$t, ci = ci, verbose = FALSE) out <- cbind(out, data.frame(CI_low = out_ci$CI_low[1], CI_high = out_ci$CI_high[1])) } # Range if (range) { out <- cbind( out, data.frame( Min = min(x, na.rm = TRUE), Max = max(x, na.rm = TRUE) ) ) } # Quartiles if (quartiles) { out <- cbind( out, data.frame( Q1 = stats::quantile(x, probs = 0.25, na.rm = TRUE), Q3 = stats::quantile(x, probs = 0.75, na.rm = TRUE) ) ) } # Skewness out <- cbind( out, data.frame( Skewness = as.numeric(skewness(x, verbose = verbose)), Kurtosis = as.numeric(kurtosis(x, verbose = verbose)) ) ) out$n <- length(x) out$n_Missing <- n_missing out$`.temp` <- NULL class(out) <- unique(c("parameters_distribution", "see_parameters_distribution", class(out))) attr(out, "data") <- x attr(out, "ci") <- ci attr(out, "threshold") <- threshold if (centrality == "all") attr(out, "first_centrality") <- colnames(out)[1] out } #' @rdname describe_distribution #' @export describe_distribution.factor <- function(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...) { # Missing n_missing <- sum(is.na(x)) x <- stats::na.omit(x) out <- data.frame( Mean = NA, SD = NA, CI_low = NA, CI_high = NA, IQR = NA, Min = levels(x)[1], Max = levels(x)[nlevels(x)], Q1 = NA, Q3 = NA, Skewness = as.numeric(skewness(x, verbose = verbose)), Kurtosis = as.numeric(kurtosis(x, verbose = verbose)), n = length(x), n_Missing = n_missing, stringsAsFactors = FALSE ) if (!dispersion) { out$SD <- NULL } dot.arguments <- list(...) if (is.null(dot.arguments[["ci"]])) { out$CI_low <- NULL out$CI_high <- NULL } if (is.null(dot.arguments[["iqr"]]) || isFALSE(dot.arguments[["iqr"]])) { out$IQR <- NULL } if (is.null(dot.arguments[["quartiles"]]) || isFALSE(dot.arguments[["quartiles"]])) { out$Q1 <- NULL out$Q3 <- NULL } if (!range) { out$Min <- NULL out$Max <- NULL } class(out) <- unique(c("parameters_distribution", "see_parameters_distribution", class(out))) attr(out, "data") <- x out } #' @export describe_distribution.character <- function(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...) { # Missing n_missing <- sum(is.na(x)) x <- stats::na.omit(x) values <- unique(x) out <- data.frame( Mean = NA, SD = NA, IQR = NA, CI_low = NA, CI_high = NA, Min = values[1], Max = values[length(values)], Q1 = NA, Q3 = NA, Skewness = as.numeric(skewness(x, verbose = verbose)), Kurtosis = as.numeric(kurtosis(x, verbose = verbose)), n = length(x), n_Missing = n_missing, stringsAsFactors = FALSE ) if (!dispersion) { out$SD <- NULL } dot.arguments <- list(...) if (is.null(dot.arguments[["ci"]])) { out$CI_low <- NULL out$CI_high <- NULL } if (is.null(dot.arguments[["iqr"]]) || isFALSE(dot.arguments[["iqr"]])) { out$IQR <- NULL } if (is.null(dot.arguments[["quartiles"]]) || isFALSE(dot.arguments[["quartiles"]])) { out$Q1 <- NULL out$Q3 <- NULL } if (!range) { out$Min <- NULL out$Max <- NULL } class(out) <- unique(c("parameters_distribution", "see_parameters_distribution", class(out))) attr(out, "data") <- x out } #' @rdname describe_distribution #' @export describe_distribution.data.frame <- function(x, centrality = "mean", dispersion = TRUE, iqr = TRUE, range = TRUE, quartiles = FALSE, include_factors = FALSE, ci = NULL, iterations = 100, threshold = .1, verbose = TRUE, ...) { out <- do.call(rbind, lapply(x, function(i) { if ((include_factors && is.factor(i)) || (!is.character(i) && !is.factor(i))) { describe_distribution( i, centrality = centrality, dispersion = dispersion, iqr = iqr, range = range, quartiles = quartiles, ci = ci, iterations = iterations, threshold = threshold, verbose = verbose ) } })) out$Variable <- row.names(out) row.names(out) <- NULL out <- out[c("Variable", setdiff(colnames(out), "Variable"))] class(out) <- unique(c("parameters_distribution", "see_parameters_distribution", class(out))) attr(out, "object_name") <- deparse(substitute(x), width.cutoff = 500) attr(out, "ci") <- ci attr(out, "threshold") <- threshold if (centrality == "all") attr(out, "first_centrality") <- colnames(out)[2] out } #' @export describe_distribution.grouped_df <- function(x, centrality = "mean", dispersion = TRUE, iqr = TRUE, range = TRUE, quartiles = FALSE, include_factors = FALSE, ci = NULL, iterations = 100, threshold = .1, verbose = TRUE, ...) { group_vars <- setdiff(colnames(attributes(x)$groups), ".rows") group_data <- expand.grid(lapply(x[group_vars], function(i) unique(sort(i)))) groups <- split(x, x[group_vars]) out <- do.call(rbind, lapply(1:length(groups), function(i) { d <- describe_distribution.data.frame( groups[[i]], centrality = centrality, dispersion = dispersion, iqr = iqr, range = range, quartiles = quartiles, include_factors = include_factors, ci = ci, iterations = iterations, threshold = threshold, ... ) d[[".group"]] <- paste(sprintf("%s=%s", group_vars, sapply(group_data[i, ], as.character)), collapse = " | ") d })) class(out) <- unique(c("parameters_distribution", "see_parameters_distribution", class(out))) attr(out, "object_name") <- deparse(substitute(x), width.cutoff = 500) attr(out, "ci") <- ci attr(out, "threshold") <- threshold if (centrality == "all") attr(out, "first_centrality") <- colnames(out)[2] out } #' @export print.parameters_distribution <- function(x, digits = 2, ...) { formatted_table <- format( x, digits = digits, format = "text", ci_width = NULL, ci_brackets = TRUE, ... ) cat(insight::export_table(formatted_table, format = "text", digits = digits)) invisible(x) } # bootstrapping CIs ---------------------------------- .boot_distribution <- function(data, indices, centrality) { out <- datawizard::describe_distribution( data[indices], centrality = centrality, dispersion = FALSE, iqr = FALSE, range = FALSE, quartiles = FALSE, ci = NULL ) out[[1]] } datawizard/R/data_relocate.R0000644000176200001440000000505614113652110015517 0ustar liggesusers#' Relocate (reorder) columns of a data frame #' #' @param data A data frame to pivot. #' @param cols A character vector indicating the names of the columns to move. #' @param before,after Destination of columns. Supplying neither will move #' columns to the left-hand side; specifying both is an error. #' @param safe If `TRUE`, will disregard non-existing columns. #' @inheritParams data_rename #' #' @examples #' # Reorder columns #' head(data_relocate(iris, cols = "Species", before = "Sepal.Length")) #' head(data_relocate(iris, cols = "Species", before = "Sepal.Width")) #' head(data_relocate(iris, cols = "Sepal.Width", after = "Species")) #' head(data_relocate(iris, cols = c("Species", "Petal.Length"), after = "Sepal.Width")) #' @return A data frame with reordered columns. #' #' @export data_relocate <- function(data, cols, before = NULL, after = NULL, safe = TRUE, ...) { # Sanitize if (!is.null(before) && !is.null(after)) { stop("You must supply only one of `before` or `after`.") } if (safe) { cols <- cols[cols %in% names(data)] } # save attributes att <- attributes(data) # Move columns to the right hand side data <- data[c(setdiff(names(data), cols), cols)] # Get columns and their original position data_cols <- names(data) position <- which(data_cols %in% cols) # Find new positions if (!is.null(before)) { before <- before[before %in% data_cols][1] # Take first that exists (if vector is supplied) if (length(before) != 1) { stop("The column passed to 'before' wasn't found. Possibly mispelled.") } where <- min(match(before, data_cols)) position <- c(setdiff(position, where), where) } else if (!is.null(after)) { after <- after[after %in% data_cols][1] # Take first that exists (if vector is supplied) if (length(after) != 1) { stop("The column passed to 'after' wasn't found. Possibly mispelled.") } where <- max(match(after, data_cols)) position <- c(where, setdiff(position, where)) } else { where <- 1 position <- union(position, where) } # Set left and right side lhs <- setdiff(seq(1, where - 1), position) rhs <- setdiff(seq(where + 1, ncol(data)), position) position <- unique(c(lhs, position, rhs)) position <- position[position <= length(data_cols)] out <- data[position] attributes(out) <- utils::modifyList(att, attributes(out)) out } datawizard/R/center.R0000644000176200001440000001605114164744506014226 0ustar liggesusers#' Centering (Grand-Mean Centering) #' #' Performs a grand-mean centering of data. #' #' @param x A data frame, a (numeric or character) vector or a factor. #' @param select Character vector of column names. If `NULL` (the default), #' all variables will be selected. #' @param exclude Character vector of column names to be excluded from selection. #' @param force Logical, if `TRUE`, forces centering of factors as #' well. Factors are converted to numerical values, with the lowest level #' being the value `1` (unless the factor has numeric levels, which are #' converted to the corresponding numeric value). #' @param robust Logical, if `TRUE`, centering is done by subtracting the #' median from the variables. If `FALSE`, variables are centered by #' subtracting the mean. #' @param append Logical or string. If `TRUE`, centered variables get new #' column names (with the suffix `"_c"`) and are appended (column bind) to `x`, #' thus returning both the original and the centered variables. If `FALSE`, #' original variables in `x` will be overwritten by their centered versions. #' If a character value, centered variables are appended with new column #' names (using the defined suffix) to the original data frame. #' @param verbose Toggle warnings and messages. #' @param weights Can be `NULL` (for no weighting), or: #' - For data frames: a numeric vector of weights, or a character of the #' name of a column in the `data.frame` that contains the weights. #' - For numeric vectors: a numeric vector of weights. #' @param center Numeric value, which can be used as alternative to #' `reference` to define a reference centrality. If `center` is of length 1, #' it will be recycled to match the length of selected variables for centering. #' Else, `center` must be of same length as the number of selected variables. #' Values in `center` will be matched to selected variables in the provided #' order, unless a named vector is given. In this case, names are matched #' against the names of the selected variables. #' @param ... Currently not used. #' @inheritParams standardize #' #' @note #' **Difference between centering and standardizing**: Standardized variables #' are computed by subtracting the mean of the variable and then dividing it by #' the standard deviation, while centering variables involves only the #' subtraction. #' #' @seealso If centering within-clusters (instead of grand-mean centering) #' is required, see [demean()]. For standardizing, see [standardize()]. #' #' @return The centered variables. #' #' @examples #' data(iris) #' #' # entire dataframe or a vector #' head(iris$Sepal.Width) #' head(center(iris$Sepal.Width)) #' head(center(iris)) #' head(center(iris, force = TRUE)) #' #' # only the selected columns from a dataframe #' center(anscombe, select = c("x1", "x3")) #' center(anscombe, exclude = c("x1", "x3")) #' #' # centering with reference center and scale #' d <- data.frame( #' a = c(-2, -1, 0, 1, 2), #' b = c(3, 4, 5, 6, 7) #' ) #' #' # default centering at mean #' center(d) #' #' # centering, using 0 as mean #' center(d, center = 0) #' #' # centering, using -5 as mean #' center(d, center = -5) #' @export center <- function(x, ...) { UseMethod("center") } #' @rdname center #' @export centre <- center #' @rdname center #' @export center.numeric <- function(x, robust = FALSE, weights = NULL, verbose = TRUE, reference = NULL, center = NULL, ...) { args <- .process_std_center(x, weights, robust, verbose, reference, center, scale = NULL) if (is.null(args)) { # all NA? return(x) } else if (is.null(args$check)) { vals <- rep(0, length(args$vals)) # If only unique value } else { vals <- as.vector(args$vals - args$center) } centered_x <- rep(NA, length(args$valid_x)) centered_x[args$valid_x] <- vals attr(centered_x, "center") <- args$center attr(centered_x, "scale") <- args$scale attr(centered_x, "robust") <- robust centered_x } #' @export center.factor <- function(x, weights = NULL, robust = FALSE, verbose = TRUE, force = FALSE, ...) { if (!force) { return(x) } center(.factor_to_numeric(x), weights = weights, robust = robust, verbose = verbose, ...) } #' @export center.logical <- center.factor #' @export center.character <- center.factor #' @rdname center #' @inheritParams standardize.data.frame #' @export center.data.frame <- function(x, robust = FALSE, weights = NULL, verbose = TRUE, reference = NULL, select = NULL, exclude = NULL, remove_na = c("none", "selected", "all"), force = FALSE, append = FALSE, center = NULL, ...) { # process arguments args <- .process_std_args(x, select, exclude, weights, append, append_suffix = "_c", force, remove_na, reference, .center = center, .scale = NULL ) # set new values x <- args$x for (var in args$select) { x[[var]] <- center( x[[var]], robust = robust, weights = args$weights, verbose = FALSE, reference = reference[[var]], center = args$center[var], force = force ) } attr(x, "center") <- sapply(x[args$select], function(z) attributes(z)$center) attr(x, "scale") <- sapply(x[args$select], function(z) attributes(z)$scale) attr(x, "robust") <- robust x } #' @export center.grouped_df <- function(x, robust = FALSE, weights = NULL, verbose = TRUE, reference = NULL, select = NULL, exclude = NULL, remove_na = c("none", "selected", "all"), force = FALSE, append = FALSE, center = NULL, ...) { args <- .process_grouped_df(x, select, exclude, append, append_suffix = "_c", reference, weights, force ) for (rows in args$grps) { args$x[rows, ] <- center( args$x[rows, ], select = args$select, exclude = NULL, robust = robust, weights = args$weights, remove_na = remove_na, verbose = verbose, force = force, append = FALSE, center = center, ... ) } # set back class, so data frame still works with dplyr attributes(args$x) <- args$info args$x } datawizard/R/compact_character.R0000644000176200001440000000067514174263210016402 0ustar liggesusers#' Remove empty strings from character #' #' @param x A single character or a vector of characters. #' #' @return #' #' A character or a character vector with empty strings removed. #' #' @examples #' compact_character(c("x", "y", NA)) #' compact_character(c("x", "NULL", "", "y")) #' #' @export compact_character <- function(x) { x[!sapply(x, function(i) nchar(i) == 0 || all(is.na(i)) || any(i == "NULL", na.rm = TRUE))] } datawizard/R/convert_data_to_numeric.R0000644000176200001440000000357114174253064017641 0ustar liggesusers#' Convert data to numeric #' #' Convert data to numeric by converting characters to factors and factors to either numeric levels or dummy variables. #' #' @param x A data frame or a vector. #' @param dummy_factors Transform factors to dummy factors (all factor levels as different columns filled with a binary 0-1 value). #' @param ... Arguments passed to or from other methods. #' #' @examples #' head(convert_data_to_numeric(iris)) #' @return A data frame of numeric variables. #' @export convert_data_to_numeric <- function(x, ...) { UseMethod("convert_data_to_numeric") } #' @rdname convert_data_to_numeric #' @export data_to_numeric <- convert_data_to_numeric #' @rdname convert_data_to_numeric #' @export convert_data_to_numeric.data.frame <- function(x, dummy_factors = TRUE, ...) { out <- sapply(x, convert_data_to_numeric, dummy_factors = dummy_factors, simplify = FALSE) as.data.frame(do.call(cbind, out)) } #' @export convert_data_to_numeric.numeric <- function(x, ...) { as.numeric(x) } #' @export convert_data_to_numeric.double <- convert_data_to_numeric.numeric #' @export convert_data_to_numeric.logical <- convert_data_to_numeric.numeric #' @export convert_data_to_numeric.factor <- function(x, dummy_factors = TRUE, ...) { if (dummy_factors) { out <- as.data.frame(stats::model.matrix(~x, contrasts.arg = list(x = "contr.treatment"))) out[1] <- as.numeric(rowSums(out[2:ncol(out)]) == 0) names(out) <- levels(x) } else { out <- as.numeric(x) } out } #' @export convert_data_to_numeric.character <- function(x, dummy_factors = FALSE, ...) { nums <- grepl("[-]?[0-9]+[.]?[0-9]*|[-]?[0-9]+[L]?|[-]?[0-9]+[.]?[0-9]*[eE][0-9]+", x) if (all(nums)) { out <- as.numeric(nums) } else { out <- convert_data_to_numeric(as.factor(nums), dummy_factors = dummy_factors) } out } datawizard/R/data_addprefix.R0000644000176200001440000000065114113652110015663 0ustar liggesusers#' @rdname data_rename #' @examples #' # Add prefix / suffix to all columns #' head(data_addprefix(iris, "NEW_")) #' head(data_addsuffix(iris, "_OLD")) #' @export data_addprefix <- function(data, pattern, ...) { names(data) <- paste0(pattern, names(data)) data } #' @rdname data_rename #' @export data_addsuffix <- function(data, pattern, ...) { names(data) <- paste0(names(data), pattern) data } datawizard/R/data_reshape.R0000644000176200001440000001517214113652110015350 0ustar liggesusers#' Reshape (pivot) data from wide to long #' #' This function "lengthens" data, increasing the number of rows and decreasing #' the number of columns. This is a dependency-free base-R equivalent of #' `tidyr::pivot_longer()`. #' #' @param data A data frame to pivot. #' @param cols A vector of column names or indices to pivot into longer format. #' @param colnames_to The name of the new column that will contain the column #' names. #' @param values_to The name of the new column that will contain the values of #' the pivoted variables. #' @param rows_to The name of the column that will contain the row-number from #' the original data. If `NULL`, will be removed. #' @param colnames_from The name of the column that contains the levels to be #' used as future columns. #' @param values_from The name of the column that contains the values of the put #' in the columns. #' @param rows_from The name of the column that identifies the rows. If #' `NULL`, will use all the unique rows. #' @param ... Additional arguments passed on to methods. #' @param names_to,names_from Same as `colnames_to`, is there for #' compatibility with `tidyr::pivot_longer()`. #' @param sep The indicating a separating character in the variable names in the #' wide format. #' #' #' @examples #' wide_data <- data.frame(replicate(5, rnorm(10))) #' #' # From wide to long #' # ------------------ #' # Default behaviour (equivalent to tidyr::pivot_longer(wide_data, cols = 1:5)) #' data_to_long(wide_data) #' #' # Customizing the names #' data_to_long(wide_data, #' cols = c(1, 2), #' colnames_to = "Column", #' values_to = "Numbers", #' rows_to = "Row" #' ) #' #' # From long to wide #' # ----------------- #' long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number #' data_to_wide(long_data, #' colnames_from = "Name", #' values_from = "Value", #' rows_from = "Row_ID" #' ) #' #' # Full example #' # ------------------ #' if (require("psych")) { #' data <- psych::bfi # Wide format with one row per participant's personality test #' #' # Pivot long format #' long <- data_to_long(data, #' cols = "\\d", # Select all columns that contain a digit #' colnames_to = "Item", #' values_to = "Score", #' rows_to = "Participant" #' ) #' #' # Separate facet and question number #' long$Facet <- gsub("\\d", "", long$Item) #' long$Item <- gsub("[A-Z]", "", long$Item) #' long$Item <- paste0("I", long$Item) #' #' wide <- data_to_wide(long, #' colnames_from = "Item", #' values_from = "Score" #' ) #' head(wide) #' } #' @return data.frame #' @export data_to_long <- function(data, cols = "all", colnames_to = "Name", values_to = "Value", rows_to = NULL, ..., names_to = colnames_to) { if (inherits(data, "tbl_df")) { tbl_input <- TRUE data <- as.data.frame(data) } else { tbl_input <- FALSE } # Select columns ---------------- if (is.character(cols) && length(cols) == 1) { # If only one name if (cols == "all") { # If all, take all cols <- names(data) } else { # Surely, a regex cols <- grep(cols, names(data), value = TRUE) } } # If numeric, surely the index of the cols if (is.numeric(cols)) { cols <- names(data)[cols] } # Sanity checks ---------------- # Make sure all cols are in data if (!all(cols %in% names(data))) { stop("Some variables as selected by 'cols' are not present in the data.") } # Compatibility with tidyr if (names_to != colnames_to) colnames_to <- names_to # save attribute of each variable variable_attr <- lapply(data, attributes) # Reshaping --------------------- # Create Index column as needed by reshape data[["_Row"]] <- to_numeric(row.names(data)) # Reshape long <- stats::reshape(data, varying = cols, idvar = "_Row", v.names = values_to, timevar = colnames_to, direction = "long" ) # Cleaning -------------------------- # Sort the dataframe (to match pivot_longer's output) long <- long[order(long[["_Row"]], long[[colnames_to]]), ] # Remove or rename the row index if (is.null(rows_to)) { long[["_Row"]] <- NULL } else { names(long)[names(long) == "_Row"] <- rows_to } # Re-insert col names as levels long[[colnames_to]] <- cols[long[[colnames_to]]] # Reset row names row.names(long) <- NULL # Remove reshape attributes attributes(long)$reshapeLong <- NULL # add back attributes where possible for (i in colnames(long)) { attributes(long[[i]]) <- variable_attr[[i]] } if (isTRUE(tbl_input)) { class(long) <- c("tbl_df", "tbl", "data.frame") } long } #' @rdname data_to_long #' @export data_to_wide <- function(data, values_from = "Value", colnames_from = "Name", rows_from = NULL, sep = "_", ..., names_from = colnames_from) { if (inherits(data, "tbl_df")) { tbl_input <- TRUE data <- as.data.frame(data) } else { tbl_input <- FALSE } # Compatibility with tidyr if (names_from != colnames_from) colnames_from <- names_from # save attribute of each variable variable_attr <- lapply(data, attributes) # If no other row identifier, create one if (is.null(rows_from)) { if (all(names(data) %in% c(values_from, colnames_from))) { data[["_Rows"]] <- row.names(data) } data[["_Rows"]] <- apply(data[, !names(data) %in% c(values_from, colnames_from), drop = FALSE], 1, paste, collapse = "_") rows_from <- "_Rows" } # Reshape wide <- stats::reshape(data, v.names = values_from, idvar = rows_from, timevar = colnames_from, sep = sep, direction = "wide" ) # Clean if ("_Rows" %in% names(wide)) wide[["_Rows"]] <- NULL row.names(wide) <- NULL # Reset row names # Remove reshape attributes attributes(wide)$reshapeWide <- NULL # add back attributes where possible for (i in colnames(wide)) { attributes(wide[[i]]) <- variable_attr[[i]] } if (isTRUE(tbl_input)) { class(wide) <- c("tbl_df", "tbl", "data.frame") } wide } # Aliases ----------------------------------------------------------------- #' @rdname data_to_long #' @export reshape_longer <- data_to_long #' @rdname data_to_long #' @export reshape_wider <- data_to_wide datawizard/R/data_transpose.R0000644000176200001440000000154114126335276015751 0ustar liggesusers#' Transpose a dataframe #' #' Transpose a dataframe. It's the equivalent of using `t()` but restores the data.frame class, and prints a warning if the data type is modified (see example). #' #' @inheritParams data_rename #' @param verbose Silence warnings and/or messages by setting it to `FALSE`. #' #' @examples #' transposed <- data_transpose(iris) #' transposed[1:5] #' #' transposed <- data_transpose(iris[1:4]) # Only numeric = no warning #' @export data_transpose <- function(data, verbose = TRUE, ...) { if (length(unique(sapply(data, class))) > 1) { if (verbose) { warning("Your data contains mixed types of data. After transposition, all will be transformed into characters.") } } new <- as.data.frame(t(data)) # Restore names colnames(new) <- rownames(data) rownames(new) <- colnames(data) new } datawizard/R/rescale_weights.R0000644000176200001440000001732514113652110016102 0ustar liggesusers#' @title Rescale design weights for multilevel analysis #' @name rescale_weights #' #' @description Most functions to fit multilevel and mixed effects models only #' allow to specify frequency weights, but not design (i.e. sampling or #' probability) weights, which should be used when analyzing complex samples #' and survey data. `rescale_weights()` implements an algorithm proposed #' by \cite{Asparouhov (2006)} and \cite{Carle (2009)} to rescale design #' weights in survey data to account for the grouping structure of multilevel #' models, which then can be used for multilevel modelling. #' #' @param data A data frame. #' @param group Variable names (as character vector, or as formula), indicating #' the grouping structure (strata) of the survey data (level-2-cluster #' variable). It is also possible to create weights for multiple group #' variables; in such cases, each created weighting variable will be suffixed #' by the name of the group variable. #' @param probability_weights Variable indicating the probability (design or #' sampling) weights of the survey data (level-1-weight). #' @param nest Logical, if `TRUE` and `group` indicates at least two #' group variables, then groups are "nested", i.e. groups are now a #' combination from each group level of the variables in `group`. #' #' @return `data`, including the new weighting variables: `pweights_a` #' and `pweights_b`, which represent the rescaled design weights to use #' in multilevel models (use these variables for the `weights` argument). #' #' @details #' #' Rescaling is based on two methods: For `pweights_a`, the sample weights #' `probability_weights` are adjusted by a factor that represents the proportion #' of group size divided by the sum of sampling weights within each group. The #' adjustment factor for `pweights_b` is the sum of sample weights within each #' group divided by the sum of squared sample weights within each group (see #' Carle (2009), Appendix B). #' #' Regarding the choice between scaling methods A and B, Carle suggests that #' "analysts who wish to discuss point estimates should report results based on #' weighting method A. For analysts more interested in residual between-group #' variance, method B may generally provide the least biased estimates". In #' general, it is recommended to fit a non-weighted model and weighted models #' with both scaling methods and when comparing the models, see whether the #' "inferential decisions converge", to gain confidence in the results. #' #' Though the bias of scaled weights decreases with increasing group size, #' method A is preferred when insufficient or low group size is a concern. #' #' The group ID and probably PSU may be used as random effects (e.g. nested #' design, or group and PSU as varying intercepts), depending on the survey #' design that should be mimicked. #' #' @references #' - Carle A.C. (2009). Fitting multilevel models in complex survey data #' with design weights: Recommendations. BMC Medical Research Methodology #' 9(49): 1-13 #' #' - Asparouhov T. (2006). General Multi-Level Modeling with Sampling #' Weights. Communications in Statistics - Theory and Methods 35: 439-460 #' #' @examples #' if (require("lme4")) { #' data(nhanes_sample) #' head(rescale_weights(nhanes_sample, "SDMVSTRA", "WTINT2YR")) #' #' # also works with multiple group-variables #' head(rescale_weights(nhanes_sample, c("SDMVSTRA", "SDMVPSU"), "WTINT2YR")) #' #' # or nested structures. #' x <- rescale_weights( #' data = nhanes_sample, #' group = c("SDMVSTRA", "SDMVPSU"), #' probability_weights = "WTINT2YR", #' nest = TRUE #' ) #' head(x) #' #' nhanes_sample <- rescale_weights(nhanes_sample, "SDMVSTRA", "WTINT2YR") #' #' glmer( #' total ~ factor(RIAGENDR) * (log(age) + factor(RIDRETH1)) + (1 | SDMVPSU), #' family = poisson(), #' data = nhanes_sample, #' weights = pweights_a #' ) #' } #' @export rescale_weights <- function(data, group, probability_weights, nest = FALSE) { if (inherits(group, "formula")) { group <- all.vars(group) } # check if weight has missings. we need to remove them first, # and add back weights to correct cases later weight_missings <- which(is.na(data[[probability_weights]])) weight_non_na <- which(!is.na(data[[probability_weights]])) if (length(weight_missings) > 0) { data_tmp <- data[weight_non_na, ] } else { data_tmp <- data } # sort id data_tmp$.bamboozled <- 1:nrow(data_tmp) if (nest && length(group) < 2) { warning(insight::format_message(sprintf("Only one group variable selected, no nested structure possible. Rescaling weights for grout '%s' now.", group)), call. = FALSE) nest <- FALSE } if (nest) { out <- .rescale_weights_nested(data_tmp, group, probability_weights, nrow(data), weight_non_na) } else { out <- lapply(group, function(i) { x <- .rescale_weights(data_tmp, i, probability_weights, nrow(data), weight_non_na) if (length(group) > 1) { colnames(x) <- sprintf(c("pweight_a_%s", "pweight_b_%s"), i) } x }) } do.call(cbind, list(data, out)) } # rescale weights, for one or more group variables ---------------------------- .rescale_weights <- function(x, group, probability_weights, n, weight_non_na) { # compute sum of weights per group design_weights <- .data_frame( group = sort(unique(x[[group]])), sum_weights_by_group = tapply(x[[probability_weights]], as.factor(x[[group]]), sum), sum_squared_weights_by_group = tapply(x[[probability_weights]]^2, as.factor(x[[group]]), sum), n_per_group = as.vector(table(x[[group]])) ) colnames(design_weights)[1] <- group x <- merge(x, design_weights, by = group, sort = FALSE) # restore original order x <- x[order(x$.bamboozled), ] x$.bamboozled <- NULL # multiply the original weight by the fraction of the # sampling unit total population based on Carle 2009 w_a <- x[[probability_weights]] * x$n_per_group / x$sum_weights_by_group w_b <- x[[probability_weights]] * x$sum_weights_by_group / x$sum_squared_weights_by_group out <- data.frame( pweights_a = rep(as.numeric(NA), times = n), pweights_b = rep(as.numeric(NA), times = n) ) out$pweights_a[weight_non_na] <- w_a out$pweights_b[weight_non_na] <- w_b out } # rescale weights, for nested groups ---------------------------- .rescale_weights_nested <- function(x, group, probability_weights, n, weight_non_na) { groups <- expand.grid(lapply(group, function(i) sort(unique(x[[i]])))) colnames(groups) <- group # compute sum of weights per group design_weights <- cbind( groups, .data_frame( sum_weights_by_group = unlist(as.list(tapply(x[[probability_weights]], lapply(group, function(i) as.factor(x[[i]])), sum))), sum_squared_weights_by_group = unlist(as.list(tapply(x[[probability_weights]]^2, lapply(group, function(i) as.factor(x[[i]])), sum))), n_per_group = unlist(as.list(table(x[, group]))) ) ) x <- merge(x, design_weights, by = group, sort = FALSE) # restore original order x <- x[order(x$.bamboozled), ] x$.bamboozled <- NULL # multiply the original weight by the fraction of the # sampling unit total population based on Carle 2009 w_a <- x[[probability_weights]] * x$n_per_group / x$sum_weights_by_group w_b <- x[[probability_weights]] * x$sum_weights_by_group / x$sum_squared_weights_by_group out <- data.frame( pweights_a = rep(as.numeric(NA), times = n), pweights_b = rep(as.numeric(NA), times = n) ) out$pweights_a[weight_non_na] <- w_a out$pweights_b[weight_non_na] <- w_b out } datawizard/R/data_partition.R0000644000176200001440000000443514113652110015732 0ustar liggesusers#' Partition data into a test and a training set #' #' Creates a training and a test set based on a dataframe. Can also be #' stratified (i.e., evenly spread a given factor) using the `group` #' argument. #' #' @inheritParams data_rename #' @param training_proportion The proportion (between 0 and 1) of the training #' set. The remaining part will be used for the test set. #' @param group A character vector indicating the name(s) of the column(s) used #' for stratified partitioning. #' @param seed A random number generator seed. Enter an integer (e.g. 123) so #' that the random sampling will be the same each time you run the function. #' #' @return A list of two data frames, named `test` and `training`. #' #' @examples #' df <- iris #' df$Smell <- rep(c("Strong", "Light"), 75) #' #' data_partition(df) #' data_partition(df, group = "Species") #' data_partition(df, group = c("Species", "Smell")) #' @export data_partition <- function(data, training_proportion = 0.7, group = NULL, seed = NULL, ...) { if (!is.data.frame(data)) { data <- tryCatch( expr = { as.data.frame(data) }, error = function(e) { NULL } ) if (is.null(data)) { stop("`data` needs to be a data frame, or an object that can be coerced to a data frame.") } } if (!is.null(seed)) { set.seed(seed) } training <- data.frame() test <- data.frame() if (!is.null(group)) { for (i in split(data, data[group])) { out <- .data_partition(i, training_proportion) training <- rbind(training, i[out$training, ]) test <- rbind(test, i[out$test, ]) } } else { out <- .data_partition(data, training_proportion) training <- rbind(training, data[out$training, ]) test <- rbind(test, data[out$test, ]) } list( training = training, test = test ) } #' @keywords internal .data_partition <- function(data, training_proportion = 0.8) { training_indices <- sample(1:nrow(data), size = training_proportion * nrow(data)) test_indices <- (1:nrow(data))[-training_indices] list( training = training_indices, test = test_indices ) } datawizard/R/data_rescale.R0000644000176200001440000001127514173025777015362 0ustar liggesusers#' Rescale Variables to a New Range #' #' Rescale variables to a new range. #' #' @inheritParams standardize.data.frame #' #' @param x A numeric variable. #' @param to New range that the variable will have after rescaling. #' @param range Initial (old) range of values. If `NULL`, will take the range of #' the input vector (`range(x)`). #' @param ... Arguments passed to or from other methods. #' #' @examples #' data_rescale(c(0, 1, 5, -5, -2)) #' data_rescale(c(0, 1, 5, -5, -2), to = c(-5, 5)) #' #' # Specify the "theoretical" range of the input vector #' data_rescale(c(1, 3, 4), to = c(0, 40), range = c(0, 4)) #' #' # Dataframes #' head(data_rescale(iris, to = c(0, 1))) #' head(data_rescale(iris, to = c(0, 1), select = "Sepal.Length")) #' #' # One can specify a list of ranges #' head(data_rescale(iris, to = list( #' "Sepal.Length" = c(0, 1), #' "Petal.Length" = c(-1, 0) #' ))) #' @seealso [normalize()] [standardize()] [ranktransform()] #' #' @return A rescaled object. #' #' @family transform utilities #' #' @export data_rescale <- function(x, ...) { UseMethod("data_rescale") } #' @rdname data_rescale #' @export change_scale <- function(x, ...) { # TODO: Don't deprecate for now # so we have time to change it accross the verse, but for next round # .Deprecated("data_rescale") data_rescale(x, ...) } #' @rdname data_rescale #' @export data_rescale.numeric <- function(x, to = c(0, 100), range = NULL, verbose = TRUE, ...) { if (is.null(to)) { return(x) } # Warning if all NaNs if (all(is.na(x))) { return(x) } # Warning if only one value if (length(unique(x)) == 1 && is.null(range)) { if (verbose) { warning(paste0("A `range` must be provided for data with only one observation.")) } return(x) } if (is.null(range)) { range <- c(min(x, na.rm = TRUE), max(x, na.rm = TRUE)) } min <- ifelse(is.na(range[1]), min(x, na.rm = TRUE), range[1]) max <- ifelse(is.na(range[2]), max(x, na.rm = TRUE), range[2]) new_min <- ifelse(is.na(to[1]), min, to[1]) new_max <- ifelse(is.na(to[2]), max, to[2]) out <- as.vector((new_max - new_min) / (max - min) * (x - min) + new_min) out } #' @export data_rescale.factor <- function(x, ...) { x } #' @rdname data_rescale #' @export data_rescale.grouped_df <- function(x, to = c(0, 100), range = NULL, select = NULL, exclude = NULL, ...) { info <- attributes(x) # dplyr >= 0.8.0 returns attribute "indices" grps <- attr(x, "groups", exact = TRUE) # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } # dplyr < 0.8.0? if (is.null(grps)) { grps <- attr(x, "indices", exact = TRUE) grps <- lapply(grps, function(x) x + 1) } else { grps <- grps[[".rows"]] } x <- as.data.frame(x) for (rows in grps) { x[rows, ] <- change_scale( x[rows, ], select = select, exclude = exclude, to = to, range = range, ... ) } # set back class, so data frame still works with dplyr attributes(x) <- info x } #' @rdname data_rescale #' @export data_rescale.data.frame <- function(x, to = c(0, 100), range = NULL, select = NULL, exclude = NULL, ...) { # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } if (is.null(select)) { select <- names(x) } if (!is.null(exclude)) { select <- setdiff(select, exclude) } # Transform the range so that it is a list now if (!is.null(range)) { if (!is.list(range)) { range <- stats::setNames(rep(list(range), length(select)), select) } } # Transform the 'to' so that it is a list now if (!is.list(to)) { to <- stats::setNames(rep(list(to), length(select)), select) } x[select] <- as.data.frame(sapply(select, function(n) { data_rescale(x[[n]], to = to[[n]], range = range[[n]]) }, simplify = FALSE)) x } datawizard/R/smoothness.R0000644000176200001440000000665714113652110015142 0ustar liggesusers#' Quantify the smoothness of a vector #' #' @param x Numeric vector (similar to a time series). #' @param method Can be "diff" (the standard deviation of the standardized #' differences) or "cor" (default, lag-one autocorrelation). #' @param lag An integer indicating which lag to use. If less than 1, will be #' interpreted as expressed in percentage of the length of the vector. #' @inheritParams skewness #' #' @examples #' x <- (-10:10)^3 + rnorm(21, 0, 100) #' plot(x) #' smoothness(x, method = "cor") #' smoothness(x, method = "diff") #' @return Value of smoothness. #' @references https://stats.stackexchange.com/questions/24607/how-to-measure-smoothness-of-a-time-series-in-r #' #' @export smoothness <- function(x, method = "cor", lag = 1, iterations = NULL, ...) { UseMethod("smoothness") } #' @export smoothness.numeric <- function(x, method = "cor", lag = 1, iterations = NULL, ...) { if (lag < 1) { lag <- round(lag * length(x)) } if (lag <= 0) { stop("lag cannot be that small.") } if (method == "cor") { smooth <- stats::cor(utils::head(x, length(x) - lag), utils::tail(x, length(x) - lag)) } else { smooth <- stats::sd(diff(x, lag = lag)) / abs(mean(diff(x, lag = lag))) } if (!is.null(iterations)) { if (!requireNamespace("boot", quietly = TRUE)) { warning("Package 'boot' needed for bootstrapping SEs.", call. = FALSE) } else { results <- boot::boot( data = x, statistic = .boot_smoothness, R = iterations, method = method, lag = lag ) out_se <- stats::sd(results$t, na.rm = TRUE) smooth <- data.frame(Smoothness = smooth, SE = out_se) } } class(smooth) <- unique(c("parameters_smoothness", class(smooth))) smooth } #' @export smoothness.data.frame <- function(x, method = "cor", lag = 1, iterations = NULL, ...) { .smoothness <- lapply( x, smoothness, method = method, lag = lag, iterations = iterations ) .smoothness <- cbind(Parameter = names(.smoothness), do.call(rbind, .smoothness)) class(.smoothness) <- unique(c("parameters_smoothness", class(.smoothness))) .smoothness } #' @export smoothness.default <- function(x, method = "cor", lag = 1, iterations = NULL, ...) { smoothness( .factor_to_numeric(x), method = method, lag = lag, iterations = iterations ) } # bootstrapping ----------------------------------- .boot_smoothness <- function(data, indices, method, lag) { datawizard::smoothness( x = data[indices], method = method, lag = lag, iterations = NULL ) } # methods ----------------------------------------- #' @export as.numeric.parameters_smoothness <- function(x, ...) { if (is.data.frame(x)) { x$Smoothness } else { as.vector(x) } } #' @export as.double.parameters_smoothness <- as.numeric.parameters_smoothness datawizard/R/data.R0000644000176200001440000000064014113652110013633 0ustar liggesusers#' @docType data #' @title Sample dataset from the National Health and Nutrition Examination Survey #' @name nhanes_sample #' @keywords data #' #' @description Selected variables from the National Health and Nutrition Examination #' Survey that are used in the example from Lumley (2010), Appendix E. #' #' @references Lumley T (2010). Complex Surveys: a guide to analysis using R. Wiley NULL datawizard/R/format_text.R0000644000176200001440000000557114113652110015266 0ustar liggesusers#' Convenient text formatting functionalities #' #' Convenience functions to manipulate and format text. #' #' @param text,text2 A character string. #' @param width Positive integer giving the target column width for wrapping #' lines in the output. Can be "auto", in which case it will select 90\% of the #' default width. #' @inheritParams data_rename #' @param sep Separator. #' @param last Last separator. #' @param n The number of characters to find. #' @param ... Other arguments to be passed to or from other functions. #' #' @return A character string. #' #' @examples #' # Add full stop if missing #' text_fullstop(c("something", "something else.")) #' #' # Find last characters #' text_lastchar(c("ABC", "DEF"), n = 2) #' #' # Smart concatenation #' text_concatenate(c("First", "Second", "Last")) #' #' # Remove parts of string #' text_remove(c("one!", "two", "three!"), "!") #' #' # Wrap text #' long_text <- paste(rep("abc ", 100), collapse = "") #' cat(text_wrap(long_text, width = 50)) #' #' # Paste with optional separator #' text_paste(c("A", "", "B"), c("42", "42", "42")) #' @export format_text <- function(text, sep = ", ", last = " and ", width = NULL, ...) { text_wrap(text_concatenate(text, sep = sep, last = last), width = width) } #' @rdname format_text #' @export text_fullstop <- function(text) { text[!text_lastchar(text) %in% c(".", ":", ",", ";", "!", "?")] <- paste0(text[text_lastchar(text) != "."], ".") text } #' @rdname format_text #' @export text_lastchar <- function(text, n = 1) { sapply(text, function(xx) { substr(xx, (nchar(xx) - n + 1), nchar(xx)) }) } #' @rdname format_text #' @export text_concatenate <- function(text, sep = ", ", last = " and ") { text <- text[text != ""] if (length(text) == 1) { text } else { s <- paste0(utils::head(text, -1), collapse = sep) s <- paste0(c(s, utils::tail(text, 1)), collapse = last) s } } #' @rdname format_text #' @export text_paste <- function(text, text2 = NULL, sep = ", ", ...) { if (!is.null(text2)) { paste0(text, ifelse(text == "" | text2 == "", "", sep), text2) } } #' @rdname format_text #' @export text_remove <- function(text, pattern = "", ...) { gsub(pattern, "", text, ...) } #' @rdname format_text #' @export text_wrap <- function(text, width = NULL, ...) { if (is.null(width)) { return(text) } text <- strsplit(text, "\n", perl = TRUE) text <- unlist(text) if (width == "auto") { width <- 0.9 * getOption("width") } wrapped <- "" for (s in text) { if (nchar(s) > width) { leading_spaces <- nchar(s) - nchar(trimws(s)) s <- strwrap(s, width = width) s <- paste0(s, collapse = "\n") s <- paste0(paste0(rep(" ", leading_spaces), collapse = ""), s) } wrapped <- paste0(wrapped, s, "\n") } wrapped } datawizard/R/utils_strings.R0000644000176200001440000000077514174253064015660 0ustar liggesusersstring_starts_with <- function(pattern, x) { pattern <- paste0("^\\Q", pattern, "\\E") grep(pattern, x, perl = TRUE) } string_contains <- function(pattern, x) { pattern <- paste0("\\Q", pattern, "\\E") grep(pattern, x, perl = TRUE) } string_ends_with <- function(pattern, x) { pattern <- paste0("\\Q", pattern, "\\E$") grep(pattern, x, perl = TRUE) } string_one_of <- function(pattern, x) { m <- unlist(sapply(pattern, grep, x = x, fixed = TRUE, useBytes = TRUE)) x[m] } datawizard/R/normalize.R0000644000176200001440000001123614141236076014737 0ustar liggesusers#' Normalize numeric variable to 0-1 range #' #' Performs a normalization of data, i.e., it scales variables in the range 0 - #' 1. This is a special case of [data_rescale()]. #' #' @inheritParams standardize.data.frame #' #' @param x A numeric vector, data frame, or matrix. See details. #' @param include_bounds Logical, if `TRUE`, return value may include 0 and 1. #' If `FALSE`, the return value is compressed, using Smithson and Verkuilen's #' (2006) formula `(x * (n - 1) + 0.5) / n`, to avoid zeros and ones in the #' normalized variables. This can be useful in case of beta-regression, where #' the response variable is not allowed to include zeros and ones. #' @param ... Arguments passed to or from other methods. #' #' @details #' - If `x` is a matrix, normalization is performed across all values (not #' column- or row-wise). For column-wise normalization, convert the matrix to a #' data.frame. #' - If `x` is a grouped data frame (`grouped_df`), normalization is performed #' separately for each group. #' #' @examples #' normalize(c(0, 1, 5, -5, -2)) #' normalize(c(0, 1, 5, -5, -2), include_bounds = FALSE) #' #' head(normalize(trees)) #' @references #' Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood #' Regression with Beta-Distributed Dependent Variables. Psychological Methods, #' 11(1), 54–71. #' #' @family transform utilities #' #' @return A normalized object. #' #' @export normalize <- function(x, ...) { UseMethod("normalize") } #' @rdname normalize #' @export normalize.numeric <- function(x, include_bounds = TRUE, verbose = TRUE, ...) { # Warning if all NaNs if (all(is.na(x))) { return(x) } # Warning if only one value if (length(unique(x)) == 1) { if (is.null(names(x))) { name <- deparse(substitute(x)) } else { name <- names(x) } if (verbose) { warning(paste0("Variable `", name, "` contains only one unique value and will not be normalized.")) } return(x) } # Warning if logical vector if (length(unique(x)) == 2) { if (is.null(names(x))) { name <- deparse(substitute(x)) } else { name <- names(x) } if (verbose) { warning(paste0("Variable `", name, "` contains only two different values. Consider converting it to a factor.")) } } out <- as.vector((x - min(x, na.rm = TRUE)) / diff(range(x, na.rm = TRUE), na.rm = TRUE)) if (!include_bounds && (any(out == 0) | any(out == 1))) { out <- (out * (length(out) - 1) + 0.5) / length(out) } out } #' @export normalize.factor <- function(x, ...) { x } #' @rdname normalize #' @export normalize.grouped_df <- function(x, select = NULL, exclude = NULL, include_bounds = TRUE, verbose = TRUE, ...) { info <- attributes(x) # dplyr >= 0.8.0 returns attribute "indices" grps <- attr(x, "groups", exact = TRUE) # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } # dplyr < 0.8.0? if (is.null(grps)) { grps <- attr(x, "indices", exact = TRUE) grps <- lapply(grps, function(x) x + 1) } else { grps <- grps[[".rows"]] } x <- as.data.frame(x) for (rows in grps) { x[rows, ] <- normalize( x[rows, ], select = select, exclude = exclude, include_bounds = include_bounds, verbose = verbose, ... ) } # set back class, so data frame still works with dplyr attributes(x) <- info x } #' @rdname normalize #' @export normalize.data.frame <- function(x, select = NULL, exclude = NULL, include_bounds = TRUE, verbose = TRUE, ...) { # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } if (is.null(select)) { select <- names(x) } if (!is.null(exclude)) { select <- setdiff(select, exclude) } x[select] <- lapply(x[select], normalize, include_bounds = include_bounds, verbose = verbose) x } #' @export normalize.matrix <- function(x, ...) { matrix(normalize(as.numeric(x), ...), nrow = nrow(x)) } datawizard/R/visualisation_recipe.R0000644000176200001440000000465214126335276017171 0ustar liggesusers#' Prepare objects for visualisation #' #' This function prepares objects for visualisation by returning a list of #' layers with data and geoms that can be easily plotted using for instance #' `ggplot2`. See the documentation for your object's class: #' \itemize{ #' \item{[modelbased][modelbased::visualisation_recipe.estimate_predicted] (`estimate_means`, `estimate_contrasts`, `estimate_slopes`, `estimate_predicted`, `estimate_grouplevel`)} #' } #' #' @param x An `easystats` object. #' @param ... Other arguments passed to other functions. #' #' @export visualisation_recipe <- function(x, ...) { UseMethod("visualisation_recipe") } #' @export print.visualisation_recipe <- function(x, ...) { for (i in 1:length(x)) { l <- x[[paste0("l", i)]] insight::print_color(paste0("Layer ", i, "\n--------\n"), "blue") insight::print_color(paste0("Geom type: ", ifelse(is.null(l$geom), "[NULL]", l$geom), "\n"), "yellow") elements <- names(l)[!sapply(l, is.null)] # Loop through all elements of list for (element in elements[elements != "geom"]) { # Print element name if (element == "aes") { cat("aes_string(\n") } else { cat(paste0(element, " = ")) } # Print element if (element == "data") { cat(paste0("[", paste0(dim(l$data), collapse = " x "), "]")) } else if (element == "aes") { for (aes in names(l$aes)) { if (!is.null(l$aes[[aes]])) { if (is.character(l$aes[[aes]])) { cat(paste0(" ", aes, " = '", l$aes[[aes]], "'\n")) } else { cat(paste0(" ", aes, " = ", l$aes[[aes]], "\n")) } } } cat(")") } else { if (is.character(l[[element]]) || is.numeric(l[[element]]) || is.factor(l[[element]])) { if (is.character(l[[element]])) { cat(paste0("'", l[[element]], "'")) } else { if (length(l[[element]]) == 1) { cat(l[[element]]) } else { cat(paste0("c(", paste0(l[[element]], collapse = ", "), ")")) } } } else { cat(paste0("class: ", class(l[[element]]), collapse = "/")) } } cat("\n") } cat("\n") } } #' @export plot.visualisation_recipe <- function(x, ...) { insight::check_if_installed("see") NextMethod() } datawizard/R/data_restoretype.R0000644000176200001440000000350714113652110016305 0ustar liggesusers#' Restore the type of columns according to a reference data frame #' #' @inheritParams data_to_long #' @inheritParams data_rename #' @param reference A reference data frame from which to find the correct #' column types. #' #' @return #' #' A dataframe with columns whose types have been restored based on the #' reference dataframe. #' #' @examples #' data <- data.frame( #' Sepal.Length = c("1", "3", "2"), #' Species = c("setosa", "versicolor", "setosa"), #' New = c("1", "3", "4") #' ) #' #' fixed <- data_restoretype(data, reference = iris) #' summary(fixed) #' @export data_restoretype <- function(data, reference = NULL, ...) { for (col in names(data)) { # No reference data (regular fixing) ---------------- if (is.null(reference)) { if (is.character(data[[col]])) { data[[col]] <- to_numeric(data[[col]]) } } else { if (is.factor(reference[[col]]) && !is.factor(data[[col]])) { # Restore factor levels data[[col]] <- factor(data[[col]], levels = levels(reference[[col]])) } if (is.numeric(reference[[col]]) && !is.numeric(data[[col]])) { data[[col]] <- to_numeric(as.character(data[[col]])) } if (is.character(reference[[col]]) && !is.character(data[[col]])) { data[[col]] <- as.character(data[[col]]) } } } data } #' Convert to Numeric (if possible) #' #' Tries to convert vector to numeric if possible (if no warnings or errors). #' Otherwise, leaves it as is. #' #' @param x A vector to be converted. #' #' @examples #' to_numeric(c("1", "2")) #' to_numeric(c("1", "2", "A")) #' @return Numeric vector (if possible) #' @export to_numeric <- function(x) { tryCatch(as.numeric(as.character(x)), error = function(e) x, warning = function(w) x ) } datawizard/R/adjust.R0000644000176200001440000001671114113652110014222 0ustar liggesusers#' Adjust data for the effect of other variable(s) #' #' This function can be used to adjust the data for the effect of other #' variables present in the dataset. It is based on an underlying fitting of #' regressions models, allowing for quite some flexibility, such as including #' factors as random effects in mixed models (multilevel partialization), #' continuous variables as smooth terms in general additive models (non-linear #' partialization) and/or fitting these models under a Bayesian framework. The #' values returned by this function are the residuals of the regression models. #' Note that a regular correlation between two "adjusted" variables is #' equivalent to the partial correlation between them. #' #' @param data A dataframe. #' @param effect Character vector of column names to be adjusted for (regressed #' out). If `NULL` (the default), all variables will be selected. #' @inheritParams standardize #' @param multilevel If `TRUE`, the factors are included as random factors. #' Else, if `FALSE` (default), they are included as fixed effects in the #' simple regression model. #' @param additive If `TRUE`, continuous variables as included as smooth terms #' in additive models. The goal is to regress-out potential non-linear #' effects. #' @param bayesian If `TRUE`, the models are fitted under the Bayesian framework #' using `rstanarm`. #' @param keep_intercept If `FALSE` (default), the intercept of the model is #' re-added. This avoids the centering around 0 that happens by default #' when regressing out another variable (see the examples below for a #' visual representation of this). #' #' @return A data frame comparable to `data`, with adjusted variables. #' #' @examples #' adjusted_all <- adjust(attitude) #' head(adjusted_all) #' adjusted_one <- adjust(attitude, effect = "complaints", select = "rating") #' head(adjusted_one) #' \donttest{ #' adjust(attitude, effect = "complaints", select = "rating", bayesian = TRUE) #' adjust(attitude, effect = "complaints", select = "rating", additive = TRUE) #' attitude$complaints_LMH <- cut(attitude$complaints, 3) #' adjust(attitude, effect = "complaints_LMH", select = "rating", multilevel = TRUE) #' } #' #' if (require("MASS") && require("bayestestR")) { #' # Generate data #' data <- simulate_correlation(n = 100, r = 0.7) #' data$V2 <- (5 * data$V2) + 20 # Add intercept #' #' # Adjust #' adjusted <- adjust(data, effect = "V1", select = "V2") #' adjusted_icpt <- adjust(data, effect = "V1", select = "V2", keep_intercept = TRUE) #' #' # Visualize #' plot(data$V1, data$V2, #' pch = 19, col = "blue", #' ylim = c(min(adjusted$V2), max(data$V2)), #' main = "Original (blue), adjusted (green), and adjusted - intercept kept (red) data" #' ) #' abline(lm(V2 ~ V1, data = data), col = "blue") #' points(adjusted$V1, adjusted$V2, pch = 19, col = "green") #' abline(lm(V2 ~ V1, data = adjusted), col = "green") #' points(adjusted_icpt$V1, adjusted_icpt$V2, pch = 19, col = "red") #' abline(lm(V2 ~ V1, data = adjusted_icpt), col = "red") #' } #' @export adjust <- function(data, effect = NULL, select = NULL, exclude = NULL, multilevel = FALSE, additive = FALSE, bayesian = FALSE, keep_intercept = FALSE) { if (!all(colnames(data) == make.names(colnames(data), unique = TRUE))) { warning("Bad column names (e.g., with spaces) have been detected which might create issues in many functions.\n", "Please fix it (you can run `names(mydata) <- make.names(names(mydata))` for a quick fix).", call. = FALSE ) } # check for formula notation, convert to character vector if (inherits(effect, "formula")) { effect <- all.vars(effect) } if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } # Find predictors if (is.null(effect)) { effect <- names(data) } # Factors formula_random <- NULL facs <- names(data[effect][!sapply(data[effect], is.numeric)]) if (length(facs) >= 1) { if (multilevel) { if (additive) { formula_random <- stats::as.formula(paste("~", paste(paste0("(1|", facs, ")"), collapse = " + "))) } else { formula_random <- paste("+", paste(paste0("(1|", facs, ")"), collapse = " + ")) } effect <- effect[!effect %in% facs] } } nums <- sapply(data, is.numeric) # Find outcomes if (is.null(select)) { select <- names(data[nums]) } if (!is.null(exclude)) { select <- select[!select %in% c(exclude)] } # Fit models out <- data.frame(.ID = 1:nrow(data)) for (var in select) { predictors <- effect[effect != var] if (additive) { predictors_num <- names(data[predictors][sapply(data[predictors], is.numeric)]) predictors[predictors == predictors_num] <- paste0("s(", predictors_num, ")") } formula_predictors <- paste(c("1", predictors), collapse = " + ") formula <- paste(var, "~", formula_predictors) x <- .model_adjust_for( data = data[unique(c(var, effect, facs))], formula, multilevel = multilevel, additive = additive, bayesian = bayesian, formula_random = formula_random, keep_intercept = keep_intercept ) out[var] <- x } out[names(data)[!names(data) %in% names(out)]] <- data[names(data)[!names(data) %in% names(out)]] out[names(data)] } #' @rdname adjust #' @export data_adjust <- adjust #' @keywords internal .model_adjust_for <- function(data, formula, multilevel = FALSE, additive = FALSE, bayesian = FALSE, formula_random = NULL, keep_intercept = FALSE) { # Additive ----------------------- if (additive) { # Bayesian if (bayesian) { insight::check_if_installed("rstanarm") model <- rstanarm::stan_gamm4(stats::as.formula(formula), random = formula_random, data = data, refresh = 0) # Frequentist } else { insight::check_if_installed("gamm4") model <- gamm4::gamm4(stats::as.formula(formula), random = formula_random, data = data) } # Linear ------------------------- } else { # Bayesian if (bayesian) { insight::check_if_installed("rstanarm") if (multilevel) { model <- rstanarm::stan_lmer(paste(formula, formula_random), data = data, refresh = 0) } else { model <- rstanarm::stan_glm(formula, data = data, refresh = 0) } # Frequentist } else { if (multilevel) { insight::check_if_installed("lme4") model <- lme4::lmer(paste(formula, formula_random), data = data) } else { model <- stats::lm(formula, data = data) } } } adjusted <- insight::get_residuals(model) # Re-add intercept if need be if (keep_intercept) { intercept <- insight::get_intercept(model) if (length(intercept) > 1) intercept <- stats::median(intercept) # For bayesian model if (is.na(intercept)) intercept <- 0 adjusted <- adjusted + intercept } # Deal with missing data out <- rep(NA, nrow(data)) out[stats::complete.cases(data)] <- as.vector(adjusted) out } datawizard/R/data_match.R0000644000176200001440000000160414113652110015010 0ustar liggesusers#' Find row indices of a data frame matching a specific condition #' #' Find row indices of a data frame that match a specific condition. #' #' @param x A data frame. #' @param to A data frame matching the specified conditions. #' @inheritParams data_rename #' #' @return #' #' A dataframe containing rows that match the specified configuration. #' #' @examples #' matching_rows <- data_match(mtcars, data.frame(vs = 0, am = 1)) #' mtcars[matching_rows, ] #' #' matching_rows <- data_match(mtcars, data.frame(vs = 0, am = c(0, 1))) #' mtcars[matching_rows, ] #' @export data_match <- function(x, to, ...) { # Input checks if (!is.data.frame(to)) to <- as.data.frame(to) # Find matching rows idx <- 1:nrow(x) for (col in names(to)) { if (col %in% names(x)) { idx <- idx[x[[col]][idx] %in% to[[col]]] } } to_numeric(row.names(x)[idx]) } datawizard/R/unstandardize.R0000644000176200001440000001257614174253064015624 0ustar liggesusers#' @rdname standardize #' @export unstandardize <- function(x, ...) { UseMethod("unstandardize") } #' @rdname standardize #' @export unstandardise <- unstandardize #' @rdname standardize #' @export unstandardize.numeric <- function(x, center = NULL, scale = NULL, reference = NULL, robust = FALSE, two_sd = FALSE, ...) { if (!is.null(reference)) { if (robust) { center <- stats::median(reference, na.rm = TRUE) scale <- stats::mad(reference, na.rm = TRUE) } else { center <- mean(reference, na.rm = TRUE) scale <- stats::sd(reference, na.rm = TRUE) } } else if (is.null(center) || is.null(scale)) { if (all(c("center", "scale") %in% names(attributes(x)))) { center <- attr(x, "center", exact = TRUE) scale <- attr(x, "scale", exact = TRUE) attr(x, "scale") <- attr(x, "center") <- NULL } else if (all(c("scaled:center", "scaled:scale") %in% names(attributes(x)))) { center <- attr(x, "scaled:center", exact = TRUE) scale <- attr(x, "scaled:scale", exact = TRUE) attr(x, "scaled:scale") <- attr(x, "scaled:center") <- NULL } else { stop("You must provide the arguments `center`, `scale` or `reference`.") } } if (two_sd) { scale <- 2 * scale } x * scale + center } #' @rdname standardize #' @export unstandardize.data.frame <- function(x, center = NULL, scale = NULL, reference = NULL, robust = FALSE, two_sd = FALSE, select = NULL, exclude = NULL, ...) { if (!is.null(reference)) { i <- sapply(x, is.numeric) i <- i[i] reference <- reference[names(i)] if (robust) { center <- sapply(reference, stats::median, na.rm = TRUE) scale <- sapply(reference, stats::mad, na.rm = TRUE) } else { center <- sapply(reference, mean, na.rm = TRUE) scale <- sapply(reference, stats::sd, na.rm = TRUE) } } else if (is.null(center) || is.null(scale)) { i <- sapply(x, function(k) { is.numeric(k) && !is.null(a <- attributes(k)) && all(c("scale", "center") %in% names(a)) }) if (any(i)) { i <- i[i] center <- sapply(x[names(i)], attr, "center", exact = TRUE) scale <- sapply(x[names(i)], attr, "scale", exact = TRUE) } else if (all(c("center", "scale") %in% names(attributes(x)))) { center <- attr(x, "center", exact = TRUE) scale <- attr(x, "scale", exact = TRUE) attr(x, "center") <- attr(x, "scale") <- NULL i <- names(x) %in% names(scale) i <- i[i] } else { stop("You must provide the arguments `center`, `scale` or `reference`.") } } else { if (is.null(names(center))) { i <- sapply(x, is.numeric) names(center) <- names(scale) <- names(x[i]) } i <- names(x) %in% names(center) names(i) <- names(x) i <- i[i] } if (two_sd) { scale <- 2 * scale } # Select and deselect cols <- names(i) if (!is.null(select)) cols <- cols[cols %in% select] if (!is.null(exclude)) cols <- cols[!cols %in% exclude] # Apply unstandardization to cols for (col in cols) { x[col] <- unstandardize(x[[col]], center = center[[col]], scale = scale[[col]]) } x } #' @export unstandardize.factor <- function(x, ...) { x } #' @export unstandardize.character <- function(x, ...) { x } #' @export unstandardize.grouped_df <- function(x, center = NULL, scale = NULL, reference = NULL, robust = FALSE, two_sd = FALSE, ...) { stop("Cannot (yet) unstandardize a grouped_df.") } #' @export unstandardize.matrix <- function(x, center = NULL, scale = NULL, reference = NULL, robust = FALSE, two_sd = FALSE, ...) { if (all(c("scaled:center", "scaled:scale") %in% names(attributes(x)))) { center <- attr(x, "scaled:center", exact = TRUE) scale <- attr(x, "scaled:scale", exact = TRUE) attr(x, "scaled:center") <- attr(x, "scaled:scale") <- NULL for (col in seq_len(ncol(x))) { x[, col] <- unstandardize.numeric(x[, col], center = center[col], scale = scale[col]) } } else { scales <- attr(x, "scale") centers <- attr(x, "center") xl <- lapply(seq_len(ncol(x)), function(i) { tmp <- x[, i] attributes(tmp) <- list(center = centers[i], scale = scales[i]) tmp }) xz <- lapply(xl, datawizard::unstandardize, ...) x_out <- do.call(cbind, xz) dimnames(x_out) <- dimnames(x) x <- x_out } x } #' @export unstandardize.array <- unstandardize.matrix datawizard/R/utils_data.R0000644000176200001440000000055214174263210015063 0ustar liggesusers#' help-functions #' @keywords internal #' @noRd .data_frame <- function(...) { x <- data.frame(..., stringsAsFactors = FALSE) rownames(x) <- NULL x } rownames_as_column <- function(x, var = "rowname") { rn <- data.frame(rn = rownames(x), stringsAsFactors = FALSE) x <- cbind(rn, x) colnames(x)[1] <- var rownames(x) <- NULL x } datawizard/R/skewness_kurtosis.R0000644000176200001440000003111214113652110016525 0ustar liggesusers#' Compute Skewness and (Excess) Kurtosis #' #' @param x A numeric vector or data.frame. #' @param na.rm Remove missing values. #' @param type Type of algorithm for computing skewness. May be one of `1` #' (or `"1"`, `"I"` or `"classic"`), `2` (or `"2"`, #' `"II"` or `"SPSS"` or `"SAS"`) or `3` (or `"3"`, #' `"III"` or `"Minitab"`). See 'Details'. #' @param iterations The number of bootstrap replicates for computing standard #' errors. If `NULL` (default), parametric standard errors are computed. #' @param test Logical, if `TRUE`, tests if skewness or kurtosis is #' significantly different from zero. #' @param digits Number of decimal places. #' @param object An object returned by `skewness()` or `kurtosis()`. #' @param verbose Toggle warnings and messages. #' @param ... Arguments passed to or from other methods. #' #' @details #' #' \subsection{Skewness}{ #' Symmetric distributions have a `skewness` around zero, while #' a negative skewness values indicates a "left-skewed" distribution, and a #' positive skewness values indicates a "right-skewed" distribution. Examples #' for the relationship of skewness and distributions are: #' \itemize{ #' \item Normal distribution (and other symmetric distribution) has a skewness #' of 0 #' \item Half-normal distribution has a skewness just below 1 #' \item Exponential distribution has a skewness of 2 #' \item Lognormal distribution can have a skewness of any positive value, #' depending on its parameters #' } #' (\cite{https://en.wikipedia.org/wiki/Skewness}) #' } #' \subsection{Types of Skewness}{ #' `skewness()` supports three different methods for estimating skewness, #' as discussed in \cite{Joanes and Gill (1988)}: #' \itemize{ #' \item Type "1" is the "classical" method, which is `g1 = (sum((x - #' mean(x))^3) / n) / (sum((x - mean(x))^2) / n)^1.5` #' #' \item Type "2" first calculates the type-1 skewness, then adjusts the result: #' `G1 = g1 * sqrt(n * (n - 1)) / (n - 2)`. This is what SAS and SPSS #' usually return #' #' \item Type "3" first calculates the type-1 skewness, then adjusts the result: #' `b1 = g1 * ((1 - 1 / n))^1.5`. This is what Minitab usually returns. #' } #' } #' \subsection{Kurtosis}{ #' The `kurtosis` is a measure of "tailedness" of a distribution. A #' distribution with a kurtosis values of about zero is called "mesokurtic". A #' kurtosis value larger than zero indicates a "leptokurtic" distribution with #' *fatter* tails. A kurtosis value below zero indicates a "platykurtic" #' distribution with *thinner* tails #' (\cite{https://en.wikipedia.org/wiki/Kurtosis}). #' } #' \subsection{Types of Kurtosis}{ #' `kurtosis()` supports three different methods for estimating kurtosis, #' as discussed in \cite{Joanes and Gill (1988)}: #' \itemize{ #' \item Type "1" is the "classical" method, which is `g2 = n * sum((x - #' mean(x))^4) / (sum((x - mean(x))^2)^2) - 3`. #' #' \item Type "2" first calculates the type-1 kurtosis, than adjusts the result: #' `G2 = ((n + 1) * g2 + 6) * (n - 1)/((n - 2) * (n - 3))`. This is what #' SAS and SPSS usually return #' #' \item Type "3" first calculates the type-1 kurtosis, than adjusts the result: #' `b2 = (g2 + 3) * (1 - 1 / n)^2 - 3`. This is what Minitab usually #' returns. #' } #' } #' \subsection{Standard Errors}{ #' It is recommended to compute empirical (bootstrapped) standard errors (via #' the `iterations` argument) than relying on analytic standard errors #' (\cite{Wright & Herrington, 2011}). #' } #' #' @references #' - D. N. Joanes and C. A. Gill (1998). Comparing measures of sample #' skewness and kurtosis. The Statistician, 47, 183–189. #' #' - Wright, D. B., & Herrington, J. A. (2011). Problematic standard #' errors and confidence intervals for skewness and kurtosis. Behavior #' research methods, 43(1), 8-17. #' #' @return Values of skewness or kurtosis. #' #' @examples #' skewness(rnorm(1000)) #' kurtosis(rnorm(1000)) #' @export skewness <- function(x, na.rm = TRUE, type = "2", iterations = NULL, verbose = TRUE, ...) { UseMethod("skewness") } # skewness ----------------------------------------- #' @export skewness.numeric <- function(x, na.rm = TRUE, type = "2", iterations = NULL, verbose = TRUE, ...) { if (na.rm) x <- x[!is.na(x)] n <- length(x) out <- (sum((x - mean(x))^3) / n) / (sum((x - mean(x))^2) / n)^1.5 type <- .check_skewness_type(type) if (type == "2" && n < 3) { if (verbose) { warning(insight::format_message("Need at least 3 complete observations for type-2-skewness. Using 'type=\"1\"' now."), call. = FALSE) } type <- "1" } .skewness <- switch(type, "1" = out, "2" = out * sqrt(n * (n - 1)) / (n - 2), "3" = out * ((1 - 1 / n))^1.5 ) out_se <- sqrt((6 * (n - 2)) / ((n + 1) * (n + 3))) .skewness_se <- switch(type, "1" = out_se, "2" = out_se * ((sqrt(n * (n - 1))) / (n - 2)), "3" = out_se * (((n - 1) / n)^1.5), ) if (!is.null(iterations)) { if (!requireNamespace("boot", quietly = TRUE)) { warning("Package 'boot' needed for bootstrapping SEs.", call. = FALSE) } else { results <- boot::boot( data = x, statistic = .boot_skewness, R = iterations, na.rm = na.rm, type = type ) out_se <- stats::sd(results$t, na.rm = TRUE) } } .skewness <- data.frame( Skewness = .skewness, SE = out_se ) class(.skewness) <- unique(c("parameters_skewness", class(.skewness))) .skewness } #' @export skewness.matrix <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { .skewness <- apply( x, 2, skewness, na.rm = na.rm, type = type, iterations = iterations ) .names <- colnames(x) if (length(.names) == 0) { .names <- paste0("X", seq_len(ncol(x))) } .skewness <- cbind(Parameter = .names, do.call(rbind, .skewness)) class(.skewness) <- unique(c("parameters_skewness", class(.skewness))) .skewness } #' @export skewness.data.frame <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { .skewness <- lapply(x, skewness, na.rm = na.rm, type = type, iterations = iterations ) .skewness <- cbind(Parameter = names(.skewness), do.call(rbind, .skewness)) class(.skewness) <- unique(c("parameters_skewness", class(.skewness))) .skewness } #' @export skewness.default <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { skewness( .factor_to_numeric(x), na.rm = na.rm, type = type, iterations = iterations ) } # Kurtosis ----------------------------------- #' @rdname skewness #' @export kurtosis <- function(x, na.rm = TRUE, type = "2", iterations = NULL, verbose = TRUE, ...) { UseMethod("kurtosis") } #' @export kurtosis.numeric <- function(x, na.rm = TRUE, type = "2", iterations = NULL, verbose = TRUE, ...) { if (na.rm) x <- x[!is.na(x)] n <- length(x) out <- n * sum((x - mean(x))^4) / (sum((x - mean(x))^2)^2) type <- .check_skewness_type(type) if (type == "2" && n < 4) { if (verbose) { warning(insight::format_message("Need at least 4 complete observations for type-2-kurtosis Using 'type=\"1\"' now."), call. = FALSE) } type <- "1" } .kurtosis <- switch(type, "1" = out - 3, "2" = ((n + 1) * (out - 3) + 6) * (n - 1) / ((n - 2) * (n - 3)), "3" = out * (1 - 1 / n)^2 - 3 ) out_se <- sqrt((24 * n * (n - 2) * (n - 3)) / (((n + 1)^2) * (n + 3) * (n + 5))) .kurtosis_se <- switch(type, "1" = out_se, "2" = out_se * (((n - 1) * (n + 1)) / ((n - 2) * (n - 3))), "3" = out_se * ((n - 1) / n)^2 ) if (!is.null(iterations)) { insight::check_if_installed("boot") results <- boot::boot( data = x, statistic = .boot_kurtosis, R = iterations, na.rm = na.rm, type = type ) out_se <- stats::sd(results$t, na.rm = TRUE) } .kurtosis <- data.frame( Kurtosis = .kurtosis, SE = out_se ) class(.kurtosis) <- unique(c("parameters_kurtosis", class(.kurtosis))) .kurtosis } #' @export kurtosis.matrix <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { .kurtosis <- apply( x, 2, kurtosis, na.rm = na.rm, type = type, iterations = iterations ) .names <- colnames(x) if (length(.names) == 0) { .names <- paste0("X", seq_len(ncol(x))) } .kurtosis <- cbind(Parameter = .names, do.call(rbind, .kurtosis)) class(.kurtosis) <- unique(c("parameters_kurtosis", class(.kurtosis))) .kurtosis } #' @export kurtosis.data.frame <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { .kurtosis <- lapply(x, kurtosis, na.rm = na.rm, type = type, iterations = iterations ) .kurtosis <- cbind(Parameter = names(.kurtosis), do.call(rbind, .kurtosis)) class(.kurtosis) <- unique(c("parameters_kurtosis", class(.kurtosis))) .kurtosis } #' @export kurtosis.default <- function(x, na.rm = TRUE, type = "2", iterations = NULL, ...) { kurtosis( .factor_to_numeric(x), na.rm = na.rm, type = type, iterations = iterations ) } # methods ----------------------------------------- #' @export as.numeric.parameters_kurtosis <- function(x, ...) { x$Kurtosis } #' @export as.numeric.parameters_skewness <- function(x, ...) { x$Skewness } #' @export as.double.parameters_kurtosis <- as.numeric.parameters_kurtosis #' @export as.double.parameters_skewness <- as.numeric.parameters_skewness #' @rdname skewness #' @export print.parameters_kurtosis <- function(x, digits = 3, test = FALSE, ...) { out <- summary(x, test = test) cat(insight::export_table(out, digits = digits)) invisible(x) } #' @rdname skewness #' @export print.parameters_skewness <- print.parameters_kurtosis #' @rdname skewness #' @export summary.parameters_skewness <- function(object, test = FALSE, ...) { if (test) { object$z <- object$Skewness / object$SE object$p <- 2 * (1 - stats::pnorm(abs(object$z))) } object } #' @rdname skewness #' @export summary.parameters_kurtosis <- function(object, test = FALSE, ...) { if (test) { object$z <- object$Kurtosis / object$SE object$p <- 2 * (1 - stats::pnorm(abs(object$z))) } object } # helper ------------------------------------------ .check_skewness_type <- function(type) { # convenience if (is.numeric(type)) type <- as.character(type) if (is.null(type) || is.na(type) || !(type %in% c("1", "2", "3", "I", "II", "III", "classic", "SPSS", "SAS", "Minitab"))) { warning(insight::format_message("'type' must be a character value from \"1\" to \"3\". Using 'type=\"2\"' now."), call. = FALSE) type <- "2" } switch(type, "1" = , "I" = , "classic" = "1", "2" = , "II" = , "SPSS" = , "SAS" = "2", "3" = , "III" = , "Minitab" = "3" ) } # bootstrapping ----------------------------------- .boot_skewness <- function(data, indices, na.rm, type) { datawizard::skewness(data[indices], na.rm = na.rm, type = type, iterations = NULL )$Skewness } .boot_kurtosis <- function(data, indices, na.rm, type) { datawizard::kurtosis(data[indices], na.rm = na.rm, type = type, iterations = NULL )$Kurtosis } datawizard/R/data_remove.R0000644000176200001440000000047614113652110015217 0ustar liggesusers#' @rdname data_rename #' @examples #' # Remove columns #' head(data_remove(iris, "Sepal.Length")) #' @export data_remove <- function(data, pattern, ...) { new <- data[!names(data) %in% pattern] attributes(new) <- utils::modifyList(attributes(data), attributes(new)) class(new) <- class(data) new } datawizard/R/standardize.R0000644000176200001440000002671014174253064015254 0ustar liggesusers#' Standardization (Z-scoring) #' #' Performs a standardization of data (z-scoring), i.e., centering and scaling, #' so that the data is expressed in terms of standard deviation (i.e., mean = 0, #' SD = 1) or Median Absolute Deviance (median = 0, MAD = 1). When applied to a #' statistical model, this function extracts the dataset, standardizes it, and #' refits the model with this standardized version of the dataset. The #' [normalize()] function can also be used to scale all numeric variables within #' the 0 - 1 range. #' \cr\cr #' For model standardization, see [effectsize::standardize.default()] #' #' @param x A data frame, a vector or a statistical model (for `unstandardize()` #' cannot be a model). #' @param robust Logical, if `TRUE`, centering is done by subtracting the #' median from the variables and dividing it by the median absolute deviation #' (MAD). If `FALSE`, variables are standardized by subtracting the #' mean and dividing it by the standard deviation (SD). #' @param two_sd If `TRUE`, the variables are scaled by two times the deviation #' (SD or MAD depending on `robust`). This method can be useful to obtain #' model coefficients of continuous parameters comparable to coefficients #' related to binary predictors, when applied to **the predictors** (not the #' outcome) (Gelman, 2008). #' @param weights Can be `NULL` (for no weighting), or: #' - For model: if `TRUE` (default), a weighted-standardization is carried out. #' - For `data.frame`s: a numeric vector of weights, or a character of the #' name of a column in the `data.frame` that contains the weights. #' - For numeric vectors: a numeric vector of weights. #' @param verbose Toggle warnings and messages on or off. #' @param select Character vector of column names. If `NULL` (the default), all #' variables will be selected. #' @param exclude Character vector of column names to be excluded from selection. #' @param remove_na How should missing values (`NA`) be treated: if `"none"` #' (default): each column's standardization is done separately, ignoring #' `NA`s. Else, rows with `NA` in the columns selected with `select` / #' `exclude` (`"selected"`) or in all columns (`"all"`) are dropped before #' standardization, and the resulting data frame does not include these cases. #' @param force Logical, if `TRUE`, forces standardization of factors and dates #' as well. Factors are converted to numerical values, with the lowest level #' being the value `1` (unless the factor has numeric levels, which are #' converted to the corresponding numeric value). #' @param append Logical or string. If `TRUE`, standardized variables get new #' column names (with the suffix `"_z"`) and are appended (column bind) to `x`, #' thus returning both the original and the standardized variables. If `FALSE`, #' original variables in `x` will be overwritten by their standardized versions. #' If a character value, standardized variables are appended with new column #' names (using the defined suffix) to the original data frame. #' @param reference A data frame or variable from which the centrality and #' deviation will be computed instead of from the input variable. Useful for #' standardizing a subset or new data according to another data frame. #' @param center,scale #' * For `standardize()`: \cr #' Numeric values, which can be used as alternative to `reference` to define #' a reference centrality and deviation. If `scale` and `center` are of #' length 1, they will be recycled to match the length of selected variables #' for standardization. Else, `center` and `scale` must be of same length as #' the number of selected variables. Values in `center` and `scale` will be #' matched to selected variables in the provided order, unless a named vector #' is given. In this case, names are matched against the names of the selected #' variables. #' #' * For `unstandardize()`: \cr #' `center` and `scale` correspond to the center (the mean / median) and the scale (SD / MAD) of #' the original non-standardized data (for data frames, should be named, or #' have column order correspond to the numeric column). However, one can also #' directly provide the original data through `reference`, from which the #' center and the scale will be computed (according to `robust` and `two_sd`). #' Alternatively, if the input contains the attributes `center` and `scale` #' (as does the output of `standardize()`), it will take it from there if the #' rest of the arguments are absent. #' @param ... Arguments passed to or from other methods. #' #' @return The standardized object (either a standardize data frame or a #' statistical model fitted on standardized data). #' #' @note When `x` is a vector or a data frame with `remove_na = "none")`, #' missing values are preserved, so the return value has the same length / #' number of rows as the original input. #' #' @seealso See [center()] for grand-mean centering of variables. #' #' @family transform utilities #' @family standardize #' #' @examples #' d <- iris[1:4, ] #' #' # vectors #' standardise(d$Petal.Length) #' #' # Data frames #' # overwrite #' standardise(d, select = c("Sepal.Length", "Sepal.Width")) #' #' # append #' standardise(d, select = c("Sepal.Length", "Sepal.Width"), append = TRUE) #' #' # append, suffix #' standardise(d, select = c("Sepal.Length", "Sepal.Width"), append = "_std") #' #' # standardizing with reference center and scale #' d <- data.frame( #' a = c(-2, -1, 0, 1, 2), #' b = c(3, 4, 5, 6, 7) #' ) #' #' # default standardization, based on mean and sd of each variable #' standardize(d) # means are 0 and 5, sd ~ 1.581139 #' #' # standardization, based on mean and sd set to the same values #' standardize(d, center = c(0, 5), scale = c(1.581, 1.581)) #' #' # standardization, mean and sd for each variable newly defined #' standardize(d, center = c(3, 4), scale = c(2, 4)) #' #' # standardization, taking same mean and sd for each variable #' standardize(d, center = 1, scale = 3) #' @export standardize <- function(x, ...) { UseMethod("standardize") } #' @rdname standardize #' @export standardise <- standardize #' @rdname standardize #' @export standardize.numeric <- function(x, robust = FALSE, two_sd = FALSE, weights = NULL, verbose = TRUE, reference = NULL, center = NULL, scale = NULL, ...) { args <- .process_std_center(x, weights, robust, verbose, reference, center, scale) # Perform standardization if (is.null(args)) { # all NA? return(x) } else if (is.null(args$check)) { vals <- rep(0, length(args$vals)) # If only unique value } else { if (two_sd) { vals <- as.vector((args$vals - args$center) / (2 * args$scale)) } else { vals <- as.vector((args$vals - args$center) / args$scale) } } scaled_x <- rep(NA, length(args$valid_x)) scaled_x[args$valid_x] <- vals attr(scaled_x, "center") <- args$center attr(scaled_x, "scale") <- args$scale attr(scaled_x, "robust") <- robust scaled_x } #' @export standardize.double <- standardize.numeric #' @export standardize.integer <- standardize.numeric #' @export standardize.matrix <- function(x, ...) { xl <- lapply(seq_len(ncol(x)), function(i) x[, i]) xz <- lapply(xl, datawizard::standardize, ...) x_out <- do.call(cbind, xz) dimnames(x_out) <- dimnames(x) attr(x_out, "center") <- sapply(xz, attr, "center") attr(x_out, "scale") <- sapply(xz, attr, "scale") attr(x_out, "robust") <- sapply(xz, attr, "robust")[1] x_out } #' @rdname standardize #' @export standardize.factor <- function(x, robust = FALSE, two_sd = FALSE, weights = NULL, verbose = TRUE, force = FALSE, ...) { if (!force) { return(x) } standardize(.factor_to_numeric(x), robust = robust, two_sd = two_sd, weights = weights, verbose = verbose, ... ) } #' @export standardize.character <- standardize.factor #' @export standardize.logical <- standardize.factor #' @export standardize.Date <- standardize.factor #' @export standardize.AsIs <- standardize.numeric # Data frames ------------------------------------------------------------- #' @rdname standardize #' @export standardize.data.frame <- function(x, robust = FALSE, two_sd = FALSE, weights = NULL, verbose = TRUE, reference = NULL, select = NULL, exclude = NULL, remove_na = c("none", "selected", "all"), force = FALSE, append = FALSE, center = NULL, scale = NULL, ...) { # process arguments args <- .process_std_args(x, select, exclude, weights, append, append_suffix = "_z", force, remove_na, reference, .center = center, .scale = scale ) # set new values x <- args$x # Loop through variables and standardize it for (var in args$select) { x[[var]] <- standardize(x[[var]], robust = robust, two_sd = two_sd, weights = args$weights, reference = reference[[var]], center = args$center[var], scale = args$scale[var], verbose = FALSE, force = force ) } attr(x, "center") <- sapply(x[args$select], function(z) attributes(z)$center) attr(x, "scale") <- sapply(x[args$select], function(z) attributes(z)$scale) attr(x, "robust") <- robust x } #' @export standardize.grouped_df <- function(x, robust = FALSE, two_sd = FALSE, weights = NULL, verbose = TRUE, reference = NULL, select = NULL, exclude = NULL, remove_na = c("none", "selected", "all"), force = FALSE, append = FALSE, center = NULL, scale = NULL, ...) { args <- .process_grouped_df(x, select, exclude, append, append_suffix = "_z", reference, weights, force ) for (rows in args$grps) { args$x[rows, ] <- standardize( args$x[rows, ], select = args$select, exclude = NULL, robust = robust, two_sd = two_sd, weights = args$weights, remove_na = remove_na, verbose = verbose, force = force, append = FALSE, center = center, scale = scale, ... ) } # set back class, so data frame still works with dplyr attributes(args$x) <- args$info args$x } datawizard/R/utils_misc.R0000644000176200001440000000253714173221405015111 0ustar liggesusers#' Check if object is empty #' #' @param x A list, a vector, or a dataframe. #' #' @return A logical indicating whether the entered object is empty. #' #' @examples #' is_empty_object(c(1, 2, 3, NA)) #' is_empty_object(list(NULL, c(NA, NA))) #' is_empty_object(list(NULL, NA)) #' @export is_empty_object <- function(x) { flag_empty <- FALSE # precaution to take for a tibble if (inherits(x, c("tbl_df", "tbl"))) x <- as.data.frame(x) if (inherits(x, "data.frame")) { x <- as.data.frame(x) if (nrow(x) > 0 && ncol(x) > 0) { x <- x[, !sapply(x, function(i) all(is.na(i))), drop = FALSE] # this is much faster than apply(x, 1, FUN) flag_empty <- all(rowSums(is.na(x)) == ncol(x)) } else { flag_empty <- TRUE } # a list but not a data.frame } else if (is.list(x) && length(x) > 0) { x <- tryCatch( { compact_list(x) }, error = function(x) { x } ) } else if (!is.null(x)) { x <- stats::na.omit(x) } # need to check for is.null for R 3.4 isTRUE(flag_empty) || length(x) == 0 || is.null(x) || isTRUE(nrow(x) == 0) || isTRUE(ncol(x) == 0) } obj_has_name <- function(x, name) { name %in% names(x) } obj_has_rownames <- function(x) { !identical(as.character(1:nrow(x)), rownames(x)) } datawizard/R/winsorize.R0000644000176200001440000000376714113652110014770 0ustar liggesusers#' Winsorize data #' #' @details #' #' Winsorizing or winsorization is the transformation of statistics by limiting #' extreme values in the statistical data to reduce the effect of possibly #' spurious outliers. The distribution of many statistics can be heavily #' influenced by outliers. A typical strategy is to set all outliers (values #' beyond a certain threshold) to a specified percentile of the data; for #' example, a 90\% winsorization would see all data below the 5th percentile set #' to the 5th percentile, and data above the 95th percentile set to the 95th #' percentile. Winsorized estimators are usually more robust to outliers than #' their more standard forms. #' #' @return #' #' A dataframe with winsorized columns or a winsorized vector. #' #' @param data Dataframe or vector. #' @param threshold The amount of winsorization. #' @param verbose Toggle warnings. #' @param ... Currently not used. #' #' @examples #' winsorize(iris$Sepal.Length, threshold = 0.2) #' winsorize(iris, threshold = 0.2) #' @export winsorize <- function(data, ...) { UseMethod("winsorize") } #' @export winsorize.factor <- function(data, ...) { data } #' @export winsorize.character <- winsorize.factor #' @export winsorize.logical <- winsorize.factor #' @export winsorize.data.frame <- function(data, threshold = 0.2, verbose = TRUE, ...) { sapply(data, winsorize, threshold = threshold, verbose = verbose) } #' @rdname winsorize #' @export winsorize.numeric <- function(data, threshold = 0.2, verbose = TRUE, ...) { if (threshold < 0 || threshold > 1) { if (isTRUE(verbose)) { warning("'threshold' for winsorization must be a scalar between 0 and 1. Did not winsorize data.", call. = FALSE) } return(data) } y <- sort(data) n <- length(data) ibot <- floor(threshold * n) + 1 itop <- length(data) - ibot + 1 xbot <- y[ibot] xtop <- y[itop] winval <- ifelse(data <= xbot, xbot, data) ifelse(winval >= xtop, xtop, winval) } datawizard/R/utils_list.R0000644000176200001440000000114014173161423015121 0ustar liggesusers#' Remove empty elements from lists #' #' @param x A list or vector. #' @param remove_na Logical to decide if `NA`s should be removed. #' #' @examples #' compact_list(list(NULL, 1, c(NA, NA))) #' compact_list(c(1, NA, NA)) #' compact_list(c(1, NA, NA), remove_na = TRUE) #' @export compact_list <- function(x, remove_na = FALSE) { if (remove_na) { x[!sapply(x, function(i) length(i) == 0L || is.null(i) || (length(i) == 1L & is.na(i)) || any(i == "NULL", na.rm = TRUE))] } else { x[!sapply(x, function(i) length(i) == 0L || is.null(i) || any(i == "NULL", na.rm = TRUE))] } } datawizard/R/demean.R0000644000176200001440000004266014126335276014202 0ustar liggesusers#' Compute group-meaned and de-meaned variables #' #' `demean()` computes group- and de-meaned versions of a variable that can be #' used in regression analysis to model the between- and within-subject effect. #' `degroup()` is more generic in terms of the centering-operation. While #' `demean()` always uses mean-centering, `degroup()` can also use the mode or #' median for centering. #' #' @param x A data frame. #' @param select Character vector (or formula) with names of variables to select #' that should be group- and de-meaned. #' @param group Character vector (or formula) with the name of the variable that #' indicates the group- or cluster-ID. #' @param center Method for centering. `demean()` always performs #' mean-centering, while `degroup()` can use `center = "median"` or #' `center = "mode"` for median- or mode-centering, and also `"min"` #' or `"max"`. #' @param suffix_demean,suffix_groupmean String value, will be appended to the #' names of the group-meaned and de-meaned variables of `x`. By default, #' de-meaned variables will be suffixed with `"_within"` and #' grouped-meaned variables with `"_between"`. #' @param add_attributes Logical, if `TRUE`, the returned variables gain #' attributes to indicate the within- and between-effects. This is only #' relevant when printing `model_parameters()` - in such cases, the #' within- and between-effects are printed in separated blocks. #' @inheritParams center #' #' @return A data frame with the group-/de-meaned variables, which get the suffix #' `"_between"` (for the group-meaned variable) and `"_within"` (for #' the de-meaned variable) by default. #' #' @seealso If grand-mean centering (instead of centering within-clusters) #' is required, see [center()]. #' #' @details #' \subsection{Heterogeneity Bias}{ #' Mixed models include different levels of sources of variability, i.e. #' error terms at each level. When macro-indicators (or level-2 predictors, #' or higher-level units, or more general: *group-level predictors that #' **vary** within and across groups*) are included as fixed effects (i.e. #' treated as covariate at level-1), the variance that is left unaccounted for #' this covariate will be absorbed into the error terms of level-1 and level-2 #' (\cite{Bafumi and Gelman 2006; Gelman and Hill 2007, Chapter 12.6.}): #' \dQuote{Such covariates contain two parts: one that is specific to the #' higher-level entity that does not vary between occasions, and one that #' represents the difference between occasions, within higher-level entities} #' (\cite{Bell et al. 2015}). Hence, the error terms will be correlated with #' the covariate, which violates one of the assumptions of mixed models #' (iid, independent and identically distributed error terms). This bias is #' also called the *heterogeneity bias* (\cite{Bell et al. 2015}). To #' resolve this problem, level-2 predictors used as (level-1) covariates should #' be separated into their "within" and "between" effects by "de-meaning" and #' "group-meaning": After demeaning time-varying predictors, \dQuote{at the #' higher level, the mean term is no longer constrained by Level 1 effects, #' so it is free to account for all the higher-level variance associated #' with that variable} (\cite{Bell et al. 2015}). #' } #' \subsection{Panel data and correlating fixed and group effects}{ #' `demean()` is intended to create group- and de-meaned variables #' for panel regression models (fixed effects models), or for complex #' random-effect-within-between models (see \cite{Bell et al. 2015, 2018}), #' where group-effects (random effects) and fixed effects correlate (see #' \cite{Bafumi and Gelman 2006}). This can happen, for instance, when #' analyzing panel data, which can lead to *Heterogeneity Bias*. To #' control for correlating predictors and group effects, it is recommended #' to include the group-meaned and de-meaned version of *time-varying covariates* #' (and group-meaned version of *time-invariant covariates* that are on #' a higher level, e.g. level-2 predictors) in the model. By this, one can #' fit complex multilevel models for panel data, including time-varying #' predictors, time-invariant predictors and random effects. #' } #' \subsection{Why mixed models are preferred over fixed effects models}{ #' A mixed models approach can model the causes of endogeneity explicitly #' by including the (separated) within- and between-effects of time-varying #' fixed effects and including time-constant fixed effects. Furthermore, #' mixed models also include random effects, thus a mixed models approach #' is superior to classic fixed-effects models, which lack information of #' variation in the group-effects or between-subject effects. Furthermore, #' fixed effects regression cannot include random slopes, which means that #' fixed effects regressions are neglecting \dQuote{cross-cluster differences #' in the effects of lower-level controls (which) reduces the precision of #' estimated context effects, resulting in unnecessarily wide confidence #' intervals and low statistical power} (\cite{Heisig et al. 2017}). #' } #' \subsection{Terminology}{ #' The group-meaned variable is simply the mean of an independent variable #' within each group (or id-level or cluster) represented by `group`. #' It represents the cluster-mean of an independent variable. The regression #' coefficient of a group-meaned variable is the *between-subject-effect*. #' The de-meaned variable is then the centered version of the group-meaned #' variable. De-meaning is sometimes also called person-mean centering or #' centering within clusters. The regression coefficient of a de-meaned #' variable represents the *within-subject-effect*. #' } #' \subsection{De-meaning with continuous predictors}{ #' For continuous time-varying predictors, the recommendation is to include #' both their de-meaned and group-meaned versions as fixed effects, but not #' the raw (untransformed) time-varying predictors themselves. The de-meaned #' predictor should also be included as random effect (random slope). In #' regression models, the coefficient of the de-meaned predictors indicates #' the within-subject effect, while the coefficient of the group-meaned #' predictor indicates the between-subject effect. #' } #' \subsection{De-meaning with binary predictors}{ #' For binary time-varying predictors, there are two recommendations. First #' is to include the raw (untransformed) binary predictor as fixed effect #' only and the *de-meaned* variable as random effect (random slope). #' The alternative would be to add the de-meaned version(s) of binary #' time-varying covariates as additional fixed effect as well (instead of #' adding it as random slope). Centering time-varying binary variables to #' obtain within-effects (level 1) isn't necessary. They have a sensible #' interpretation when left in the typical 0/1 format (\cite{Hoffmann 2015, #' chapter 8-2.I}). `demean()` will thus coerce categorical time-varying #' predictors to numeric to compute the de- and group-meaned versions for #' these variables, where the raw (untransformed) binary predictor and the #' de-meaned version should be added to the model. #' } #' \subsection{De-meaning of factors with more than 2 levels}{ #' Factors with more than two levels are demeaned in two ways: first, these #' are also converted to numeric and de-meaned; second, dummy variables #' are created (binary, with 0/1 coding for each level) and these binary #' dummy-variables are de-meaned in the same way (as described above). #' Packages like \pkg{panelr} internally convert factors to dummies before #' demeaning, so this behaviour can be mimicked here. #' } #' \subsection{De-meaning interaction terms}{ There are multiple ways to deal #' with interaction terms of within- and between-effects. A classical approach #' is to simply use the product term of the de-meaned variables (i.e. #' introducing the de-meaned variables as interaction term in the model #' formula, e.g. `y ~ x_within * time_within`). This approach, however, #' might be subject to bias (see \cite{Giesselmann & Schmidt-Catran 2020}). #' \cr \cr #' Another option is to first calculate the product term and then apply the #' de-meaning to it. This approach produces an estimator \dQuote{that reflects #' unit-level differences of interacted variables whose moderators vary #' within units}, which is desirable if *no* within interaction of #' two time-dependent variables is required. \cr \cr #' A third option, when the interaction should result in a genuine within #' estimator, is to "double de-mean" the interaction terms #' (\cite{Giesselmann & Schmidt-Catran 2018}), however, this is currently #' not supported by `demean()`. If this is required, the `wmb()` #' function from the \pkg{panelr} package should be used. \cr \cr #' To de-mean interaction terms for within-between models, simply specify #' the term as interaction for the `select`-argument, e.g. #' `select = "a*b"` (see 'Examples'). #' } #' \subsection{Analysing panel data with mixed models using lme4}{ #' A description of how to translate the #' formulas described in *Bell et al. 2018* into R using `lmer()` #' from \pkg{lme4} can be found in #' [this vignette](https://easystats.github.io/datawizard/articles/demean.html). #' } #' #' @references \itemize{ #' \item Bafumi J, Gelman A. 2006. Fitting Multilevel Models When Predictors #' and Group Effects Correlate. In. Philadelphia, PA: Annual meeting of the #' American Political Science Association. #' #' \item Bell A, Fairbrother M, Jones K. 2019. Fixed and Random Effects #' Models: Making an Informed Choice. Quality & Quantity (53); 1051-1074 #' #' \item Bell A, Jones K. 2015. Explaining Fixed Effects: Random Effects #' Modeling of Time-Series Cross-Sectional and Panel Data. Political Science #' Research and Methods, 3(1), 133–153. #' #' \item Gelman A, Hill J. 2007. Data Analysis Using Regression and #' Multilevel/Hierarchical Models. Analytical Methods for Social Research. #' Cambridge, New York: Cambridge University Press #' #' \item Giesselmann M, Schmidt-Catran, AW. 2020. Interactions in fixed #' effects regression models. Sociological Methods & Research, 1–28. #' https://doi.org/10.1177/0049124120914934 #' #' \item Heisig JP, Schaeffer M, Giesecke J. 2017. The Costs of Simplicity: #' Why Multilevel Models May Benefit from Accounting for Cross-Cluster #' Differences in the Effects of Controls. American Sociological Review 82 #' (4): 796–827. #' #' \item Hoffman L. 2015. Longitudinal analysis: modeling within-person #' fluctuation and change. New York: Routledge #' } #' @examples #' data(iris) #' iris$ID <- sample(1:4, nrow(iris), replace = TRUE) # fake-ID #' iris$binary <- as.factor(rbinom(150, 1, .35)) # binary variable #' #' x <- demean(iris, select = c("Sepal.Length", "Petal.Length"), group = "ID") #' head(x) #' #' x <- demean(iris, select = c("Sepal.Length", "binary", "Species"), group = "ID") #' head(x) #' #' #' # demean interaction term x*y #' dat <- data.frame( #' a = c(1, 2, 3, 4, 1, 2, 3, 4), #' x = c(4, 3, 3, 4, 1, 2, 1, 2), #' y = c(1, 2, 1, 2, 4, 3, 2, 1), #' ID = c(1, 2, 3, 1, 2, 3, 1, 2) #' ) #' demean(dat, select = c("a", "x*y"), group = "ID") #' #' # or in formula-notation #' demean(dat, select = ~ a + x * y, group = ~ID) #' @export demean <- function(x, select, group, suffix_demean = "_within", suffix_groupmean = "_between", add_attributes = TRUE, verbose = TRUE) { degroup( x = x, select = select, group = group, center = "mean", suffix_demean = suffix_demean, suffix_groupmean = suffix_groupmean, add_attributes = add_attributes, verbose = verbose ) } #' @rdname demean #' @export degroup <- function(x, select, group, center = "mean", suffix_demean = "_within", suffix_groupmean = "_between", add_attributes = TRUE, verbose = TRUE) { # ugly tibbles again... x <- as.data.frame(x) center <- match.arg(tolower(center), choices = c("mean", "median", "mode", "min", "max")) if (inherits(select, "formula")) { # formula to character, remove "~", split at "+" select <- trimws(unlist(strsplit(gsub("~", "", .safe_deparse(select), fixed = TRUE), "+", fixed = TRUE))) } if (inherits(group, "formula")) { group <- all.vars(group) } interactions_no <- select[!grepl("(\\*|\\:)", select)] interactions_yes <- select[grepl("(\\*|\\:)", select)] if (length(interactions_yes)) { interaction_terms <- lapply(strsplit(interactions_yes, "*", fixed = TRUE), trimws) product <- lapply(interaction_terms, function(i) do.call(`*`, x[, i])) new_dat <- as.data.frame(stats::setNames(product, gsub("\\s", "", gsub("*", "_", interactions_yes, fixed = TRUE)))) x <- cbind(x, new_dat) select <- c(interactions_no, colnames(new_dat)) } not_found <- setdiff(select, colnames(x)) if (length(not_found) && isTRUE(verbose)) { insight::print_color(sprintf( "%i variables were not found in the dataset: %s\n", length(not_found), paste0(not_found, collapse = ", ") ), color = "red" ) } select <- intersect(colnames(x), select) # get data to demean... dat <- x[, c(select, group)] # find categorical predictors that are coded as factors categorical_predictors <- sapply(dat[select], is.factor) # convert binary predictors to numeric if (any(categorical_predictors)) { # convert categorical to numeric, and then demean dat[select[categorical_predictors]] <- lapply( dat[select[categorical_predictors]], function(i) as.numeric(i) - 1 ) # convert categorical to dummy, and demean each binary dummy for (i in select[categorical_predictors]) { if (nlevels(x[[i]]) > 2) { for (j in levels(x[[i]])) { # create vector with zeros f <- rep(0, nrow(x)) # for each matching level, set dummy to 1 f[x[[i]] == j] <- 1 dummy <- data.frame(f) # colnames = variable name + factor level # also add new dummy variables to "select" colnames(dummy) <- sprintf("%s_%s", i, j) select <- c(select, sprintf("%s_%s", i, j)) # add to data dat <- cbind(dat, dummy) } } } # tell user... if (isTRUE(verbose)) { insight::print_color( sprintf( "Categorical predictors (%s) have been coerced to numeric values to compute de- and group-meaned variables.\n", paste0(names(categorical_predictors)[categorical_predictors], collapse = ", ") ), "yellow" ) } } # group variables, then calculate the mean-value # for variables within each group (the group means). assign # mean values to a vector of same length as the data if (center == "mode") { x_gm_list <- lapply(select, function(i) { stats::ave(dat[[i]], dat[[group]], FUN = function(.gm) .mode(stats::na.omit(.gm))) }) } else if (center == "median") { x_gm_list <- lapply(select, function(i) { stats::ave(dat[[i]], dat[[group]], FUN = function(.gm) stats::median(.gm, na.rm = TRUE)) }) } else if (center == "min") { x_gm_list <- lapply(select, function(i) { stats::ave(dat[[i]], dat[[group]], FUN = function(.gm) min(.gm, na.rm = TRUE)) }) } else if (center == "max") { x_gm_list <- lapply(select, function(i) { stats::ave(dat[[i]], dat[[group]], FUN = function(.gm) max(.gm, na.rm = TRUE)) }) } else { x_gm_list <- lapply(select, function(i) { stats::ave(dat[[i]], dat[[group]], FUN = function(.gm) mean(.gm, na.rm = TRUE)) }) } names(x_gm_list) <- select # create de-meaned variables by subtracting the group mean from each individual value x_dm_list <- lapply(select, function(i) dat[[i]] - x_gm_list[[i]]) names(x_dm_list) <- select # convert to data frame and add suffix to column names x_gm <- as.data.frame(x_gm_list) x_dm <- as.data.frame(x_dm_list) colnames(x_dm) <- sprintf("%s%s", colnames(x_dm), suffix_demean) colnames(x_gm) <- sprintf("%s%s", colnames(x_gm), suffix_groupmean) if (isTRUE(add_attributes)) { x_dm[] <- lapply(x_dm, function(i) { attr(i, "within-effect") <- TRUE i }) x_gm[] <- lapply(x_gm, function(i) { attr(i, "between-effect") <- TRUE i }) } cbind(x_gm, x_dm) } #' @rdname demean #' @export detrend <- degroup # helper ------------------ .mode <- function(x) { uniqv <- unique(x) tab <- tabulate(match(x, uniqv)) idx <- which.max(tab) uniqv[idx] } #' @keywords internal .safe_deparse <- function(string) { paste0(sapply(deparse(string, width.cutoff = 500), trimws, simplify = TRUE), collapse = " ") } datawizard/R/backports.R0000644000176200001440000000050414113652110014711 0ustar liggesusers.str2lang <- function(s) { stopifnot(length(s) == 1L) ex <- parse(text = s, keep.source = FALSE) stopifnot(length(ex) == 1L) ex[[1L]] } isTRUE <- function(x) { is.logical(x) && length(x) == 1L && !is.na(x) && x } isFALSE <- function(x) { is.logical(x) && length(x) == 1L && !is.na(x) && !x } datawizard/R/utils_standardize_center.R0000644000176200001440000002761614164744634020051 0ustar liggesusers # helper ----------------------------- ## preparation for standardize and center ---- .process_std_center <- function(x, weights, robust, verbose, reference = NULL, center = NULL, scale = NULL) { # Warning if all NaNs if (all(is.na(x))) { return(NULL) } if (.are_weights(weights)) { valid_x <- !is.na(x) & !is.na(weights) vals <- x[valid_x] weights <- weights[valid_x] } else { valid_x <- !is.na(x) vals <- x[valid_x] } # Sanity checks check <- .check_standardize_numeric(x, name = NULL, verbose = verbose, reference = reference) if (is.factor(vals) || is.character(vals)) { vals <- .factor_to_numeric(vals) } # Get center and scale ref <- .get_center_scale(vals, robust, weights, reference, .center = center, .scale = scale) list( vals = vals, valid_x = valid_x, center = ref$center, scale = ref$scale, check = check ) } ## processing and checking of arguments ---- .process_std_args <- function(x, select, exclude, weights, append, append_suffix = "_z", force, remove_na = "none", reference = NULL, .center = NULL, .scale = NULL) { # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } # check append argument, and set default if (isFALSE(append)) { append <- NULL } else if (isTRUE(append)) { append <- append_suffix } if (!is.null(weights) && is.character(weights)) { if (weights %in% colnames(x)) { exclude <- c(exclude, weights) } else { warning(insight::format_message("Could not find weighting column '", weights, "'. Weighting not carried out.")) weights <- NULL } } select <- .select_variables(x, select, exclude, force) # check if selected variables are in reference if (!is.null(reference) && !all(select %in% names(reference))) { stop("The 'reference' must include all variables from 'select'.") } # drop NAs remove_na <- match.arg(remove_na, c("none", "selected", "all")) omit <- switch(remove_na, none = logical(nrow(x)), selected = rowSums(sapply(x[select], is.na)) > 0, all = rowSums(sapply(x, is.na)) > 0 ) x <- x[!omit, , drop = FALSE] if (!is.null(weights) && is.character(weights)) weights <- x[[weights]] # append standardized variables if (!is.null(append) && append != "") { new_variables <- x[select] colnames(new_variables) <- paste0(colnames(new_variables), append) x <- cbind(x, new_variables) select <- colnames(new_variables) } # check for reference center and scale if (!is.null(.center)) { # for center(), we have no scale - set it to default value if (is.null(.scale)) { .scale <- rep(1, length(.center)) } # center and scale must have same length if (length(.center) != length(.scale)) { stop("'center' and 'scale' must be of same length.") } # center and scale must either be of length 1 or of same length as selected variables if (length(.center) > 1 && length(.center) != length(select)) { stop(insight::format_message("'center' and 'scale' must have the same length as the selected variables for standardization or centering.")) } # if of length 1, recycle if (length(.center) == 1) { .center <- rep(.center, length(select)) .scale <- rep(.scale, length(select)) } # set names if (is.null(names(.center))) { .center <- stats::setNames(.center, select) } if (is.null(names(.scale))) { .scale <- stats::setNames(.scale, select) } } else { # use NA if missing, so we can index these as vectors .center <- stats::setNames(rep(NA, length(select)), select) .scale <- stats::setNames(rep(NA, length(select)), select) } list( x = x, select = select, exclude = exclude, weights = weights, append = append, center = .center, scale = .scale ) } ## retrieve center and scale information ---- .get_center_scale <- function(x, robust = FALSE, weights = NULL, reference = NULL, .center = NULL, .scale = NULL) { if (is.null(reference)) reference <- x # for center(), we have no scale. default to 0 if (is.null(.scale) || is.na(.scale)) { .scale <- 1 } if (!is.null(.center) && !is.na(.center)) { center <- .center scale <- .scale } else if (robust) { center <- .median(reference, weights) scale <- .mad(reference, weights) } else { center <- .mean(reference, weights) scale <- .sd(reference, weights) } list(center = center, scale = scale) } ## check range of input variables ---- #' @keywords internal .check_standardize_numeric <- function(x, name = NULL, verbose = TRUE, reference = NULL) { # Warning if only one value if (length(unique(x)) == 1 && is.null(reference)) { if (verbose) { if (is.null(name)) { message(insight::format_message("The variable contains only one unique value and will be set to 0.")) } else { message(insight::format_message(paste0("The variable `", name, "` contains only one unique value and will be set to 0."))) } } return(NULL) } # Warning if logical vector if (length(unique(x)) == 2 && !is.factor(x) && !is.character(x)) { if (verbose) { if (is.null(name)) { message(insight::format_message("The variable contains only two different values. Consider converting it to a factor.")) } else { message(insight::format_message(paste0("Variable `", name, "` contains only two different values. Consider converting it to a factor."))) } } } x } ## variables to standardize and center ---- .select_variables <- function(x, select, exclude, force) { if (is.null(select)) { select <- names(x) } if (!is.null(exclude)) { select <- setdiff(select, exclude) } if (!force) { factors <- sapply(x[select], function(i) is.factor(i) | is.character(i)) select <- select[!factors] } select } .are_weights <- function(w) { !is.null(w) && length(w) && !all(w == 1) && !all(w == w[1]) } .factor_to_numeric <- function(x) { # no need to change for numeric if (is.numeric(x)) { return(x) } # Dates can be coerced by as.numeric(), w/o as.character() if (inherits(x, "Date")) { return(as.numeric(x)) } if (anyNA(suppressWarnings(as.numeric(as.character(stats::na.omit(x)))))) { if (is.character(x)) { x <- as.factor(x) } levels(x) <- 1:nlevels(x) } as.numeric(as.character(x)) } ## own implementation of mean/median/mad/sd ---- .mean <- function(x, weights = NULL, verbose = TRUE, ...) { if (!.are_weights(weights)) { return(mean(x, na.rm = TRUE)) } if (!all(weights > 0, na.rm = TRUE)) { if (isTRUE(verbose)) { warning("Some weights were negative. Weighting not carried out.", call. = FALSE) } return(mean(x, na.rm = TRUE)) } stats::weighted.mean(x, weights, na.rm = TRUE) } .median <- function(x, weights = NULL, verbose = TRUE, ...) { # From spatstat + wiki if (!.are_weights(weights)) { return(stats::median(x, na.rm = TRUE)) } if (!all(weights > 0, na.rm = TRUE)) { if (isTRUE(verbose)) { warning("Some weights were negative. Weighting not carried out.", call. = FALSE) } return(stats::median(x, na.rm = TRUE)) } oo <- order(x) x <- x[oo] weights <- weights[oo] Fx <- cumsum(weights) / sum(weights) lefties <- which(Fx <= 0.5) left <- max(lefties) if (length(lefties) == 0) { result <- x[1] } else if (left == length(x)) { result <- x[length(x)] } else { result <- x[left] if (!(Fx[left - 1] < 0.5 && 1 - Fx[left] < 0.5)) { right <- left + 1 y <- x[left] * Fx[left] + x[right] * Fx[right] if (is.finite(y)) result <- y } } result } # For standardize_info ---------------------------------------------------- .sd <- function(x, weights = NULL) { # from cov.wt if (!.are_weights(weights)) { return(stats::sd(x, na.rm = TRUE)) } stopifnot(all(weights > 0, na.rm = TRUE)) weights1 <- weights / sum(weights) center <- sum(weights1 * x) xc <- sqrt(weights1) * (x - center) var <- (t(xc) %*% xc) / (1 - sum(weights1^2)) sqrt(as.vector(var)) } .mad <- function(x, weights = NULL, constant = 1.4826) { # From matrixStats if (!.are_weights(weights)) { return(stats::mad(x, na.rm = TRUE)) } stopifnot(all(weights > 0, na.rm = TRUE)) center <- .median(x, weights = weights) x <- abs(x - center) constant * .median(x, weights = weights) } # For standardize_parameters ---------------------------------------------- #' @keywords internal .get_object <- function(x, attribute_name = "object_name") { obj_name <- attr(x, attribute_name, exact = TRUE) model <- NULL if (!is.null(obj_name)) { model <- tryCatch( { get(obj_name, envir = parent.frame()) }, error = function(e) { NULL } ) if (is.null(model) || # prevent self reference inherits(model, "parameters_model")) { model <- tryCatch( { get(obj_name, envir = globalenv()) }, error = function(e) { NULL } ) } } model } # for grouped df --------------------------- .process_grouped_df <- function(x, select, exclude, append, append_suffix = "_z", reference, weights, force) { if (!is.null(reference)) { stop("The `reference` argument cannot be used with grouped standardization for now.") } # check append argument, and set default if (isFALSE(append)) { append <- NULL } else if (isTRUE(append)) { append <- append_suffix } info <- attributes(x) # dplyr >= 0.8.0 returns attribute "indices" grps <- attr(x, "groups", exact = TRUE) # check for formula notation, convert to character vector if (inherits(select, "formula")) { select <- all.vars(select) } if (inherits(exclude, "formula")) { exclude <- all.vars(exclude) } if (is.numeric(weights)) { warning( "For grouped data frames, 'weights' must be a character, not a numeric vector.\n", "Ignoring weightings." ) weights <- NULL } # dplyr < 0.8.0? if (is.null(grps)) { grps <- attr(x, "indices", exact = TRUE) grps <- lapply(grps, function(x) x + 1) } else { grps <- grps[[".rows"]] } x <- as.data.frame(x) select <- .select_variables(x, select, exclude, force) # append standardized variables if (!is.null(append) && append != "") { new_variables <- x[select] colnames(new_variables) <- paste0(colnames(new_variables), append) x <- cbind(x, new_variables) select <- colnames(new_variables) info$names <- c(info$names, select) } list(x = x, info = info, select = select, grps = grps, weights = weights) } datawizard/R/data_reorder.R0000644000176200001440000000064514113652110015362 0ustar liggesusers#' @rdname data_rename #' @examples #' # Reorder columns #' head(data_reorder(iris, c("Species", "Sepal.Length"))) #' head(data_reorder(iris, c("Species", "dupa"))) # Safe for non-existing cols #' @export data_reorder <- function(data, cols, safe = TRUE, ...) { remaining_columns <- setdiff(colnames(data), cols) if (isTRUE(safe)) cols <- cols[cols %in% names(data)] data[, c(cols, remaining_columns)] } datawizard/NEWS.md0000644000176200001440000000320714174263210013504 0ustar liggesusers# datawizard 0.2.3 - New functions: + to work with lists: `is_empty_object()` and `compact_list()` + to work with strings: `compact_character()` # datawizard 0.2.2 - New function `data_extract()` (or its alias `extract()`) to pull single variables from a data frame, possibly naming each value by the row names of that data frame. - `reshape_ci()` gains a `ci_type` argument, to reshape data frames where CI-columns have prefixes other than `"CI"`. - `standardize()` and `center()` gain arguments `center` and `scale`, to define references for centrality and deviation that are used when centering or standardizing variables. - `center()` gains the arguments `force` and `reference`, similar to `standardize()`. - The functionality of the `append` argument in `center()` and `standardize()` was revised. 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liggesusersdatawizard/inst/doc/standardize_data.html0000644000176200001440000056635714174263642020354 0ustar liggesusers Data Standardization

Data Standardization

Introduction

To make sense of their data and effects, scientists might want to standardize (Z-score) their variables. This makes the data unitless, expressed only in terms of deviation from an index of centrality (e.g., the mean or the median). However, aside from some benefits, standardization also comes with challenges and issues, that the scientist should be aware of.

Methods of Standardization

The datawizard package offers two methods of standardization via the standardize() function:

  • Normal standardization: center around the mean, with SD units (default).

  • Robust standardization: center around the median, with MAD (median absolute deviation) units (robust = TRUE).

Let’s look at the following example:

library(datawizard)
library(effectsize) # for data

# let's have a look at what the data look like
data("hardlyworking", package = "effectsize")
head(hardlyworking)
>     salary xtra_hours n_comps age seniority
> 1 19744.65   4.161812       1  32         3
> 2 11301.95   1.621868       0  34         3
> 3 20635.62   1.190859       3  33         5
> 4 23047.16   7.190997       1  35         3
> 5 27342.15  11.261399       0  33         4
> 6 25656.63   3.633346       2  30         5
# let's use both methods of standardization
hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours)
hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE)

We can see that different methods give different central and variation values:

library(dplyr)
library(tidyr)

hardlyworking %>%
  select(starts_with("xtra_hours")) %>%
  pivot_longer(everything()) %>%
  group_by(name) %>%
  summarise(
    mean = mean(value),
    sd = sd(value),
    median = median(value),
    mad = mad(value)
  )
name mean sd median mad
xtra_hours 3.9757 3.9035 2.7726 2.8479
xtra_hours_z 0.0000 1.0000 -0.3082 0.7296
xtra_hours_zr 0.4224 1.3706 0.0000 1.0000

standardize() can also be used to standardize a full data frame - where each numeric variable is standardized separately:

hardlyworking_z <- standardize(hardlyworking)
hardlyworking_z %>%
  select(-xtra_hours_z, -xtra_hours_zr) %>%
  pivot_longer(everything()) %>%
  group_by(name) %>%
  summarise(
    mean = mean(value),
    sd = sd(value),
    median = median(value),
    mad = mad(value)
  )
name mean sd median mad
age 0 1 0.0605 1.2461
n_comps 0 1 -0.0606 1.7968
salary 0 1 -0.1619 0.8640
seniority 0 1 -0.3681 1.2931
xtra_hours 0 1 -0.3082 0.7296

Weighted standardization is also supported via the weights argument, and factors can also be standardized (if you’re into that kind of thing) by setting force = TRUE, which converts factors to treatment-coded dummy variables before standardizing.

Variable-wise vs. Participant-wise

Standardization is an important step and extra caution is required in repeated-measures designs, in which there are three ways of standardizing data:

  • Variable-wise: The most common method. A simple scaling of each column.

  • Participant-wise: Variables are standardized “within” each participant, i.e., for each participant, by the participant’s mean and SD.

  • Full: Participant-wise first and then re-standardizing variable-wise.

Unfortunately, the method used is often not explicitly stated. This is an issue as these methods can generate important discrepancies (that can in turn contribute to the reproducibility crisis). Let’s investigate these 3 methods.

The Data

We will take the emotion dataset in which participants were exposed to negative pictures and had to rate their emotions (valence) and the amount of memories associated with the picture (autobiographical link). One could make the hypothesis that for young participants with no context of war or violence, the most negative pictures (mutilations) are less related to memories than less negative pictures (involving for example car crashes or sick people). In other words, we expect a positive relationship between valence (with high values corresponding to less negativity) and autobiographical link.

Let’s have a look at the data, averaged by participants:

# Download the 'emotion' dataset
load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda"))

# Discard neutral pictures (keep only negative)
emotion <- emotion %>% filter(Emotion_Condition == "Negative")

# Summary
emotion %>%
  drop_na(Subjective_Valence, Autobiographical_Link) %>%
  group_by(Participant_ID) %>%
  summarise(
    n_Trials = n(),
    Valence_Mean = mean(Subjective_Valence),
    Valence_SD = sd(Subjective_Valence)
  )
> # A tibble: 19 x 4
> # Groups:   Participant_ID [19]
>    Participant_ID n_Trials Valence_Mean Valence_SD
>    <fct>             <int>        <dbl>      <dbl>
>  1 10S                  24       -58.1       42.6 
>  2 11S                  24       -73.2       37.0 
>  3 12S                  24       -57.5       26.6 
>  4 13S                  24       -63.2       23.7 
>  5 14S                  24       -56.6       26.5 
>  6 15S                  24       -60.6       33.7 
>  7 16S                  24       -46.1       24.9 
>  8 17S                  24        -1.54       4.98
>  9 18S                  24       -67.2       35.0 
> 10 19S                  24       -59.6       33.2 
> 11 1S                   24       -53.0       42.9 
> 12 2S                   23       -43.0       39.2 
> 13 3S                   24       -64.3       34.4 
> 14 4S                   24       -81.6       27.6 
> 15 5S                   24       -58.1       25.3 
> 16 6S                   24       -74.7       29.2 
> 17 7S                   24       -62.3       39.7 
> 18 8S                   24       -56.9       32.7 
> 19 9S                   24       -31.5       52.7

As we can see from the means and SDs, there is a lot of variability between participants both in their means and their individual within-participant SD.

Effect of Standardization

We will create three data frames standardized with each of the three techniques.

Z_VariableWise <- emotion %>%
  standardize()

Z_ParticipantWise <- emotion %>%
  group_by(Participant_ID) %>%
  standardize()

Z_Full <- emotion %>%
  group_by(Participant_ID) %>%
  standardize() %>%
  ungroup() %>%
  standardize()

Let’s see how these three standardization techniques affected the Valence variable.

Across Participants

We can calculate the mean and SD of Valence across all participants:

# Create a convenient function to print
summarise_Subjective_Valence <- function(data) {
  df_name <- deparse(substitute(data))
  data %>%
    ungroup() %>%
    summarise(
      DF = df_name,
      Mean = mean(Subjective_Valence),
      SD = sd(Subjective_Valence)
    )
}
# Check the results
rbind(
  summarise_Subjective_Valence(Z_VariableWise),
  summarise_Subjective_Valence(Z_ParticipantWise),
  summarise_Subjective_Valence(Z_Full)
)
DF Mean SD
Z_VariableWise 0 1.00
Z_ParticipantWise 0 0.98
Z_Full 0 1.00

The means and the SD appear as fairly similar (0 and 1)…

library(see)
library(ggplot2)

ggplot() +
  geom_density(aes(Z_VariableWise$Subjective_Valence,
    color = "Z_VariableWise"
  ), size = 1) +
  geom_density(aes(Z_ParticipantWise$Subjective_Valence,
    color = "Z_ParticipantWise"
  ), size = 1) +
  geom_density(aes(Z_Full$Subjective_Valence,
    color = "Z_Full"
  ), size = 1) +
  see::theme_modern() +
  labs(color = "")

and so do the marginal distributions…

At the Participant Level

However, we can also look at what happens in the participant level. Let’s look at the first 5 participants:

# Create convenient function
print_participants <- function(data) {
  df_name <- deparse(substitute(data))
  data %>%
    group_by(Participant_ID) %>%
    summarise(
      DF = df_name,
      Mean = mean(Subjective_Valence),
      SD = sd(Subjective_Valence)
    ) %>%
    head(5) %>%
    select(DF, everything())
}

# Check the results
rbind(
  print_participants(Z_VariableWise),
  print_participants(Z_ParticipantWise),
  print_participants(Z_Full)
)
DF Participant_ID Mean SD
Z_VariableWise 10S -0.05 1.15
Z_VariableWise 11S -0.46 1.00
Z_VariableWise 12S -0.03 0.72
Z_VariableWise 13S -0.19 0.64
Z_VariableWise 14S -0.01 0.71
Z_ParticipantWise 10S 0.00 1.00
Z_ParticipantWise 11S 0.00 1.00
Z_ParticipantWise 12S 0.00 1.00
Z_ParticipantWise 13S 0.00 1.00
Z_ParticipantWise 14S 0.00 1.00
Z_Full 10S 0.00 1.02
Z_Full 11S 0.00 1.02
Z_Full 12S 0.00 1.02
Z_Full 13S 0.00 1.02
Z_Full 14S 0.00 1.02

Seems like full and participant-wise standardization give similar results, but different ones than variable-wise standardization.

Compare

Let’s do a correlation between the variable-wise and participant-wise methods.

r <- cor.test(
  Z_VariableWise$Subjective_Valence,
  Z_ParticipantWise$Subjective_Valence
)

data.frame(
  Original = emotion$Subjective_Valence,
  VariableWise = Z_VariableWise$Subjective_Valence,
  ParticipantWise = Z_ParticipantWise$Subjective_Valence
) %>%
  ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) +
  geom_point(alpha = 0.75, shape = 16) +
  geom_smooth(method = "lm", color = "black") +
  scale_color_distiller(palette = 1) +
  ggtitle(paste0("r = ", round(r$estimate, 2))) +
  see::theme_modern()

While the three standardization methods roughly present the same characteristics at a general level (mean 0 and SD 1) and a similar distribution, their values are not exactly the same!

Let’s now answer the original question by investigating the linear relationship between valence and autobiographical link. We can do this by running a mixed-effects model with participants entered as random effects.

library(lme4)
m_raw <- lmer(
  formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID),
  data = emotion
)
m_VariableWise <- update(m_raw, data = Z_VariableWise)
m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise)
m_Full <- update(m_raw, data = Z_Full)

We can extract the parameters of interest from each model, and find:

# Convenient function
get_par <- function(model) {
  mod_name <- deparse(substitute(model))
  parameters::model_parameters(model) %>%
    mutate(Model = mod_name) %>%
    select(-Parameter) %>%
    select(Model, everything()) %>%
    .[-1, ]
}

# Run the model on all datasets
rbind(
  get_par(m_raw),
  get_par(m_VariableWise),
  get_par(m_ParticipantWise),
  get_par(m_Full)
)
> # Fixed Effects
> 
> Model             | Coefficient |   SE |        95% CI | t(451) |     p
> -----------------------------------------------------------------------
> m_raw             |        0.09 | 0.07 | [-0.04, 0.22] |   1.36 | 0.174
> m_VariableWise    |        0.07 | 0.05 | [-0.03, 0.17] |   1.36 | 0.174
> m_ParticipantWise |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080
> m_Full            |        0.08 | 0.05 | [-0.01, 0.17] |   1.75 | 0.080
> 
> # Random Effects: Participant_ID
> 
> Model             | Coefficient
> -------------------------------
> m_raw             |       16.49
> m_VariableWise    |        0.45
> m_ParticipantWise |        0.00
> m_Full            |        0.00
> 
> # Random Effects: Residual
> 
> Model             | Coefficient
> -------------------------------
> m_raw             |       33.56
> m_VariableWise    |        0.91
> m_ParticipantWise |        0.98
> m_Full            |        1.00

As we can see, variable-wise standardization only affects the coefficient (which is expected, as it changes the unit), but not the test statistic or statistical significance. However, using participant-wise standardization does affect the coefficient and the significance.

No method is better or more justified, and the choice depends on the specific case, context, data and goal.

Conclusion

  1. Standardization can be useful in some cases and should be justified.

  2. Variable and Participant-wise standardization methods appear to produce similar data.

  3. Variable and Participant-wise standardization can lead to different results.

  4. The chosen method can strongly influence the results and should therefore be explicitly stated and justified to enhance reproducibility of results.

We showed here yet another way of sneakily tweaking the data that can change the results. To prevent its use as a bad practice, we can only highlight the importance of open data, open analysis/scripts, and preregistration.

See also

References

datawizard/inst/doc/demean.Rmd0000644000176200001440000004651614141234536016042 0ustar liggesusers--- title: "Analysing Longitudinal or Panel Data" output: rmarkdown::html_vignette: toc: true fig_width: 10.08 fig_height: 6 vignette: > %\VignetteIndexEntry{Analysing Longitudinal or Panel Data} \usepackage[utf8]{inputenc} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console bibliography: bibliography.bib --- ```{r , include=FALSE} library(knitr) knitr::opts_chunk$set( echo = TRUE, collapse = TRUE, warning = FALSE, message = FALSE, comment = "#>", out.width = "100%" ) pkgs <- c( "datawizard", "poorman", "tidyr", "see", "ggplot2", "parameters", "performance", "lme4", "lfe" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("parameters") >= "0.14.1") { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("performance") >= "0.7.4") { knitr::opts_chunk$set(eval = FALSE) } set.seed(333) ``` This vignette explains the rational behind the `demean()` function. We give recommendations how to analyze multilevel or hierarchical data structures, when macro-indicators (or level-2 predictors, or higher-level units, or more general: _group-level predictors_) are used as covariates and the model suffers from **heterogeneity bias** [@bell_explaining_2015]. # Sample data used in this vignette ```{r} library(parameters) data("qol_cancer") ``` * Variables: * `QoL` : Response (quality of life of patient) * `phq4` : Patient Health Questionnaire, **time-varying** variable * `hospital` : Location of treatment, **time-invariant** variable, co-variate * `education`: Educational level, **time-invariant** variable, co-variate * `ID` : patient ID * `time` : time-point of measurement # Heterogeneity bias Heterogeneity bias occurs when group-level predictors vary within and across groups, and hence fixed effects may correlate with group (or random) effects. This is a typical situation when analyzing longitudinal or panel data: Due to the repeated measurements of persons, the "person" (or subject-ID) is now a level-2 variable. Predictors at level-1 ("fixed effects"), e.g. self-rated health or income, now have an effect at level-1 ("within"-effect) and at higher-level units (level-2, the subject-level, which is the "between"-effect) (see also [this posting](https://shouldbewriting.netlify.app/posts/2019-10-21-accounting-for-within-and-between-subject-effect/)). This inevitably leads to correlating fixed effects and error terms - which, in turn, results in biased estimates, because both the within- *and* between-effect are captured in *one* estimate. You can check if your model may suffer from heterogeneity bias using the `check_heterogeneity_bias()` function: ```{r} library(performance) check_heterogeneity_bias(qol_cancer, select = c("phq4", "education"), group = "ID") ``` # Adressing heterogeneity bias: the Fixed Effects Regression (FE) approach Fixed effects regression models (FE) are a popular approach for panel data analysis in particular in econometrics and considered as gold standard. To avoid the problem of heterogeneity bias, in FE all higher-level variance (and thus, any between-effects), "are controlled out using the higher-level entities themselves, included in the model as dummy variables" [@bell_explaining_2015]. As a consequence, FE models are only able estimate _within-effects_. To remove between-effects and only model within-effects, the data needs some preparation: _de-meaning_. De-meaning, or _person-mean centering_, or _centering within clusters_, takes away the higher-level mean from the regression equation, and as such, FE avoids estimating a parameter for each higher-level unit. ## Computing the de-meaned and group-meaned variables ```{r} qol_cancer <- cbind( qol_cancer, datawizard::demean(qol_cancer, select = c("phq4", "QoL"), group = "ID") ) ``` Now we have: * `phq4_between`: time-varying variable with the mean of `phq4` across all time-points, for each patient (ID). * `phq4_within`: the de-meaned time-varying variable `phq4`. A FE model is a classical linear model, where * Intercept is removed * time-invariant predictors are not allowed to be included * the group-level factor is included as predictor * time-varying predictors are de-meaned ("person-mean centered", indicating the "within-subject" effect) ```{r} fe_model1 <- lm( QoL ~ 0 + time + phq4_within + ID, data = qol_cancer ) # we use only the first two rows, because the remaining rows are # the estimates for "ID", which is not of interest here... model_parameters(fe_model1)[1:2, ] # instead of removing the intercept, we could also use the # de-meaned response... fe_model2 <- lm( QoL_within ~ time + phq4_within + ID, data = qol_cancer ) model_parameters(fe_model2)[2:3, ] # we compare the results with those from the "lfe"-package for panel data library(lfe) fe_model3 <- felm( QoL ~ time + phq4 | ID, data = qol_cancer ) model_parameters(fe_model3) ``` As we can see, the _within-effect_ of PHQ-4 is `-3.66`, hence the mean of the change for an average individual case in our sample (or, the "net" effect), is `-3.66`. But what about the between-effect? How do people with higher PHQ-4 score differ from people with lower PHQ-4 score? Or what about educational inequalities? Do higher educated people have a higher PHQ-4 score than lower educated people? This question cannot be answered with FE regression. But: "Can one fit a multilevel model with varying intercepts (or coefficients) when the units and predictors correlate? The answer is yes. And the solution is simple." [@bafumi_fitting_2006] # Adressing heterogeneity bias: the Mixed Model approach Mixed models include different levels of sources of variability (i.e. error terms at each level). Predictors used at level-1 that are varying across higher-level units will thus have residual errors at both level-1 and higher-level units. "Such covariates contain two parts: one that is specific to the higher-level entity that does not vary between occasions, and one that represents the difference between occasions, within higher-level entities" [@bell_explaining_2015]. Hence, the error terms will be correlated with the covariate, which violates one of the assumptions of mixed models (iid, independent and identically distributed error terms) - also known and described above as _heterogeneity bias_. But how can this issue be addressed outside the FE framework? There are several ways how to address this using a mixed models approach: * Correlated group factors and predictors are no problem anyway, because [partial pooling](https://www.tjmahr.com/plotting-partial-pooling-in-mixed-effects-models/) allows estimates of units o borrow strength from the whole sample and shrink toward a common mean (@shor_bayesian_2007). * If predictor and group factors correlate, one can remove this correlation by group-meaning [or "mean within clusters", @bafumi_fitting_2006; @gelman_data_2007, Chap. 12.6.]. * When time-varying predictors are "decomposed" into their time-varying and time-invariant components (demeaning), then mixed models can model **both** within- and between-subject effects [@bell_fixed_2019] - this approach is essentially a further development of a long-known recommendation by Mundlak [@mundlak_pooling_1978]. For now, we will follow the last recommendation and use the within- and between-version of `phq4`. ```{r} library(lme4) mixed_1 <- lmer( QoL ~ time + phq4_within + phq4_between + (1 | ID), data = qol_cancer ) model_parameters(mixed_1) # compare to FE-model model_parameters(fe_model1)[1:2, ] ``` As we can see, the estimates and standard errors are identical. The argument _against_ the use of mixed models, i.e. that using mixed models for panel data will yield biased estimates and standard errors, is based on an incorrect model specification [@mundlak_pooling_1978]. As such, when the (mixed) model is properly specified, the estimator of the mixed model is identical to the 'within' (i.e. FE) estimator. As a consequence, we cannot only use the above specified mixed model for panel data, we can even specify more complex models including within-effects, between-effects or random effects variation. A mixed models approach can model the causes of endogeneity explicitly by including the (separated) within- and between-effects of time-varying fixed effects and including time-constant fixed effects. ```{r} mixed_2 <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID), data = qol_cancer ) # effects = "fixed" will not display random effects, but split the # fixed effects into its between- and within-effects components. model_parameters(mixed_2, effects = "fixed") ``` For more complex models, within-effects will naturally change slightly and are no longer identical to simpler FE models. This is no "bias", but rather the result of building more complex models: FE models lack information of variation in the group-effects or between-subject effects. Furthermore, FE models cannot include random slopes, which means that fixed effects regressions are neglecting "cross-cluster differences in the effects of lower-level controls (which) reduces the precision of estimated context effects, resulting in (...) low statistical power" [@heisig_costs_2017]. # Conclusion: Complex Random Effects Within-Between Models Depending on the structure of the data, the best approach to analyzing panel data would be a so called "complex random effects within-between" model [@bell_fixed_2019]: ```{r echo=FALSE} f <- "yit = β0 + β1W (xit - ͞xi) + β2B ͞xi + β3 zi + υi0 + υi1 (xit - ͞xi) + εit" knitr::asis_output(f) ``` ```{r echo=FALSE} f <- "
  • xit - ͞xi is the de-meaned predictor, phq4_within
  • ͞xi is the group-meaned predictor, phq4_between
  • β1W is the coefficient for phq4_within (within-subject)
  • β2B is the coefficient for phq4_between (bewteen-subject)
  • β3 is the coefficient for time-constant predictors, such as `hospital` or `education` (bewteen-subject)
" knitr::asis_output(f) ``` In R-code, the model is written down like this: ```{r} rewb <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID) + (1 + phq4_within | ID), data = qol_cancer ) ``` **What about time-constant predictors?** After demeaning time-varying predictors, "at the higher level, the mean term is no longer constrained by Level 1 effects, so it is free to account for all the higher-level variance associated with that variable" [@bell_explaining_2015]. Thus, _time-constant **categorical**_ predictors, that only have a between-effect, can be simply included as fixed effects predictor (since they’re not constrained by level-1 effects). Time-constant _continuous_ **group-level predictors** (for instance, GDP of countries) should be group-meaned, to have a proper "between"-effect [@gelman_data_2007, Chap. 12.6.]. The benefit of this kind of model is that you have information on within-, between- and other time-constant (i.e. between) effects or group-level predictors... ```{r} model_parameters(rewb, effects = "fixed") ``` ... but you can also model the variation of (group) effects across time (and probably space), and you can even include more higher-level units (e.g. nested design or cross-classified design with more than two levels): ```{r} random_parameters(rewb) ``` **What about imbalanced groups, i.e. large differences in N per group?** See little example after this visual example... # A visual example First, we generate some fake data that implies a linear relationship between outcome and independent variable. The objective is that the amount of typing errors depends on how fast (typing speed) you can type, however, the more typing experience you have, the faster you can type. Thus, the outcome measure is "amount of typing errors", while our predictor is "typing speed". Furthermore, we have repeated measurements of people with different "typing experience levels". The results show that we will have two sources of variation: Overall, more experienced typists make less mistakes (group-level pattern). When typing faster, typists make more mistakes (individual-level pattern). ```{r} library(ggplot2) library(poorman) library(see) set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) ``` Let's look at the raw data... ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` ## Model 1: Linear relationship between typing errors and typing speed We can now assume a (linear) relationship between typing errors and typing speed. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` Looking at the coefficients, we have following model with a coefficient of `-1.92`. ```{r} m1 <- lm(y ~ x, data = d) model_parameters(m1) ``` However, we have ignored the clustered structure in our data, in this example due to repeated measurements. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` ## Model 2: Within-subject effect of typing speed A fixed effects regression (FE-regression) would now remove all between-effects and include only the within-effects as well as the group-level indicator. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` This returns the coefficient of the "within"-effect, which is `1.2`, with a standard error of `0.07`. Note that the FE-model does *not* take the variation *between* subjects into account, thus resulting in (possibly) biased estimates, and biased standard errors. ```{r} m2 <- lm(y ~ 0 + x_within + grp, data = d) model_parameters(m2)[1, ] ``` ## Model 3: Between-subject effect of typing speed To understand, why the above model 1 (`m1`) returns a biased estimate, which is a "weighted average" of the within- and between-effects, let us look at the between-effect now. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` As we can see, the between-effect is `-2.93`, which is different from the `-1.92` estimated in the model `m1`. ```{r} m3 <- lm(y ~ x_between, data = d) model_parameters(m3) ``` ## Model 4: Mixed model with within- and between-subjects Since FE-models can only model within-effects, we now use a mixed model with within- and between-effects. ```{r echo=FALSE} ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ``` We see, the estimate for the within-effects is *not* biased. Furthermore, we get the correct between-effect as well (standard errors differ, because the variance in the grouping structure is more accurately taken into account). ```{r} m4 <- lmer(y ~ x_between + x_within + (1 | grp), data = d) model_parameters(m4) ``` ## Model 5: Complex Random-Effects Within-Between Model Finally, we can also take the variation between subjects into account by adding a random slope. This model can be called a complex "REWB" (random-effects within-between) model. Due to the variation between subjects, we get larger standard errors for the within-effect. ```{r} m5 <- lmer(y ~ x_between + x_within + (1 + x_within | grp), data = d) model_parameters(m5) ``` # Balanced versus imbalanced groups The "simple" linear slope of the between-effect (and also from the within-effect) is (almost) identical in "classical" linear regression compared to linear mixed models when the groups are balanced, i.e. when the number of observation per group is similar or the same. Whenever group size is imbalanced, the "simple" linear slope will be adjusted. This leads to different estimates for between-effects between classical and mixed models regressions due to shrinkage - i.e. for larger variation of group sizes we find stronger regularization of estimates. Hence, for mixed models with larger differences in number of observation per random effects group, the between-effect will differ from the between-effect calculated by "classical" regression models. However, this shrinkage is a desired property of mixed models and usually improves the estimates. ```{r} set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) # create imbalanced groups d$grp[sample(which(d$grp == 8), 10)] <- 6 d$grp[sample(which(d$grp == 4), 8)] <- 2 d$grp[sample(which(d$grp == 10), 9)] <- 6 d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) # Between-subject effect of typing speed m1 <- lm(y ~ x_between, data = d) model_parameters(m1) # Between-subject effect of typing speed, accounting for group structure m2 <- lmer(y ~ x_between + (1 | grp), data = d) model_parameters(m2) ``` # References datawizard/inst/doc/demean.html0000644000176200001440000042307514174263635016273 0ustar liggesusers Analysing Longitudinal or Panel Data

Analysing Longitudinal or Panel Data

This vignette explains the rational behind the demean() function.

We give recommendations how to analyze multilevel or hierarchical data structures, when macro-indicators (or level-2 predictors, or higher-level units, or more general: group-level predictors) are used as covariates and the model suffers from heterogeneity bias (Bell and Jones 2015).

Sample data used in this vignette

library(parameters)
data("qol_cancer")

  • Variables:

    • QoL : Response (quality of life of patient)

    • phq4 : Patient Health Questionnaire, time-varying variable

    • hospital : Location of treatment, time-invariant variable, co-variate

    • education: Educational level, time-invariant variable, co-variate

    • ID : patient ID

    • time : time-point of measurement

Heterogeneity bias

Heterogeneity bias occurs when group-level predictors vary within and across groups, and hence fixed effects may correlate with group (or random) effects. This is a typical situation when analyzing longitudinal or panel data: Due to the repeated measurements of persons, the “person” (or subject-ID) is now a level-2 variable. Predictors at level-1 (“fixed effects”), e.g. self-rated health or income, now have an effect at level-1 (“within”-effect) and at higher-level units (level-2, the subject-level, which is the “between”-effect) (see also this posting). This inevitably leads to correlating fixed effects and error terms - which, in turn, results in biased estimates, because both the within- and between-effect are captured in one estimate.

You can check if your model may suffer from heterogeneity bias using the check_heterogeneity_bias() function:

library(performance)
check_heterogeneity_bias(qol_cancer, select = c("phq4", "education"), group = "ID")
#> Possible heterogeneity bias due to following predictors: phq4

Adressing heterogeneity bias: the Fixed Effects Regression (FE) approach

Fixed effects regression models (FE) are a popular approach for panel data analysis in particular in econometrics and considered as gold standard. To avoid the problem of heterogeneity bias, in FE all higher-level variance (and thus, any between-effects), “are controlled out using the higher-level entities themselves, included in the model as dummy variables” (Bell and Jones 2015). As a consequence, FE models are only able estimate within-effects.

To remove between-effects and only model within-effects, the data needs some preparation: de-meaning. De-meaning, or person-mean centering, or centering within clusters, takes away the higher-level mean from the regression equation, and as such, FE avoids estimating a parameter for each higher-level unit.

Computing the de-meaned and group-meaned variables

qol_cancer <- cbind(
  qol_cancer,
  datawizard::demean(qol_cancer, select = c("phq4", "QoL"), group = "ID")
)

Now we have:

  • phq4_between: time-varying variable with the mean of phq4 across all time-points, for each patient (ID).

  • phq4_within: the de-meaned time-varying variable phq4.

A FE model is a classical linear model, where

  • Intercept is removed

  • time-invariant predictors are not allowed to be included

  • the group-level factor is included as predictor

  • time-varying predictors are de-meaned (“person-mean centered”, indicating the “within-subject” effect)

fe_model1 <- lm(
  QoL ~ 0 + time + phq4_within + ID,
  data = qol_cancer
)
# we use only the first two rows, because the remaining rows are
# the estimates for "ID", which is not of interest here...
model_parameters(fe_model1)[1:2, ]
#> Parameter   | Coefficient |   SE |         95% CI | t(374) |      p
#> -------------------------------------------------------------------
#> time        |        1.09 | 0.64 | [-0.17,  2.34] |   1.70 | 0.089 
#> phq4 within |       -3.66 | 0.41 | [-4.46, -2.86] |  -8.95 | < .001


# instead of removing the intercept, we could also use the
# de-meaned response...
fe_model2 <- lm(
  QoL_within ~ time + phq4_within + ID,
  data = qol_cancer
)
model_parameters(fe_model2)[2:3, ]
#> Parameter   | Coefficient |   SE |         95% CI | t(374) |      p
#> -------------------------------------------------------------------
#> time        |        1.09 | 0.64 | [-0.17,  2.34] |   1.70 | 0.089 
#> phq4 within |       -3.66 | 0.41 | [-4.46, -2.86] |  -8.95 | < .001

# we compare the results with those from the "lfe"-package for panel data
library(lfe)
fe_model3 <- felm(
  QoL ~ time + phq4 | ID,
   data = qol_cancer
)
model_parameters(fe_model3)
#> # Fixed Effects
#> 
#> Parameter | Coefficient |   SE |         95% CI | t(374) |      p
#> -----------------------------------------------------------------
#> time      |        1.09 | 0.64 | [-0.17,  2.34] |   1.70 | 0.089 
#> phq4      |       -3.66 | 0.41 | [-4.46, -2.86] |  -8.95 | < .001

As we can see, the within-effect of PHQ-4 is -3.66, hence the mean of the change for an average individual case in our sample (or, the “net” effect), is -3.66.

But what about the between-effect? How do people with higher PHQ-4 score differ from people with lower PHQ-4 score? Or what about educational inequalities? Do higher educated people have a higher PHQ-4 score than lower educated people?

This question cannot be answered with FE regression. But: “Can one fit a multilevel model with varying intercepts (or coefficients) when the units and predictors correlate? The answer is yes. And the solution is simple.” (Bafumi and Gelman 2006)

Adressing heterogeneity bias: the Mixed Model approach

Mixed models include different levels of sources of variability (i.e. error terms at each level). Predictors used at level-1 that are varying across higher-level units will thus have residual errors at both level-1 and higher-level units. “Such covariates contain two parts: one that is specific to the higher-level entity that does not vary between occasions, and one that represents the difference between occasions, within higher-level entities” (Bell and Jones 2015). Hence, the error terms will be correlated with the covariate, which violates one of the assumptions of mixed models (iid, independent and identically distributed error terms) - also known and described above as heterogeneity bias.

But how can this issue be addressed outside the FE framework?

There are several ways how to address this using a mixed models approach:

  • Correlated group factors and predictors are no problem anyway, because partial pooling allows estimates of units o borrow strength from the whole sample and shrink toward a common mean (Shor et al. (2007)).

  • If predictor and group factors correlate, one can remove this correlation by group-meaning (or “mean within clusters,” Bafumi and Gelman 2006; Gelman and Hill 2007, chap. 12.6.).

  • When time-varying predictors are “decomposed” into their time-varying and time-invariant components (demeaning), then mixed models can model both within- and between-subject effects (Bell, Fairbrother, and Jones 2019) - this approach is essentially a further development of a long-known recommendation by Mundlak (Mundlak 1978).

For now, we will follow the last recommendation and use the within- and between-version of phq4.

library(lme4)
mixed_1 <- lmer(
  QoL ~ time + phq4_within + phq4_between + (1 | ID),
  data = qol_cancer
)
model_parameters(mixed_1)
#> # Fixed Effects
#> 
#> Parameter    | Coefficient |   SE |         95% CI | t(558) |      p
#> --------------------------------------------------------------------
#> (Intercept)  |       71.53 | 1.56 | [68.48, 74.59] |  45.98 | < .001
#> time         |        1.09 | 0.64 | [-0.17,  2.34] |   1.70 | 0.089 
#> phq4 within  |       -3.66 | 0.41 | [-4.46, -2.86] |  -8.95 | < .001
#> phq4 between |       -6.28 | 0.50 | [-7.27, -5.30] | -12.53 | < .001
#> 
#> # Random Effects
#> 
#> Parameter          | Coefficient
#> --------------------------------
#> SD (Intercept: ID) |        9.88
#> SD (Residual)      |       12.37

# compare to FE-model
model_parameters(fe_model1)[1:2, ]
#> Parameter   | Coefficient |   SE |         95% CI | t(374) |      p
#> -------------------------------------------------------------------
#> time        |        1.09 | 0.64 | [-0.17,  2.34] |   1.70 | 0.089 
#> phq4 within |       -3.66 | 0.41 | [-4.46, -2.86] |  -8.95 | < .001

As we can see, the estimates and standard errors are identical. The argument against the use of mixed models, i.e. that using mixed models for panel data will yield biased estimates and standard errors, is based on an incorrect model specification (Mundlak 1978). As such, when the (mixed) model is properly specified, the estimator of the mixed model is identical to the ‘within’ (i.e. FE) estimator.

As a consequence, we cannot only use the above specified mixed model for panel data, we can even specify more complex models including within-effects, between-effects or random effects variation. A mixed models approach can model the causes of endogeneity explicitly by including the (separated) within- and between-effects of time-varying fixed effects and including time-constant fixed effects.

mixed_2 <- lmer(
  QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID),
  data = qol_cancer
)
# effects = "fixed" will not display random effects, but split the
# fixed effects into its between- and within-effects components.
model_parameters(mixed_2, effects = "fixed")
#> Parameter        | Coefficient |   SE |         95% CI | t(554) |      p
#> ------------------------------------------------------------------------
#> (Intercept)      |       67.36 | 2.48 | [62.48, 72.23] |  27.15 | < .001
#> time             |        1.09 | 0.66 | [-0.21,  2.39] |   1.65 | 0.099 
#> education [mid]  |        5.01 | 2.35 | [ 0.40,  9.62] |   2.14 | 0.033 
#> education [high] |        5.52 | 2.75 | [ 0.11, 10.93] |   2.00 | 0.046 
#> 
#> # Within-Effects
#> 
#> Parameter   | Coefficient |   SE |         95% CI | t(554) |      p
#> -------------------------------------------------------------------
#> phq4 within |       -3.72 | 0.41 | [-4.52, -2.92] |  -9.10 | < .001
#> 
#> # Between-Effects
#> 
#> Parameter    | Coefficient |   SE |         95% CI | t(554) |      p
#> --------------------------------------------------------------------
#> phq4 between |       -6.13 | 0.52 | [-7.14, -5.11] | -11.84 | < .001

For more complex models, within-effects will naturally change slightly and are no longer identical to simpler FE models. This is no “bias”, but rather the result of building more complex models: FE models lack information of variation in the group-effects or between-subject effects. Furthermore, FE models cannot include random slopes, which means that fixed effects regressions are neglecting “cross-cluster differences in the effects of lower-level controls (which) reduces the precision of estimated context effects, resulting in (…) low statistical power” (Heisig, Schaeffer, and Giesecke 2017).

Conclusion: Complex Random Effects Within-Between Models

Depending on the structure of the data, the best approach to analyzing panel data would be a so called “complex random effects within-between” model (Bell, Fairbrother, and Jones 2019):

yit = β0 + β1W (xit - ͞xi) + β2B ͞xi + β3 zi + υi0 + υi1 (xit - ͞xi) + εit

  • xit - ͞xi is the de-meaned predictor, phq4_within
  • ͞xi is the group-meaned predictor, phq4_between
  • β1W is the coefficient for phq4_within (within-subject)
  • β2B is the coefficient for phq4_between (bewteen-subject)
  • β3 is the coefficient for time-constant predictors, such as hospital or education (bewteen-subject)

In R-code, the model is written down like this:

rewb <- lmer(
  QoL ~ time + phq4_within + phq4_between + education +
    (1 + time | ID) + (1 + phq4_within | ID),
  data = qol_cancer
)

What about time-constant predictors?

After demeaning time-varying predictors, “at the higher level, the mean term is no longer constrained by Level 1 effects, so it is free to account for all the higher-level variance associated with that variable” (Bell and Jones 2015).

Thus, time-constant categorical predictors, that only have a between-effect, can be simply included as fixed effects predictor (since they’re not constrained by level-1 effects). Time-constant continuous group-level predictors (for instance, GDP of countries) should be group-meaned, to have a proper “between”-effect (Gelman and Hill 2007, chap. 12.6.).

The benefit of this kind of model is that you have information on within-, between- and other time-constant (i.e. between) effects or group-level predictors…

model_parameters(rewb, effects = "fixed")
#> Parameter        | Coefficient |   SE |         95% CI | t(551) |      p
#> ------------------------------------------------------------------------
#> (Intercept)      |       67.18 | 2.39 | [62.49, 71.87] |  28.13 | < .001
#> time             |        1.18 | 0.60 | [-0.01,  2.37] |   1.95 | 0.051 
#> education [mid]  |        4.95 | 2.35 | [ 0.34,  9.56] |   2.11 | 0.035 
#> education [high] |        5.62 | 2.76 | [ 0.20, 11.04] |   2.04 | 0.042 
#> 
#> # Within-Effects
#> 
#> Parameter   | Coefficient |   SE |         95% CI | t(551) |      p
#> -------------------------------------------------------------------
#> phq4 within |       -4.50 | 0.58 | [-5.64, -3.36] |  -7.78 | < .001
#> 
#> # Between-Effects
#> 
#> Parameter    | Coefficient |   SE |         95% CI | t(551) |      p
#> --------------------------------------------------------------------
#> phq4 between |       -6.11 | 0.52 | [-7.13, -5.10] | -11.81 | < .001

… but you can also model the variation of (group) effects across time (and probably space), and you can even include more higher-level units (e.g. nested design or cross-classified design with more than two levels):

random_parameters(rewb)
#> # Random Effects
#> 
#> Within-Group Variance              119.47 (10.93)
#> Between-Group Variance
#>   Random Intercept (ID)             107.5 (10.37)
#>   Random Intercept (ID.1)           25.76  (5.08)
#>   Random Slope (ID.time)             0.49   (0.7)
#>   Random Slope (ID.1.phq4_within)   14.37  (3.79)
#> Correlations
#>   ID.time                           -0.99
#>   ID.phq4_within                     0.44
#> N (groups per factor)
#>   ID                                  188
#> Observations                          564

What about imbalanced groups, i.e. large differences in N per group?

See little example after this visual example…

A visual example

First, we generate some fake data that implies a linear relationship between outcome and independent variable. The objective is that the amount of typing errors depends on how fast (typing speed) you can type, however, the more typing experience you have, the faster you can type. Thus, the outcome measure is “amount of typing errors”, while our predictor is “typing speed”. Furthermore, we have repeated measurements of people with different “typing experience levels”.

The results show that we will have two sources of variation: Overall, more experienced typists make less mistakes (group-level pattern). When typing faster, typists make more mistakes (individual-level pattern).

library(ggplot2)
library(poorman)
library(see)

set.seed(123)
n <- 5
b <- seq(1, 1.5, length.out = 5)
x <- seq(2, 2 * n, 2)

d <- do.call(rbind, lapply(1:n, function(i) {
  data.frame(
    x = seq(1, n, by = .2),
    y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21),
    grp = as.factor(2 * i)
  )
}))

d <- d %>%
  group_by(grp) %>%
  mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>%
  ungroup()

labs <- c("very slow", "slow", "average", "fast", "very fast")
levels(d$grp) <- rev(labs)

d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp"))

Let’s look at the raw data…

Model 1: Linear relationship between typing errors and typing speed

We can now assume a (linear) relationship between typing errors and typing speed.

Looking at the coefficients, we have following model with a coefficient of -1.92.

m1 <- lm(y ~ x, data = d)
model_parameters(m1)
#> Parameter   | Coefficient |   SE |         95% CI | t(103) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       30.20 | 1.42 | [27.39, 33.00] |  21.34 | < .001
#> x           |       -1.92 | 0.18 | [-2.27, -1.56] | -10.69 | < .001

However, we have ignored the clustered structure in our data, in this example due to repeated measurements.

Model 2: Within-subject effect of typing speed

A fixed effects regression (FE-regression) would now remove all between-effects and include only the within-effects as well as the group-level indicator.

This returns the coefficient of the “within”-effect, which is 1.2, with a standard error of 0.07. Note that the FE-model does not take the variation between subjects into account, thus resulting in (possibly) biased estimates, and biased standard errors.

m2 <- lm(y ~ 0 + x_within + grp, data = d)
model_parameters(m2)[1, ]
#> Parameter | Coefficient |   SE |       95% CI | t(99) |      p
#> --------------------------------------------------------------
#> x within  |        1.20 | 0.07 | [1.06, 1.35] | 16.08 | < .001

Model 3: Between-subject effect of typing speed

To understand, why the above model 1 (m1) returns a biased estimate, which is a “weighted average” of the within- and between-effects, let us look at the between-effect now.

As we can see, the between-effect is -2.93, which is different from the -1.92 estimated in the model m1.

m3 <- lm(y ~ x_between, data = d)
model_parameters(m3)
#> Parameter   | Coefficient |   SE |         95% CI | t(103) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       37.83 | 0.62 | [36.59, 39.06] |  60.79 | < .001
#> x between   |       -2.93 | 0.08 | [-3.09, -2.78] | -36.76 | < .001

Model 4: Mixed model with within- and between-subjects

Since FE-models can only model within-effects, we now use a mixed model with within- and between-effects.

We see, the estimate for the within-effects is not biased. Furthermore, we get the correct between-effect as well (standard errors differ, because the variance in the grouping structure is more accurately taken into account).

m4 <- lmer(y ~ x_between + x_within + (1 | grp), data = d)
model_parameters(m4)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |         95% CI | t(100) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       37.83 | 0.33 | [37.17, 38.48] | 114.46 | < .001
#> x between   |       -2.93 | 0.04 | [-3.02, -2.85] | -69.22 | < .001
#> x within    |        1.20 | 0.07 | [ 1.06,  1.35] |  16.22 | < .001
#> 
#> # Random Effects
#> 
#> Parameter           | Coefficient
#> ---------------------------------
#> SD (Intercept: grp) |        0.00
#> SD (Residual)       |        0.92

Model 5: Complex Random-Effects Within-Between Model

Finally, we can also take the variation between subjects into account by adding a random slope. This model can be called a complex “REWB” (random-effects within-between) model. Due to the variation between subjects, we get larger standard errors for the within-effect.

m5 <- lmer(y ~ x_between + x_within + (1 + x_within | grp), data = d)
model_parameters(m5)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |         95% CI |  t(98) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       37.95 | 0.34 | [37.28, 38.63] | 111.15 | < .001
#> x between   |       -2.95 | 0.04 | [-3.04, -2.87] | -67.57 | < .001
#> x within    |        1.20 | 0.10 | [ 1.01,  1.40] |  12.16 | < .001
#> 
#> # Random Effects
#> 
#> Parameter                     | Coefficient
#> -------------------------------------------
#> SD (Intercept: grp)           |        0.09
#> SD (x_within: grp)            |        0.15
#> Cor (Intercept~x_within: grp) |       -1.00
#> SD (Residual)                 |        0.90

Balanced versus imbalanced groups

The “simple” linear slope of the between-effect (and also from the within-effect) is (almost) identical in “classical” linear regression compared to linear mixed models when the groups are balanced, i.e. when the number of observation per group is similar or the same.

Whenever group size is imbalanced, the “simple” linear slope will be adjusted. This leads to different estimates for between-effects between classical and mixed models regressions due to shrinkage - i.e. for larger variation of group sizes we find stronger regularization of estimates.

Hence, for mixed models with larger differences in number of observation per random effects group, the between-effect will differ from the between-effect calculated by “classical” regression models. However, this shrinkage is a desired property of mixed models and usually improves the estimates.

set.seed(123)
n <- 5
b <- seq(1, 1.5, length.out = 5)
x <- seq(2, 2 * n, 2)

d <- do.call(rbind, lapply(1:n, function(i) {
  data.frame(
    x = seq(1, n, by = .2),
    y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21),
    grp = as.factor(2 * i)
  )
}))

# create imbalanced groups
d$grp[sample(which(d$grp == 8), 10)] <- 6
d$grp[sample(which(d$grp == 4), 8)] <- 2
d$grp[sample(which(d$grp == 10), 9)] <- 6

d <- d %>%
  group_by(grp) %>%
  mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>%
  ungroup()

labs <- c("very slow", "slow", "average", "fast", "very fast")
levels(d$grp) <- rev(labs)

d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp"))

# Between-subject effect of typing speed
m1 <- lm(y ~ x_between, data = d)
model_parameters(m1)
#> Parameter   | Coefficient |   SE |         95% CI | t(103) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       38.32 | 1.33 | [35.69, 40.95] |  28.87 | < .001
#> x between   |       -2.81 | 0.16 | [-3.13, -2.49] | -17.47 | < .001

# Between-subject effect of typing speed, accounting for group structure
m2 <- lmer(y ~ x_between + (1 | grp), data = d)
model_parameters(m2)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |         95% CI | t(101) |      p
#> -------------------------------------------------------------------
#> (Intercept) |       37.02 | 2.73 | [31.59, 42.44] |  13.54 | < .001
#> x between   |       -2.71 | 0.35 | [-3.40, -2.02] |  -7.81 | < .001
#> 
#> # Random Effects
#> 
#> Parameter           | Coefficient
#> ---------------------------------
#> SD (Intercept: grp) |        1.54
#> SD (Residual)       |        2.98

References

Bafumi, Joseph, and Andrew Gelman. 2006. “Fitting Multilevel Models When Predictors and Group Effects Correlate.” In. Philadelphia, PA.
Bell, Andrew, Malcolm Fairbrother, and Kelvyn Jones. 2019. “Fixed and Random Effects Models: Making an Informed Choice.” Quality & Quantity 53: 1051–74. https://doi.org/10.1007/s11135-018-0802-x.
Bell, Andrew, and Kelvyn Jones. 2015. “Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data.” Political Science Research and Methods 3 (1): 133–53. https://doi.org/10.1017/psrm.2014.7.
Gelman, Andrew, and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Analytical Methods for Social Research. Cambridge; New York: Cambridge University Press.
Heisig, Jan Paul, Merlin Schaeffer, and Johannes Giesecke. 2017. “The Costs of Simplicity: Why Multilevel Models May Benefit from Accounting for Cross-Cluster Differences in the Effects of Controls.” American Sociological Review 82 (4): 796–827. https://doi.org/10.1177/0003122417717901.
Mundlak, Yair. 1978. “On the Pooling of Time Series and Cross Section Data.” Econometrica 46 (1): 69.
Shor, Boris, Joseph Bafumi, Luke Keele, and David Park. 2007. “A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data.” Political Analysis 15 (2): 165–81. https://doi.org/10.1093/pan/mpm006.
datawizard/inst/doc/standardize_data.Rmd0000644000176200001440000003132014174263411020075 0ustar liggesusers--- title: "Data Standardization" output: rmarkdown::html_vignette: toc: true fig_width: 10.08 fig_height: 6 vignette: > \usepackage[utf8]{inputenc} %\VignetteIndexEntry{Data Standardization} %\VignetteEngine{knitr::rmarkdown} --- ```{r message=FALSE, warning=FALSE, include=FALSE} library(knitr) options(knitr.kable.NA = "") knitr::opts_chunk$set( comment = ">", message = FALSE, warning = FALSE, dpi = 300 ) pkgs <- c( "datawizard", "poorman", "see", "ggplot2", "parameters", "lme4" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } ``` # Introduction To make sense of their data and effects, scientists might want to standardize (Z-score) their variables. This makes the data unitless, expressed only in terms of deviation from an index of centrality (e.g., the mean or the median). However, aside from some benefits, standardization also comes with challenges and issues, that the scientist should be aware of. ## Methods of Standardization The `datawizard` package offers two methods of standardization via the `standardize()` function: - **Normal standardization**: center around the *mean*, with *SD* units (default). - **Robust standardization**: center around the *median*, with *MAD* (median absolute deviation) units (`robust = TRUE`). Let's look at the following example: ```{r} library(datawizard) library(effectsize) # for data # let's have a look at what the data look like data("hardlyworking", package = "effectsize") head(hardlyworking) # let's use both methods of standardization hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours) hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE) ``` We can see that different methods give different central and variation values: ```{r, eval=FALSE} library(dplyr) library(tidyr) hardlyworking %>% select(starts_with("xtra_hours")) %>% pivot_longer(everything()) %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) ``` ```{r, echo=FALSE} library(poorman) hardlyworking %>% select(starts_with("xtra_hours")) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ``` `standardize()` can also be used to standardize a full data frame - where each numeric variable is standardized separately: ```{r} hardlyworking_z <- standardize(hardlyworking) ``` ```{r, eval=FALSE} hardlyworking_z %>% select(-xtra_hours_z, -xtra_hours_zr) %>% pivot_longer(everything()) %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) ``` ```{r, echo=FALSE} hardlyworking_z %>% select(-xtra_hours_z, -xtra_hours_zr) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ``` Weighted standardization is also supported via the `weights` argument, and factors can also be standardized (if you're into that kind of thing) by setting `force = TRUE`, which converts factors to treatment-coded dummy variables before standardizing. ## Variable-wise *vs.* Participant-wise Standardization is an important step and extra caution is required in **repeated-measures designs**, in which there are three ways of standardizing data: - **Variable-wise**: The most common method. A simple scaling of each column. - **Participant-wise**: Variables are standardized "within" each participant, *i.e.*, for each participant, by the participant's mean and SD. - **Full**: Participant-wise first and then re-standardizing variable-wise. Unfortunately, the method used is often not explicitly stated. This is an issue as these methods can generate important discrepancies (that can in turn contribute to the reproducibility crisis). Let's investigate these 3 methods. ### The Data We will take the `emotion` dataset in which participants were exposed to negative pictures and had to rate their emotions (**valence**) and the amount of memories associated with the picture (**autobiographical link**). One could make the hypothesis that for young participants with no context of war or violence, the most negative pictures (mutilations) are less related to memories than less negative pictures (involving for example car crashes or sick people). In other words, **we expect a positive relationship between valence** (with high values corresponding to less negativity) **and autobiographical link**. Let's have a look at the data, averaged by participants: ```{r, eval=FALSE} # Download the 'emotion' dataset load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == "Negative") # Summary emotion %>% drop_na(Subjective_Valence, Autobiographical_Link) %>% group_by(Participant_ID) %>% summarise( n_Trials = n(), Valence_Mean = mean(Subjective_Valence), Valence_SD = sd(Subjective_Valence) ) ``` ```{r, echo=FALSE} load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == "Negative") # Summary emotion %>% subset(!(is.na(Subjective_Valence) | is.na(Autobiographical_Link))) %>% group_by(Participant_ID) %>% summarise( n_Trials = n(), Valence_Mean = mean(Subjective_Valence), Valence_SD = sd(Subjective_Valence) ) ``` As we can see from the means and SDs, there is a lot of variability **between** participants both in their means and their individual *within*-participant SD. ### Effect of Standardization We will create three data frames standardized with each of the three techniques. ```{r} Z_VariableWise <- emotion %>% standardize() Z_ParticipantWise <- emotion %>% group_by(Participant_ID) %>% standardize() Z_Full <- emotion %>% group_by(Participant_ID) %>% standardize() %>% ungroup() %>% standardize() ``` Let's see how these three standardization techniques affected the **Valence** variable. ### Across Participants We can calculate the mean and SD of *Valence* across all participants: ```{r, eval=FALSE} # Create a convenient function to print summarise_Subjective_Valence <- function(data) { df_name <- deparse(substitute(data)) data %>% ungroup() %>% summarise( DF = df_name, Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) } # Check the results rbind( summarise_Subjective_Valence(Z_VariableWise), summarise_Subjective_Valence(Z_ParticipantWise), summarise_Subjective_Valence(Z_Full) ) ``` ```{r, echo=FALSE} # Create a convenient function to print summarise_Subjective_Valence <- function(data) { df_name <- deparse(substitute(data)) data <- data %>% ungroup() %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) cbind(DF = df_name, data) } # Check the results rbind( summarise_Subjective_Valence(Z_VariableWise), summarise_Subjective_Valence(Z_ParticipantWise), summarise_Subjective_Valence(Z_Full) ) %>% knitr::kable(digits = 2) ``` The **means** and the **SD** appear as fairly similar (0 and 1)... ```{r, fig.width=7, fig.height=4.5, results='markup', fig.align='center'} library(see) library(ggplot2) ggplot() + geom_density(aes(Z_VariableWise$Subjective_Valence, color = "Z_VariableWise" ), size = 1) + geom_density(aes(Z_ParticipantWise$Subjective_Valence, color = "Z_ParticipantWise" ), size = 1) + geom_density(aes(Z_Full$Subjective_Valence, color = "Z_Full" ), size = 1) + see::theme_modern() + labs(color = "") ``` and so do the marginal distributions... ### At the Participant Level However, we can also look at what happens in the participant level. Let's look at the first 5 participants: ```{r, eval=FALSE} # Create convenient function print_participants <- function(data) { df_name <- deparse(substitute(data)) data %>% group_by(Participant_ID) %>% summarise( DF = df_name, Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) %>% head(5) %>% select(DF, everything()) } # Check the results rbind( print_participants(Z_VariableWise), print_participants(Z_ParticipantWise), print_participants(Z_Full) ) ``` ```{r, echo=FALSE} # Create convenient function print_participants <- function(data) { df_name <- deparse(substitute(data)) data %>% group_by(Participant_ID) %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) %>% cbind(DF = df_name, .) %>% head(5) %>% select(DF, everything()) } # Check the results rbind( print_participants(Z_VariableWise), print_participants(Z_ParticipantWise), print_participants(Z_Full) ) %>% knitr::kable(digits = 2) ``` Seems like *full* and *participant-wise* standardization give similar results, but different ones than *variable-wise* standardization. ### Compare Let's do a **correlation** between the **variable-wise and participant-wise methods**. ```{r, fig.width=7, fig.height=4.5, results='markup', fig.align='center'} r <- cor.test( Z_VariableWise$Subjective_Valence, Z_ParticipantWise$Subjective_Valence ) data.frame( Original = emotion$Subjective_Valence, VariableWise = Z_VariableWise$Subjective_Valence, ParticipantWise = Z_ParticipantWise$Subjective_Valence ) %>% ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) + geom_point(alpha = 0.75, shape = 16) + geom_smooth(method = "lm", color = "black") + scale_color_distiller(palette = 1) + ggtitle(paste0("r = ", round(r$estimate, 2))) + see::theme_modern() ``` While the three standardization methods roughly present the same characteristics at a general level (mean 0 and SD 1) and a similar distribution, their values are not exactly the same! Let's now answer the original question by investigating the **linear relationship between valence and autobiographical link**. We can do this by running a mixed-effects model with participants entered as random effects. ```{r} library(lme4) m_raw <- lmer( formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID), data = emotion ) m_VariableWise <- update(m_raw, data = Z_VariableWise) m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise) m_Full <- update(m_raw, data = Z_Full) ``` We can extract the parameters of interest from each model, and find: ```{r} # Convenient function get_par <- function(model) { mod_name <- deparse(substitute(model)) parameters::model_parameters(model) %>% mutate(Model = mod_name) %>% select(-Parameter) %>% select(Model, everything()) %>% .[-1, ] } # Run the model on all datasets rbind( get_par(m_raw), get_par(m_VariableWise), get_par(m_ParticipantWise), get_par(m_Full) ) ``` As we can see, **variable-wise** standardization only affects **the coefficient** (which is expected, as it changes the unit), but not the test statistic or statistical significance. However, using **participant-wise** standardization *does* affect the coefficient **and** the significance. **No method is better or more justified, and the choice depends on the specific case, context, data and goal.** ### Conclusion 1. **Standardization can be useful in *some* cases and should be justified**. 2. **Variable and Participant-wise standardization methods *appear* to produce similar data**. 3. **Variable and Participant-wise standardization can lead to different results**. 4. **The chosen method can strongly influence the results and should therefore be explicitly stated and justified to enhance reproducibility of results**. We showed here yet another way of **sneakily tweaking the data** that can change the results. To prevent its use as a bad practice, we can only highlight the importance of open data, open analysis/scripts, and preregistration. # See also - `datawizard::demean()`: - `standardize_parameters(method = "pseudo")` for mixed-effects models # References datawizard/inst/doc/standardize_data.R0000644000176200001440000002030714174263642017565 0ustar liggesusers## ----message=FALSE, warning=FALSE, include=FALSE------------------------------ library(knitr) options(knitr.kable.NA = "") knitr::opts_chunk$set( comment = ">", message = FALSE, warning = FALSE, dpi = 300 ) pkgs <- c( "datawizard", "poorman", "see", "ggplot2", "parameters", "lme4" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } ## ----------------------------------------------------------------------------- library(datawizard) library(effectsize) # for data # let's have a look at what the data look like data("hardlyworking", package = "effectsize") head(hardlyworking) # let's use both methods of standardization hardlyworking$xtra_hours_z <- standardize(hardlyworking$xtra_hours) hardlyworking$xtra_hours_zr <- standardize(hardlyworking$xtra_hours, robust = TRUE) ## ---- eval=FALSE-------------------------------------------------------------- # library(dplyr) # library(tidyr) # # hardlyworking %>% # select(starts_with("xtra_hours")) %>% # pivot_longer(everything()) %>% # group_by(name) %>% # summarise( # mean = mean(value), # sd = sd(value), # median = median(value), # mad = mad(value) # ) ## ---- echo=FALSE-------------------------------------------------------------- library(poorman) hardlyworking %>% select(starts_with("xtra_hours")) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ## ----------------------------------------------------------------------------- hardlyworking_z <- standardize(hardlyworking) ## ---- eval=FALSE-------------------------------------------------------------- # hardlyworking_z %>% # select(-xtra_hours_z, -xtra_hours_zr) %>% # pivot_longer(everything()) %>% # group_by(name) %>% # summarise( # mean = mean(value), # sd = sd(value), # median = median(value), # mad = mad(value) # ) ## ---- echo=FALSE-------------------------------------------------------------- hardlyworking_z %>% select(-xtra_hours_z, -xtra_hours_zr) %>% reshape_longer(colnames_to = "name", values_to = "value") %>% group_by(name) %>% summarise( mean = mean(value), sd = sd(value), median = median(value), mad = mad(value) ) %>% knitr::kable(digits = 4) ## ---- eval=FALSE-------------------------------------------------------------- # # Download the 'emotion' dataset # load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # # # Discard neutral pictures (keep only negative) # emotion <- emotion %>% filter(Emotion_Condition == "Negative") # # # Summary # emotion %>% # drop_na(Subjective_Valence, Autobiographical_Link) %>% # group_by(Participant_ID) %>% # summarise( # n_Trials = n(), # Valence_Mean = mean(Subjective_Valence), # Valence_SD = sd(Subjective_Valence) # ) ## ---- echo=FALSE-------------------------------------------------------------- load(url("https://raw.github.com/neuropsychology/psycho.R/master/data/emotion.rda")) # Discard neutral pictures (keep only negative) emotion <- emotion %>% filter(Emotion_Condition == "Negative") # Summary emotion %>% subset(!(is.na(Subjective_Valence) | is.na(Autobiographical_Link))) %>% group_by(Participant_ID) %>% summarise( n_Trials = n(), Valence_Mean = mean(Subjective_Valence), Valence_SD = sd(Subjective_Valence) ) ## ----------------------------------------------------------------------------- Z_VariableWise <- emotion %>% standardize() Z_ParticipantWise <- emotion %>% group_by(Participant_ID) %>% standardize() Z_Full <- emotion %>% group_by(Participant_ID) %>% standardize() %>% ungroup() %>% standardize() ## ---- eval=FALSE-------------------------------------------------------------- # # Create a convenient function to print # summarise_Subjective_Valence <- function(data) { # df_name <- deparse(substitute(data)) # data %>% # ungroup() %>% # summarise( # DF = df_name, # Mean = mean(Subjective_Valence), # SD = sd(Subjective_Valence) # ) # } # # Check the results # rbind( # summarise_Subjective_Valence(Z_VariableWise), # summarise_Subjective_Valence(Z_ParticipantWise), # summarise_Subjective_Valence(Z_Full) # ) ## ---- echo=FALSE-------------------------------------------------------------- # Create a convenient function to print summarise_Subjective_Valence <- function(data) { df_name <- deparse(substitute(data)) data <- data %>% ungroup() %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) cbind(DF = df_name, data) } # Check the results rbind( summarise_Subjective_Valence(Z_VariableWise), summarise_Subjective_Valence(Z_ParticipantWise), summarise_Subjective_Valence(Z_Full) ) %>% knitr::kable(digits = 2) ## ---- fig.width=7, fig.height=4.5, results='markup', fig.align='center'------- library(see) library(ggplot2) ggplot() + geom_density(aes(Z_VariableWise$Subjective_Valence, color = "Z_VariableWise" ), size = 1) + geom_density(aes(Z_ParticipantWise$Subjective_Valence, color = "Z_ParticipantWise" ), size = 1) + geom_density(aes(Z_Full$Subjective_Valence, color = "Z_Full" ), size = 1) + see::theme_modern() + labs(color = "") ## ---- eval=FALSE-------------------------------------------------------------- # # Create convenient function # print_participants <- function(data) { # df_name <- deparse(substitute(data)) # data %>% # group_by(Participant_ID) %>% # summarise( # DF = df_name, # Mean = mean(Subjective_Valence), # SD = sd(Subjective_Valence) # ) %>% # head(5) %>% # select(DF, everything()) # } # # # Check the results # rbind( # print_participants(Z_VariableWise), # print_participants(Z_ParticipantWise), # print_participants(Z_Full) # ) ## ---- echo=FALSE-------------------------------------------------------------- # Create convenient function print_participants <- function(data) { df_name <- deparse(substitute(data)) data %>% group_by(Participant_ID) %>% summarise( Mean = mean(Subjective_Valence), SD = sd(Subjective_Valence) ) %>% cbind(DF = df_name, .) %>% head(5) %>% select(DF, everything()) } # Check the results rbind( print_participants(Z_VariableWise), print_participants(Z_ParticipantWise), print_participants(Z_Full) ) %>% knitr::kable(digits = 2) ## ---- fig.width=7, fig.height=4.5, results='markup', fig.align='center'------- r <- cor.test( Z_VariableWise$Subjective_Valence, Z_ParticipantWise$Subjective_Valence ) data.frame( Original = emotion$Subjective_Valence, VariableWise = Z_VariableWise$Subjective_Valence, ParticipantWise = Z_ParticipantWise$Subjective_Valence ) %>% ggplot(aes(x = VariableWise, y = ParticipantWise, colour = Original)) + geom_point(alpha = 0.75, shape = 16) + geom_smooth(method = "lm", color = "black") + scale_color_distiller(palette = 1) + ggtitle(paste0("r = ", round(r$estimate, 2))) + see::theme_modern() ## ----------------------------------------------------------------------------- library(lme4) m_raw <- lmer( formula = Subjective_Valence ~ Autobiographical_Link + (1 | Participant_ID), data = emotion ) m_VariableWise <- update(m_raw, data = Z_VariableWise) m_ParticipantWise <- update(m_raw, data = Z_ParticipantWise) m_Full <- update(m_raw, data = Z_Full) ## ----------------------------------------------------------------------------- # Convenient function get_par <- function(model) { mod_name <- deparse(substitute(model)) parameters::model_parameters(model) %>% mutate(Model = mod_name) %>% select(-Parameter) %>% select(Model, everything()) %>% .[-1, ] } # Run the model on all datasets rbind( get_par(m_raw), get_par(m_VariableWise), get_par(m_ParticipantWise), get_par(m_Full) ) datawizard/inst/doc/demean.R0000644000176200001440000002112014174263635015511 0ustar liggesusers## ---- include=FALSE----------------------------------------------------------- library(knitr) knitr::opts_chunk$set( echo = TRUE, collapse = TRUE, warning = FALSE, message = FALSE, comment = "#>", out.width = "100%" ) pkgs <- c( "datawizard", "poorman", "tidyr", "see", "ggplot2", "parameters", "performance", "lme4", "lfe" ) if (!all(sapply(pkgs, requireNamespace, quietly = TRUE))) { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("parameters") >= "0.14.1") { knitr::opts_chunk$set(eval = FALSE) } if (!packageVersion("performance") >= "0.7.4") { knitr::opts_chunk$set(eval = FALSE) } set.seed(333) ## ----------------------------------------------------------------------------- library(parameters) data("qol_cancer") ## ----------------------------------------------------------------------------- library(performance) check_heterogeneity_bias(qol_cancer, select = c("phq4", "education"), group = "ID") ## ----------------------------------------------------------------------------- qol_cancer <- cbind( qol_cancer, datawizard::demean(qol_cancer, select = c("phq4", "QoL"), group = "ID") ) ## ----------------------------------------------------------------------------- fe_model1 <- lm( QoL ~ 0 + time + phq4_within + ID, data = qol_cancer ) # we use only the first two rows, because the remaining rows are # the estimates for "ID", which is not of interest here... model_parameters(fe_model1)[1:2, ] # instead of removing the intercept, we could also use the # de-meaned response... fe_model2 <- lm( QoL_within ~ time + phq4_within + ID, data = qol_cancer ) model_parameters(fe_model2)[2:3, ] # we compare the results with those from the "lfe"-package for panel data library(lfe) fe_model3 <- felm( QoL ~ time + phq4 | ID, data = qol_cancer ) model_parameters(fe_model3) ## ----------------------------------------------------------------------------- library(lme4) mixed_1 <- lmer( QoL ~ time + phq4_within + phq4_between + (1 | ID), data = qol_cancer ) model_parameters(mixed_1) # compare to FE-model model_parameters(fe_model1)[1:2, ] ## ----------------------------------------------------------------------------- mixed_2 <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID), data = qol_cancer ) # effects = "fixed" will not display random effects, but split the # fixed effects into its between- and within-effects components. model_parameters(mixed_2, effects = "fixed") ## ----echo=FALSE--------------------------------------------------------------- f <- "yit = β0 + β1W (xit - ͞xi) + β2B ͞xi + β3 zi + υi0 + υi1 (xit - ͞xi) + εit" knitr::asis_output(f) ## ----echo=FALSE--------------------------------------------------------------- f <- "
  • xit - ͞xi is the de-meaned predictor, phq4_within
  • ͞xi is the group-meaned predictor, phq4_between
  • β1W is the coefficient for phq4_within (within-subject)
  • β2B is the coefficient for phq4_between (bewteen-subject)
  • β3 is the coefficient for time-constant predictors, such as `hospital` or `education` (bewteen-subject)
" knitr::asis_output(f) ## ----------------------------------------------------------------------------- rewb <- lmer( QoL ~ time + phq4_within + phq4_between + education + (1 + time | ID) + (1 + phq4_within | ID), data = qol_cancer ) ## ----------------------------------------------------------------------------- model_parameters(rewb, effects = "fixed") ## ----------------------------------------------------------------------------- random_parameters(rewb) ## ----------------------------------------------------------------------------- library(ggplot2) library(poorman) library(see) set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_point(colour = "#555555", size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----------------------------------------------------------------------------- m1 <- lm(y ~ x, data = d) model_parameters(m1) ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.5, alpha = .5) + geom_smooth(method = "lm", se = F, colour = "#555555") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----------------------------------------------------------------------------- m2 <- lm(y ~ 0 + x_within + grp, data = d) model_parameters(m2)[1, ] ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----------------------------------------------------------------------------- m3 <- lm(y ~ x_between, data = d) model_parameters(m3) ## ----echo=FALSE--------------------------------------------------------------- ggplot(d, aes(x, y)) + geom_smooth(mapping = aes(colour = grp), method = "lm", se = FALSE) + geom_point(mapping = aes(colour = grp), size = 2.2, alpha = .6) + geom_smooth(mapping = aes(x = x_between, y = y_between), method = "lm", se = F, colour = "#444444") + see::scale_color_flat() + see::theme_modern() + labs(x = "Typing Speed", y = "Typing Errors", colour = "Type Experience") ## ----------------------------------------------------------------------------- m4 <- lmer(y ~ x_between + x_within + (1 | grp), data = d) model_parameters(m4) ## ----------------------------------------------------------------------------- m5 <- lmer(y ~ x_between + x_within + (1 + x_within | grp), data = d) model_parameters(m5) ## ----------------------------------------------------------------------------- set.seed(123) n <- 5 b <- seq(1, 1.5, length.out = 5) x <- seq(2, 2 * n, 2) d <- do.call(rbind, lapply(1:n, function(i) { data.frame( x = seq(1, n, by = .2), y = 2 * x[i] + b[i] * seq(1, n, by = .2) + rnorm(21), grp = as.factor(2 * i) ) })) # create imbalanced groups d$grp[sample(which(d$grp == 8), 10)] <- 6 d$grp[sample(which(d$grp == 4), 8)] <- 2 d$grp[sample(which(d$grp == 10), 9)] <- 6 d <- d %>% group_by(grp) %>% mutate(x = rev(15 - (x + 1.5 * as.numeric(grp)))) %>% ungroup() labs <- c("very slow", "slow", "average", "fast", "very fast") levels(d$grp) <- rev(labs) d <- cbind(d, datawizard::demean(d, c("x", "y"), group = "grp")) # Between-subject effect of typing speed m1 <- lm(y ~ x_between, data = d) model_parameters(m1) # Between-subject effect of typing speed, accounting for group structure m2 <- lmer(y ~ x_between + (1 | grp), data = d) model_parameters(m2) datawizard/inst/CITATION0000644000176200001440000000116014113652110014506 0ustar liggesusersbibentry( bibtype="Article", title="datawizard: Easy Data Wrangling", author=c(person("Dominique", "Makowski"), person("Daniel", "Lüdecke"), person("Indrajeet", "Patil"), person("Mattan S.", "Ben-Shachar"), person("Brenton M.", "Wiernik")), journal="CRAN", year="2021", note="R package", url="https://easystats.github.io/datawizard/", textVersion = paste("Makowski, Lüdecke, Patil, Ben-Shachar, & Wiernik (2021). datawizard: Easy Data Wrangling. CRAN.", "Available from https://easystats.github.io/datawizard/" ), mheader = "To cite datawizard in publications use:" ) datawizard/inst/WORDLIST0000644000176200001440000000127314164750346014570 0ustar liggesusersAdressing Analysing Asparouhov BMC Bafumi Carle Catran De Fairbrother Gelman Giesecke Giesselmann Heisig Herrington Hockety Hoffmann Joanes Lumley Minitab Mundlak PHQ PSU REWB Rescale Rescaling Routledge SDs Schaeffer Verkuilen Verkuilen's Winsorize Winsorized Winsorizing al behaviour de doi easystats effectsize endogeneity et geoms github https io leptokurtic lme meaned mesokurtic modelling nd partialization platykurtic pockety proprties pth quartiles reproducibility rescale rescaled rescaling stackexchange tailedness th tibbles unitless untransformed visualisation wikipedia winsorization winsorize winsorized wockety