datawizard: Easy Data Wrangling and Statistical Transformations <img src='man/figures/logo.png' align="right" height="139" />

<!-- ***:sparkles: Hockety pockety wockety wack, prepare this data forth and back*** --> <!-- ***Hockety pockety wockety wock, messy data is in shock*** --> <!-- ***Hockety pockety wockety woss, you can cite i-it from JOSS*** ^(soon) --> <!-- ***Hockety pockety wockety wass, datawizard saves your ass! :sparkles:*** -->

{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.

</br> <img src='https://media.giphy.com/media/VcizxCUIgaKpa/giphy.gif' width="300"/> </br>

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))