datawizard
datawizard copied to clipboard
Published
20 hours ago •
easystats
easystats
Magic potions to clean and transform your data 🧙
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.
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.
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:
citation("datawizard")
To cite datawizard in publications use:
Patil, Makowski, Ben-Shachar, Wiernik, Bacher, & Lüdecke (2022).
datawizard: An R Package for Easy Data Preparation and Statistical
Transformations. CRAN. Available from
https://easystats.github.io/datawizard/
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 = {CRAN},
year = {2022},
note = {R package},
url = {https://easystats.github.io/datawizard/},
}
Features
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 find_columns() or get_columns():
# find column names matching a pattern
find_columns(iris, starts_with("Sepal"))
#> [1] "Sepal.Length" "Sepal.Width"
# return data columns matching a pattern
get_columns(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
#> attr(,"min_value")
#> [1] -5
#> attr(,"range_difference")
#> [1] 10
#> attr(,"to_range")
#> [1] 0 100
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
selectandexcludevariables - 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)
get_columns(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.
easystats