# `datawizard`: Easy Data Wrangling and Statistical Transformations

[![DOI](https://joss.theoj.org/papers/10.21105/joss.04684/status.svg)](https://doi.org/10.21105/joss.04684)
[![downloads](https://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/)

[datawizard](https://easystats.github.io/datawizard/) is a lightweight
package to easily manipulate, clean, transform, and prepare your data
for analysis. It is part of the [easystats
ecosystem](https://easystats.github.io/easystats/), 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](https://easystats.github.io/datawizard/) offers a very
  similar set of functions to that of the *tidyverse* packages, such as
  a [dplyr](https://dplyr.tidyverse.org) and
  [tidyr](https://tidyr.tidyverse.org), 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](https://easystats.github.io/datawizard/) is super
  lightweight (no dependencies, similar to
  [poorman](https://github.com/nathaneastwood/poorman)), which makes it
  awesome for developers to use in their packages.

- **Statistical transformations**:
  [datawizard](https://easystats.github.io/datawizard/) also has
  powerful functions to easily apply common data
  [transformations](https://easystats.github.io/datawizard/reference/index.html#statistical-transformations),
  including standardization, normalization, rescaling,
  rank-transformation, scale reversing, recoding, binning, etc.

![](https://media.giphy.com/media/VcizxCUIgaKpa/giphy.gif)

# Installation

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/datawizard)](https://cran.r-project.org/package=datawizard)
[![datawizard status
badge](https://easystats.r-universe.dev/badges/datawizard)](https://easystats.r-universe.dev)
[![codecov](https://codecov.io/gh/easystats/datawizard/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/datawizard)
[![R-CMD-check](https://github.com/easystats/datawizard/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/datawizard/actions)

| 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)`](https://easystats.github.io/datawizard/), use
> [`library(easystats)`](https://easystats.github.io/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:

``` r
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

[![Documentation](https://img.shields.io/badge/documentation-datawizard-orange.svg?colorB=E91E63)](https://easystats.github.io/datawizard/)
[![Blog](https://img.shields.io/badge/blog-easystats-orange.svg?colorB=FF9800)](https://easystats.github.io/blog/posts/)
[![Features](https://img.shields.io/badge/features-datawizard-orange.svg?colorB=2196F3)](https://easystats.github.io/datawizard/reference/index.html)

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](https://easystats.github.io/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`](https://easystats.github.io/datawizard/reference/data_match.md)`(``mtcars``, `[`data.frame`](https://rdrr.io/r/base/data.frame.html)`(``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`](https://easystats.github.io/datawizard/reference/data_match.md)`(``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()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
or
[`data_select()`](https://easystats.github.io/datawizard/reference/extract_column_names.md):

\
`# find column names matching a pattern`\
[`extract_column_names`](https://easystats.github.io/datawizard/reference/extract_column_names.md)`(``iris``, ``starts_with``(``"Sepal"``)``)`\
`#> [1] "Sepal.Length" "Sepal.Width"`\
\
`# return data columns matching a pattern`\
[`data_select`](https://easystats.github.io/datawizard/reference/extract_column_names.md)`(``iris``, ``starts_with``(``"Sepal"``)``)`` ``|>`` `[`head`](https://rdrr.io/r/utils/head.html)`(``)`\
`#>   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`](https://easystats.github.io/datawizard/reference/data_extract.md)`(``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`](https://rdrr.io/r/utils/head.html)`(`[`data_extract`](https://easystats.github.io/datawizard/reference/data_extract.md)`(``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`](https://rdrr.io/r/utils/head.html)`(`[`data_remove`](https://easystats.github.io/datawizard/reference/data_relocate.md)`(``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`](https://rdrr.io/r/utils/head.html)`(`[`data_relocate`](https://easystats.github.io/datawizard/reference/data_relocate.md)`(``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`](https://rdrr.io/r/utils/head.html)`(`[`data_rename`](https://easystats.github.io/datawizard/reference/data_rename.md)`(``iris``, `[`c`](https://rdrr.io/r/base/c.html)`(``"Sepal.Length"``, ``"Sepal.Width"``)``, `[`c`](https://rdrr.io/r/base/c.html)`(``"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`](https://rdrr.io/r/base/data.frame.html)`(``a ``=`` ``1``:``3``, b ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``"a"``, ``"b"``, ``"c"``)``, c ``=`` ``5``:``7``, id ``=`` ``1``:``3``)`\
`y`` ``<-`` `[`data.frame`](https://rdrr.io/r/base/data.frame.html)`(``c ``=`` ``6``:``8``, d ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``"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`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``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`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``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`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``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`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``x``, ``y``, join ``=`` ``"semi"``, by ``=`` ``"c"``)`\
`#>   a b c id`\
`#> 2 2 b 6  2`\
`#> 3 3 c 7  3`\
\
[`data_merge`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``x``, ``y``, join ``=`` ``"anti"``, by ``=`` ``"c"``)`\
`#>   a b c id`\
`#> 1 1 a 5  1`\
\
[`data_merge`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``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`](https://easystats.github.io/datawizard/reference/data_merge.md)`(``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`](https://rdrr.io/r/base/data.frame.html)`(`[`replicate`](https://rdrr.io/r/base/lapply.html)`(``5``, `[`rnorm`](https://rdrr.io/r/stats/Normal.html)`(``10``)``)``)`\
\
[`head`](https://rdrr.io/r/utils/head.html)`(`[`data_to_long`](https://easystats.github.io/datawizard/reference/data_to_long.md)`(``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`](https://easystats.github.io/datawizard/reference/data_to_long.md)`(``wide_data``, rows_to ``=`` ``"Row_ID"``)`` ``# Save row number`\
\
[`data_to_wide`](https://easystats.github.io/datawizard/reference/data_to_wide.md)`(``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`](https://rdrr.io/r/base/data.frame.html)`(`\
`  a ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``1``, ``2``, ``3``, ``NA``, ``5``)``,`\
`  b ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``1``, ``NA``, ``3``, ``NA``, ``5``)``,`\
`  c ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``NA``, ``NA``, ``NA``, ``NA``, ``NA``)``,`\
`  d ``=`` `[`c`](https://rdrr.io/r/base/c.html)`(``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`](https://easystats.github.io/datawizard/reference/remove_empty.md)`(``tmp``)`\
`#> c `\
`#> 3`\
[`empty_rows`](https://easystats.github.io/datawizard/reference/remove_empty.md)`(``tmp``)`\
`#> [1] 4`\
\
`# remove empty columns or rows`\
[`remove_empty_columns`](https://easystats.github.io/datawizard/reference/remove_empty.md)`(``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`](https://easystats.github.io/datawizard/reference/remove_empty.md)`(``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`](https://easystats.github.io/datawizard/reference/remove_empty.md)`(``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`](https://rdrr.io/r/base/Random.html)`(``123``)`\
`x`` ``<-`` `[`sample`](https://rdrr.io/r/base/sample.html)`(``1``:``10``, size ``=`` ``50``, replace ``=`` ``TRUE``)`\
\
[`table`](https://rdrr.io/r/base/table.html)`(``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`](https://rdrr.io/r/base/table.html)`(`[`categorize`](https://easystats.github.io/datawizard/reference/categorize.md)`(``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`](https://rdrr.io/r/base/summary.html)`(``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`](https://rdrr.io/r/base/summary.html)`(`[`standardize`](https://easystats.github.io/datawizard/reference/standardize.md)`(``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`](https://easystats.github.io/datawizard/reference/winsorize.md)`(``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`](https://easystats.github.io/datawizard/reference/center.md)`(``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`](https://rdrr.io/r/utils/head.html)`(``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`](https://rdrr.io/r/utils/head.html)`(`[`ranktransform`](https://easystats.github.io/datawizard/reference/ranktransform.md)`(``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`](https://easystats.github.io/datawizard/reference/rescale.md)`(`[`c`](https://rdrr.io/r/base/c.html)`(``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`](https://easystats.github.io/datawizard/reference/data_rotate.md)`(``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`](https://rdrr.io/r/utils/data.html)`(``iris``)`\
[`describe_distribution`](https://easystats.github.io/datawizard/reference/describe_distribution.md)`(``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`](https://easystats.github.io/datawizard/reference/describe_distribution.md)`(``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`](https://rdrr.io/r/stats/Normal.html)`(``21``, ``0``, ``100``)`\
[`smoothness`](https://easystats.github.io/datawizard/reference/smoothness.md)`(``x``, method ``=`` ``"diff"``)`\
`#> [1] 1.791243`\
`#> attr(,"class")`\
`#> [1] "datawizard_smoothness" "parameters_smoothness" "numeric"`

## Function design and pipe-workflow

The design of the [datawizard](https://easystats.github.io/datawizard/)
functions follows a design principle that makes it easy for user to
understand and remember how functions work:

1.  the first argument is the data
2.  for methods that work on data frames, two arguments are following to
    `select` and `exclude` variables
3.  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](https://easystats.github.io/datawizard/) integrates
smoothly into a “pipe-workflow”.

\
`iris`` ``|>`\
`  ``# all rows where Species is "versicolor" or "virginica"`\
`  `[`data_filter`](https://easystats.github.io/datawizard/reference/data_match.md)`(``Species`` `[`%in%`](https://rdrr.io/r/base/match.html)` `[`c`](https://rdrr.io/r/base/c.html)`(``"versicolor"``, ``"virginica"``)``)`` ``|>`\
`  ``# select only columns with "." in names (i.e. drop Species)`\
`  `[`data_select`](https://easystats.github.io/datawizard/reference/extract_column_names.md)`(``contains``(``"\\."``)``)`` ``|>`\
`  ``# move columns that ends with "Length" to start of data frame`\
`  `[`data_relocate`](https://easystats.github.io/datawizard/reference/data_relocate.md)`(``ends_with``(``"Length"``)``)`` ``|>`\
`  ``# remove fourth column`\
`  `[`data_remove`](https://easystats.github.io/datawizard/reference/data_relocate.md)`(``4``)`` ``|>`\
`  `[`head`](https://rdrr.io/r/utils/head.html)`(``)`\
`#>    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](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).
By participating in this project you agree to abide by its terms.

# Package index

## Data Preparation

Main functions for cleaning and preparing data

- [`data_to_long()`](https://easystats.github.io/datawizard/reference/data_to_long.md)
  [`reshape_longer()`](https://easystats.github.io/datawizard/reference/data_to_long.md)
  : Reshape (pivot) data from wide to long
- [`data_to_wide()`](https://easystats.github.io/datawizard/reference/data_to_wide.md)
  [`reshape_wider()`](https://easystats.github.io/datawizard/reference/data_to_wide.md)
  : Reshape (pivot) data from long to wide
- [`data_extract()`](https://easystats.github.io/datawizard/reference/data_extract.md)
  : Extract one or more columns or elements from an object
- [`data_match()`](https://easystats.github.io/datawizard/reference/data_match.md)
  [`data_filter()`](https://easystats.github.io/datawizard/reference/data_match.md)
  : Return filtered or sliced data frame, or row indices
- [`data_select()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  [`extract_column_names()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  [`find_columns()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  : Find or get columns in a data frame based on search patterns
- [`data_relocate()`](https://easystats.github.io/datawizard/reference/data_relocate.md)
  [`data_reorder()`](https://easystats.github.io/datawizard/reference/data_relocate.md)
  [`data_remove()`](https://easystats.github.io/datawizard/reference/data_relocate.md)
  : Relocate (reorder) columns of a data frame
- [`data_arrange()`](https://easystats.github.io/datawizard/reference/data_arrange.md)
  : Arrange rows by column values
- [`data_merge()`](https://easystats.github.io/datawizard/reference/data_merge.md)
  [`data_join()`](https://easystats.github.io/datawizard/reference/data_merge.md)
  : Merge (join) two data frames, or a list of data frames
- [`data_partition()`](https://easystats.github.io/datawizard/reference/data_partition.md)
  : Partition data
- [`data_rotate()`](https://easystats.github.io/datawizard/reference/data_rotate.md)
  [`data_transpose()`](https://easystats.github.io/datawizard/reference/data_rotate.md)
  : Rotate a data frame
- [`data_group()`](https://easystats.github.io/datawizard/reference/data_group.md)
  [`data_ungroup()`](https://easystats.github.io/datawizard/reference/data_group.md)
  : Create a grouped data frame
- [`data_replicate()`](https://easystats.github.io/datawizard/reference/data_replicate.md)
  : Expand (i.e. replicate rows) a data frame
- [`data_duplicated()`](https://easystats.github.io/datawizard/reference/data_duplicated.md)
  : Extract all duplicates
- [`data_unique()`](https://easystats.github.io/datawizard/reference/data_unique.md)
  : Keep only one row from all with duplicated IDs

## Data and Variable Transformations

### Statistical Transformations

Functions for transforming variables

- [`data_modify()`](https://easystats.github.io/datawizard/reference/data_modify.md)
  : Create new variables in a data frame
- [`data_separate()`](https://easystats.github.io/datawizard/reference/data_separate.md)
  : Separate single variable into multiple variables
- [`data_unite()`](https://easystats.github.io/datawizard/reference/data_unite.md)
  : Unite ("merge") multiple variables
- [`categorize()`](https://easystats.github.io/datawizard/reference/categorize.md)
  : Recode (or "cut" / "bin") data into groups of values.
- [`recode_into()`](https://easystats.github.io/datawizard/reference/recode_into.md)
  : Recode values from one or more variables into a new variable
- [`recode_values()`](https://easystats.github.io/datawizard/reference/recode_values.md)
  : Recode old values of variables into new values
- [`adjust()`](https://easystats.github.io/datawizard/reference/adjust.md)
  [`data_adjust()`](https://easystats.github.io/datawizard/reference/adjust.md)
  : Adjust data for the effect of other variable(s)
- [`demean()`](https://easystats.github.io/datawizard/reference/demean.md)
  [`degroup()`](https://easystats.github.io/datawizard/reference/demean.md)
  [`detrend()`](https://easystats.github.io/datawizard/reference/demean.md)
  : Compute group-meaned and de-meaned variables
- [`ranktransform()`](https://easystats.github.io/datawizard/reference/ranktransform.md)
  : (Signed) rank transformation
- [`rescale_weights()`](https://easystats.github.io/datawizard/reference/rescale_weights.md)
  : Rescale design weights for multilevel analysis
- [`winsorize()`](https://easystats.github.io/datawizard/reference/winsorize.md)
  : Winsorize data

### Linear Transformers

Convenient functions for common linear transformations

- [`center()`](https://easystats.github.io/datawizard/reference/center.md)
  [`centre()`](https://easystats.github.io/datawizard/reference/center.md)
  : Centering (Grand-Mean Centering)

- [`slide()`](https://easystats.github.io/datawizard/reference/slide.md)
  : Shift numeric value range

- [`standardize()`](https://easystats.github.io/datawizard/reference/standardize.md)
  [`standardise()`](https://easystats.github.io/datawizard/reference/standardize.md)
  [`unstandardize()`](https://easystats.github.io/datawizard/reference/standardize.md)
  [`unstandardise()`](https://easystats.github.io/datawizard/reference/standardize.md)
  : Standardization (Z-scoring)

- [`standardize(`*`<default>`*`)`](https://easystats.github.io/datawizard/reference/standardize.default.md)
  : Re-fit a model with standardized data

- [`reverse()`](https://easystats.github.io/datawizard/reference/reverse.md)
  [`reverse_scale()`](https://easystats.github.io/datawizard/reference/reverse.md)
  : Reverse-Score Variables

- [`rescale()`](https://easystats.github.io/datawizard/reference/rescale.md)
  [`change_scale()`](https://easystats.github.io/datawizard/reference/rescale.md)
  : Rescale Variables to a New Range

- [`normalize()`](https://easystats.github.io/datawizard/reference/normalize.md)
  [`unnormalize()`](https://easystats.github.io/datawizard/reference/normalize.md)
  : Normalize numeric variable to 0-1 range

- [`makepredictcall(`*`<dw_transformer>`*`)`](https://easystats.github.io/datawizard/reference/makepredictcall.dw_transformer.md)
  :

  Utility Function for Safe Prediction with `datawizard` transformers

### Others

- [`contr.deviation()`](https://easystats.github.io/datawizard/reference/contr.deviation.md)
  : Deviation Contrast Matrix

## Data Properties

Functions to compute statistical summaries of data properties and
distributions

- [`as.prop.table()`](https://easystats.github.io/datawizard/reference/as.prop.table.md)
  [`as.data.frame(`*`<datawizard_tables>`*`)`](https://easystats.github.io/datawizard/reference/as.prop.table.md)
  [`as.table(`*`<datawizard_table>`*`)`](https://easystats.github.io/datawizard/reference/as.prop.table.md)
  : Convert a crosstable to a frequency or a propensity table
- [`data_codebook()`](https://easystats.github.io/datawizard/reference/data_codebook.md)
  [`print_html(`*`<data_codebook>`*`)`](https://easystats.github.io/datawizard/reference/data_codebook.md)
  [`display(`*`<data_codebook>`*`)`](https://easystats.github.io/datawizard/reference/data_codebook.md)
  : Generate a codebook of a data frame.
- [`data_summary()`](https://easystats.github.io/datawizard/reference/data_summary.md)
  : Summarize data
- [`data_tabulate()`](https://easystats.github.io/datawizard/reference/data_tabulate.md)
  [`print(`*`<datawizard_table>`*`)`](https://easystats.github.io/datawizard/reference/data_tabulate.md)
  [`display(`*`<datawizard_table>`*`)`](https://easystats.github.io/datawizard/reference/data_tabulate.md)
  : Create frequency and crosstables of variables
- [`data_peek()`](https://easystats.github.io/datawizard/reference/data_peek.md)
  : Peek at values and type of variables in a data frame
- [`data_seek()`](https://easystats.github.io/datawizard/reference/data_seek.md)
  : Find variables by their names, variable or value labels
- [`means_by_group()`](https://easystats.github.io/datawizard/reference/means_by_group.md)
  : Summary of mean values by group
- [`coef_var()`](https://easystats.github.io/datawizard/reference/coef_var.md)
  [`distribution_coef_var()`](https://easystats.github.io/datawizard/reference/coef_var.md)
  : Compute the coefficient of variation
- [`describe_distribution()`](https://easystats.github.io/datawizard/reference/describe_distribution.md)
  : Describe a distribution
- [`distribution_mode()`](https://easystats.github.io/datawizard/reference/distribution_mode.md)
  : Compute mode for a statistical distribution
- [`skewness()`](https://easystats.github.io/datawizard/reference/skewness.md)
  [`kurtosis()`](https://easystats.github.io/datawizard/reference/skewness.md)
  [`print(`*`<datawizard_kurtosis>`*`)`](https://easystats.github.io/datawizard/reference/skewness.md)
  [`print(`*`<datawizard_skewness>`*`)`](https://easystats.github.io/datawizard/reference/skewness.md)
  [`summary(`*`<datawizard_skewness>`*`)`](https://easystats.github.io/datawizard/reference/skewness.md)
  [`summary(`*`<datawizard_kurtosis>`*`)`](https://easystats.github.io/datawizard/reference/skewness.md)
  : Compute Skewness and (Excess) Kurtosis
- [`smoothness()`](https://easystats.github.io/datawizard/reference/smoothness.md)
  : Quantify the smoothness of a vector
- [`row_count()`](https://easystats.github.io/datawizard/reference/row_count.md)
  : Count specific values row-wise
- [`row_means()`](https://easystats.github.io/datawizard/reference/row_means.md)
  [`row_sums()`](https://easystats.github.io/datawizard/reference/row_means.md)
  : Row means or sums (optionally with minimum amount of valid values)
- [`weighted_mean()`](https://easystats.github.io/datawizard/reference/weighted_mean.md)
  [`weighted_median()`](https://easystats.github.io/datawizard/reference/weighted_mean.md)
  [`weighted_sd()`](https://easystats.github.io/datawizard/reference/weighted_mean.md)
  [`weighted_mad()`](https://easystats.github.io/datawizard/reference/weighted_mean.md)
  : Weighted Mean, Median, SD, and MAD
- [`mean_sd()`](https://easystats.github.io/datawizard/reference/mean_sd.md)
  [`median_mad()`](https://easystats.github.io/datawizard/reference/mean_sd.md)
  : Summary Helpers

## Convert and Replace Data

Helpers for data replacements

- [`assign_labels()`](https://easystats.github.io/datawizard/reference/assign_labels.md)
  : Assign variable and value labels

- [`labels_to_levels()`](https://easystats.github.io/datawizard/reference/labels_to_levels.md)
  : Convert value labels into factor levels

- [`coerce_to_numeric()`](https://easystats.github.io/datawizard/reference/coerce_to_numeric.md)
  : Convert to Numeric (if possible)

- [`to_numeric()`](https://easystats.github.io/datawizard/reference/to_numeric.md)
  : Convert data to numeric

- [`to_factor()`](https://easystats.github.io/datawizard/reference/to_factor.md)
  : Convert data to factors

- [`replace_nan_inf()`](https://easystats.github.io/datawizard/reference/replace_nan_inf.md)
  :

  Convert infinite or `NaN` values into `NA`

- [`convert_na_to()`](https://easystats.github.io/datawizard/reference/convert_na_to.md)
  : Replace missing values in a variable or a data frame.

- [`convert_to_na()`](https://easystats.github.io/datawizard/reference/convert_to_na.md)
  : Convert non-missing values in a variable into missing values.

## Import data

Helpers for importing data

- [`data_read()`](https://easystats.github.io/datawizard/reference/data_read.md)
  [`data_write()`](https://easystats.github.io/datawizard/reference/data_read.md)
  : Read (import) data files from various sources

## Helpers for Data Preparation

Primarily useful in the context of other ‘easystats’ packages

- [`reshape_ci()`](https://easystats.github.io/datawizard/reference/reshape_ci.md)
  : Reshape CI between wide/long formats
- [`data_rename()`](https://easystats.github.io/datawizard/reference/data_rename.md)
  [`data_rename_rows()`](https://easystats.github.io/datawizard/reference/data_rename.md)
  : Rename columns and variable names
- [`data_addprefix()`](https://easystats.github.io/datawizard/reference/data_prefix_suffix.md)
  [`data_addsuffix()`](https://easystats.github.io/datawizard/reference/data_prefix_suffix.md)
  : Add a prefix or suffix to column names
- [`empty_columns()`](https://easystats.github.io/datawizard/reference/remove_empty.md)
  [`empty_rows()`](https://easystats.github.io/datawizard/reference/remove_empty.md)
  [`remove_empty_columns()`](https://easystats.github.io/datawizard/reference/remove_empty.md)
  [`remove_empty_rows()`](https://easystats.github.io/datawizard/reference/remove_empty.md)
  [`remove_empty()`](https://easystats.github.io/datawizard/reference/remove_empty.md)
  : Return or remove variables or observations that are completely
  missing
- [`rownames_as_column()`](https://easystats.github.io/datawizard/reference/rownames.md)
  [`column_as_rownames()`](https://easystats.github.io/datawizard/reference/rownames.md)
  [`rowid_as_column()`](https://easystats.github.io/datawizard/reference/rownames.md)
  : Tools for working with row names or row ids
- [`row_to_colnames()`](https://easystats.github.io/datawizard/reference/colnames.md)
  [`colnames_to_row()`](https://easystats.github.io/datawizard/reference/colnames.md)
  : Tools for working with column names
- [`data_select()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  [`extract_column_names()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  [`find_columns()`](https://easystats.github.io/datawizard/reference/extract_column_names.md)
  : Find or get columns in a data frame based on search patterns
- [`data_restoretype()`](https://easystats.github.io/datawizard/reference/data_restoretype.md)
  : Restore the type of columns according to a reference data frame

## Helpers for Text Formatting

Primarily useful for ‘report’ package

- [`text_format()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_fullstop()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_lastchar()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_concatenate()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_paste()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_remove()`](https://easystats.github.io/datawizard/reference/text_format.md)
  [`text_wrap()`](https://easystats.github.io/datawizard/reference/text_format.md)
  : Convenient text formatting functionalities

## Visualization helpers

Primarily useful in the context of other ‘easystats’ packages

- [`visualisation_recipe()`](https://easystats.github.io/datawizard/reference/visualisation_recipe.md)
  : Prepare objects for visualisation

## Data

Datasets useful for examples and tests

- [`efc`](https://easystats.github.io/datawizard/reference/efc.md) :
  Sample dataset from the EFC Survey
- [`nhanes_sample`](https://easystats.github.io/datawizard/reference/nhanes_sample.md)
  : Sample dataset from the National Health and Nutrition Examination
  Survey

# Articles

### Overview of vignettes

- [Overview of
  Vignettes](https://easystats.github.io/datawizard/articles/overview_of_vignettes.md):

### Data Preparation

Articles explaining utility of ‘datawizard’ for data wrangling

- [Coming from
  'tidyverse'](https://easystats.github.io/datawizard/articles/tidyverse_translation.md):
- [A quick summary of selection syntax in
  \`{datawizard}\`](https://easystats.github.io/datawizard/articles/selection_syntax.md):

### Statistical Transformations

Articles describing use of ‘datawizard’ functions for tranforming data
to use in regression models

- [Data
  Standardization](https://easystats.github.io/datawizard/articles/standardize_data.md):