datawizard is a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It is part of the easystats ecosystem, a suite of R packages to deal with your entire statistical analysis, from cleaning the data to reporting the results.

It covers two aspects of data preparation:

Data manipulation: datawizard offers a very similar set of functions to that of the tidyverse packages, such as a dplyr and tidyr, to select, filter and reshape data, with a few key differences. 1) All data manipulation functions start with the prefix data_* (which makes them easy to identify). 2) Although most functions can be used exactly as their tidyverse equivalents, they are also string-friendly (which makes them easy to program with and use inside functions). Finally, datawizard is super lightweight (no dependencies, similar to poorman), which makes it awesome for developers to use in their packages.
Statistical transformations: datawizard also has powerful functions to easily apply common data transformations, including standardization, normalization, rescaling, rank-transformation, scale reversing, recoding, binning, etc.

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")`

Tip

Instead of library(datawizard), use library(easystats). This will make all features of the easystats-ecosystem available.

To stay updated, use easystats::install_latest().

Citation

To cite the package, run the following command:

citation("datawizard") To cite package 'datawizard' in publications use:   Patil et al., (2022). datawizard: An R Package for Easy Data  Preparation and Statistical Transformations. Journal of Open Source  Software, 7(78), 4684, https://doi.org/10.21105/joss.04684  A BibTeX entry for LaTeX users is   @Article{,  title = {{datawizard}: An {R} Package for Easy Data Preparation and Statistical Transformations},  author = {Indrajeet Patil and Dominique Makowski and Mattan S. Ben-Shachar and Brenton M. Wiernik and Etienne Bacher and Daniel Lüdecke},  journal = {Journal of Open Source Software},  year = {2022},  volume = {7},  number = {78},  pages = {4684},  doi = {10.21105/joss.04684},  }

Features

Most courses and tutorials about statistical modeling assume that you are working with a clean and tidy dataset. In practice, however, a major part of doing statistical modeling is preparing your data–cleaning up values, creating new columns, reshaping the dataset, or transforming some variables. datawizard provides easy to use tools to perform these common, critical, and sometimes tedious data preparation tasks.

Data wrangling

Select, filter and remove variables

The package provides helpers to filter rows meeting certain conditions…

 data_match(mtcars, data.frame(vs = 0, am = 1)) #> 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 logical expressions:

 data_filter(mtcars, vs == 0 & am == 1) #> 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

Finding columns in a data frame, or retrieving the data of selected columns, can be achieved using extract_column_names() or data_select():

 # find column names matching a pattern extract_column_names(iris, starts_with("Sepal")) #> [1] "Sepal.Length" "Sepal.Width"  # return data columns matching a pattern data_select(iris, starts_with("Sepal")) |> head() #> Sepal.Length Sepal.Width #> 1 5.1 3.5 #> 2 4.9 3.0 #> 3 4.7 3.2 #> 4 4.6 3.1 #> 5 5.0 3.6 #> 6 5.4 3.9

It is also possible to extract one or more variables:

 # single variable data_extract(mtcars, "gear") #> [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4  # more variables head(data_extract(iris, ends_with("Width"))) #> Sepal.Width Petal.Width #> 1 3.5 0.2 #> 2 3.0 0.2 #> 3 3.2 0.2 #> 4 3.1 0.2 #> 5 3.6 0.2 #> 6 3.9 0.4

Due to the consistent API, removing variables is just as simple:

 head(data_remove(iris, starts_with("Sepal"))) #> Petal.Length Petal.Width Species #> 1 1.4 0.2 setosa #> 2 1.4 0.2 setosa #> 3 1.3 0.2 setosa #> 4 1.5 0.2 setosa #> 5 1.4 0.2 setosa #> 6 1.7 0.4 setosa

Reorder or rename

 head(data_relocate(iris, select = "Species", before = "Sepal.Length")) #> Species Sepal.Length Sepal.Width Petal.Length Petal.Width #> 1 setosa 5.1 3.5 1.4 0.2 #> 2 setosa 4.9 3.0 1.4 0.2 #> 3 setosa 4.7 3.2 1.3 0.2 #> 4 setosa 4.6 3.1 1.5 0.2 #> 5 setosa 5.0 3.6 1.4 0.2 #> 6 setosa 5.4 3.9 1.7 0.4

 head(data_rename(iris, c("Sepal.Length", "Sepal.Width"), c("length", "width"))) #> length width Petal.Length Petal.Width 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

Merge

 x <- data.frame(a = 1:3, b = c("a", "b", "c"), c = 5:7, id = 1:3) y <- data.frame(c = 6:8, d = c("f", "g", "h"), e = 100:102, id = 2:4)  x #> a b c id #> 1 1 a 5 1 #> 2 2 b 6 2 #> 3 3 c 7 3 y #> c d e id #> 1 6 f 100 2 #> 2 7 g 101 3 #> 3 8 h 102 4  data_merge(x, y, join = "full") #> a b c id d e #> 3 1 a 5 1 <NA> NA #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101 #> 4 NA <NA> 8 4 h 102  data_merge(x, y, join = "left") #> a b c id d e #> 3 1 a 5 1 <NA> NA #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101  data_merge(x, y, join = "right") #> a b c id d e #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101 #> 3 NA <NA> 8 4 h 102  data_merge(x, y, join = "semi", by = "c") #> a b c id #> 2 2 b 6 2 #> 3 3 c 7 3  data_merge(x, y, join = "anti", by = "c") #> a b c id #> 1 1 a 5 1  data_merge(x, y, join = "inner") #> a b c id d e #> 1 2 b 6 2 f 100 #> 2 3 c 7 3 g 101  data_merge(x, y, join = "bind") #> a b c id d e #> 1 1 a 5 1 <NA> NA #> 2 2 b 6 2 <NA> NA #> 3 3 c 7 3 <NA> NA #> 4 NA <NA> 6 2 f 100 #> 5 NA <NA> 7 3 g 101 #> 6 NA <NA> 8 4 h 102

Reshape

A common data wrangling task is to reshape data.

Either to go from wide/Cartesian to long/tidy format

 wide_data <- data.frame(replicate(5, rnorm(10)))  head(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

or the other way

 long_data <- data_to_long(wide_data, rows_to = "Row_ID") # Save row number  data_to_wide(long_data,  names_from = "name",  values_from = "value",  id_cols = "Row_ID" ) #> Row_ID X1 X2 X3 X4 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

Empty rows and columns

 tmp <- data.frame(  a = c(1, 2, 3, NA, 5),  b = c(1, NA, 3, NA, 5),  c = c(NA, NA, NA, NA, NA),  d = c(1, NA, 3, NA, 5) )  tmp #> a b c d #> 1 1 1 NA 1 #> 2 2 NA NA NA #> 3 3 3 NA 3 #> 4 NA NA NA NA #> 5 5 5 NA 5  # indices of empty columns or rows empty_columns(tmp) #> c  #> 3 empty_rows(tmp) #> [1] 4  # remove empty columns or rows remove_empty_columns(tmp) #> a b d #> 1 1 1 1 #> 2 2 NA NA #> 3 3 3 3 #> 4 NA NA NA #> 5 5 5 5 remove_empty_rows(tmp) #> a b c d #> 1 1 1 NA 1 #> 2 2 NA NA NA #> 3 3 3 NA 3 #> 5 5 5 NA 5  # remove empty columns and rows remove_empty(tmp) #> a b d #> 1 1 1 1 #> 2 2 NA NA #> 3 3 3 3 #> 5 5 5 5

Recode or cut dataframe

 set.seed(123) x <- sample(1:10, size = 50, replace = TRUE)  table(x) #> x #> 1 2 3 4 5 6 7 8 9 10  #> 2 3 5 3 7 5 5 2 11 7  # cut into 3 groups, based on distribution (quantiles) table(categorize(x, split = "quantile", n_groups = 3)) #>  #> 1 2 3  #> 13 19 18

Data Transformations

The packages also contains multiple functions to help transform data.

Standardize

For example, to standardize (z-score) data:

 # 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

Winsorize

To winsorize data:

 # 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

Center

To grand-mean center data

 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

Ranktransform

To rank-transform data:

 # 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

Rescale

To rescale a numeric variable to a new range:

 change_scale(c(0, 1, 5, -5, -2)) #> [1] 50 60 100 0 30 #> (original range = -5 to 5)

Rotate or transpose

 x <- mtcars[1:3, 1:4]  x #> mpg cyl disp hp #> Mazda RX4 21.0 6 160 110 #> Mazda RX4 Wag 21.0 6 160 110 #> Datsun 710 22.8 4 108 93  data_rotate(x) #> Mazda RX4 Mazda RX4 Wag Datsun 710 #> mpg 21 21 22.8 #> cyl 6 6 4.0 #> disp 160 160 108.0 #> hp 110 110 93.0

Data properties

datawizard provides a way to provide comprehensive descriptive summary for all variables in a dataframe:

 data(iris) describe_distribution(iris) #> Variable | Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing #> ---------------------------------------------------------------------------------------- #> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] | 0.31 | -0.55 | 150 | 0 #> Sepal.Width | 3.06 | 0.44 | 0.52 | [2.00, 4.40] | 0.32 | 0.23 | 150 | 0 #> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] | -0.27 | -1.40 | 150 | 0 #> Petal.Width | 1.20 | 0.76 | 1.50 | [0.10, 2.50] | -0.10 | -1.34 | 150 | 0

Or even just a variable

 describe_distribution(mtcars$wt) #> Mean | SD | IQR | Range | Skewness | Kurtosis | n | n_Missing #> ------------------------------------------------------------------------ #> 3.22 | 0.98 | 1.19 | [1.51, 5.42] | 0.47 | 0.42 | 32 | 0

There are also some additional data properties that can be computed using this package.

 x <- (-10:10)^3 + rnorm(21, 0, 100) smoothness(x, method = "diff") #> [1] 1.791243 #> attr(,"class") #> [1] "parameters_smoothness" "numeric"

Function design and pipe-workflow

The design of the datawizard functions follows a design principle that makes it easy for user to understand and remember how functions work:

the first argument is the data
for methods that work on data frames, two arguments are following to select and exclude variables
the following arguments are arguments related to the specific tasks of the functions

Most important, functions that accept data frames usually have this as their first argument, and also return a (modified) data frame again. Thus, datawizard integrates smoothly into a “pipe-workflow”.

 iris |>  # all rows where Species is "versicolor" or "virginica"  data_filter(Species %in% c("versicolor", "virginica")) |>  # select only columns with "." in names (i.e. drop Species)  data_select(contains("\\.")) |>  # move columns that ends with "Length" to start of data frame  data_relocate(ends_with("Length")) |>  # remove fourth column  data_remove(4) |>  head() #> Sepal.Length Petal.Length Sepal.Width #> 51 7.0 4.7 3.2 #> 52 6.4 4.5 3.2 #> 53 6.9 4.9 3.1 #> 54 5.5 4.0 2.3 #> 55 6.5 4.6 2.8 #> 56 5.7 4.5 2.8

Contributing and Support

In case you want to file an issue or contribute in another way to the package, please follow this guide. 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. By participating in this project you agree to abide by its terms.

datawizard: Easy Data Wrangling and Statistical Transformations