`datawizard`: Easy Data Wrangling and Statistical Transformations

{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
#> [32] 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
#> 1       1 -0.08281164 -1.12490028 -0.70632036 -0.7027895
#> 2       2  1.93468099 -0.87430362  0.96687656  0.2998642
#> 3       3 -2.05128979  0.04386162 -0.71016648  1.1494697
#> 4       4  0.27773897 -0.58397514 -0.05917365 -0.3016415
#> 5       5 -1.52596060 -0.82329858 -0.23094342 -0.5473394
#> 6       6 -0.26916362  0.11059280  0.69200045 -0.3854041
#> 7       7  1.23305388  0.36472778  1.35682290  0.2763720
#> 8       8  0.63360774  0.05370100  1.78872284  0.1518608
#> 9       9  0.35271746  1.36867235  0.41071582 -0.4313808
#> 10     10 -0.56048248 -0.38045724 -2.18785470 -1.8705001
#>             X5
#> 1   0.07633326
#> 2  -0.23035595
#> 3   0.31746484
#> 4  -1.59268440
#> 5  -0.18194062
#> 6   1.75614174
#> 7   0.11394932
#> 8  -0.29216508
#> 9   1.75409316
#> 10  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      
#>  Min.   :-2.81327   Min.   :-2.1778   Min.   :-1.69084  
#>  1st Qu.:-0.43569   1st Qu.:-0.6499   1st Qu.:-0.56273  
#>  Median : 0.02061   Median : 0.1515   Median :-0.06134  
#>  Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.00000  
#>  3rd Qu.: 0.66504   3rd Qu.: 0.7481   3rd Qu.: 0.69074  
#>  Max.   : 1.78978   Max.   : 1.7190   Max.   : 2.57094  
#>    Education          Catholic       Infant.Mortality  
#>  Min.   :-1.0378   Min.   :-0.9350   Min.   :-3.13886  
#>  1st Qu.:-0.5178   1st Qu.:-0.8620   1st Qu.:-0.61543  
#>  Median :-0.3098   Median :-0.6235   Median : 0.01972  
#>  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.00000  
#>  3rd Qu.: 0.1062   3rd Qu.: 1.2464   3rd Qu.: 0.60337  
#>  Max.   : 4.3702   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.

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data		data
inst		inst
man		man
paper		paper
pkgdown		pkgdown
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.lintr		.lintr
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
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`datawizard`: Easy Data Wrangling and Statistical Transformations

Installation

Citation

Features

Data wrangling

Select, filter and remove variables

Reorder or rename

Merge

Reshape

Empty rows and columns

Recode or cut dataframe

Data Transformations

Standardize

Winsorize

Center

Ranktransform

Rescale

Rotate or transpose

Data properties

Function design and pipe-workflow

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datawizard: Easy Data Wrangling and Statistical Transformations

Installation

Citation

Features

Data wrangling

Select, filter and remove variables

Reorder or rename

Merge

Reshape

Empty rows and columns

Recode or cut dataframe

Data Transformations

Standardize

Winsorize

Center

Ranktransform

Rescale

Rotate or transpose

Data properties

Function design and pipe-workflow

Contributing and Support

Code of Conduct

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`datawizard`: Easy Data Wrangling and Statistical Transformations