| output | ||||
|---|---|---|---|---|
|
The goal of skimr is to provide a frictionless approach to dealing with summary statistics iteratively and interactively as part of a pipeline, and that conforms to the principle of least surprise.
Skimr provides summary statistics that you can skim quickly to understand
your data and see what may be missing. It handles different data types
(numerics, factors, etc), and returns a skimr object that can be piped or
displayed nicely for the human reader.
The current released version of Skimr can be install from CRAN. If you wish to install the current build of the next release you can do so using the following:
# install.packages("devtools")
devtools::install_github("ropenscilabs/skimr")The APIs for this branch should be considered reasonably stable but still subject to change if an issue is discovered.
To install the version with the most recent changes that have not yet been incorporated in the master branch (and may not be):
devtools::install_github("ropenscilabs/skimr", ref = "develop")Do not rely on APIs from the develop branch.
- Provides a larger set of statistics than
summary()including missing, complete, n, sd - reports numeric/int/double separately from factor/chr
- handles dates, logicals
- supports spark-bar and spark-line based on Hadley Wickham's pillar package.
Nicely separates variables by class:
skim(chickwts)## Skim summary statistics
## n obs: 71
## n variables: 2
##
## Variable type: factor
## variable missing complete n n_unique top_counts ordered
## 1 feed 0 71 71 6 soy: 14, cas: 12, lin: 12, sun: 12 FALSE
##
## Variable type: numeric
## variable missing complete n mean sd min p25 median p75 max hist
## 1 weight 0 71 71 261.31 78.07 108 204.5 258 323.5 423 ▃▅▅▇▃▇▂▂
Presentation is in a compact horizontal format:
skim(iris)## Skim summary statistics
## n obs: 150
## n variables: 5
##
## Variable type: factor
## variable missing complete n n_unique top_counts ordered
## 1 Species 0 150 150 3 set: 50, ver: 50, vir: 50, NA: 0 FALSE
##
## Variable type: numeric
## variable missing complete n mean sd min p25 median p75 max hist
## 1 Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35 5.1 6.9 ▇▁▁▂▅▅▃▁
## 2 Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 1.8 2.5 ▇▁▁▅▃▃▂▂
## 3 Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 6.4 7.9 ▂▇▅▇▆▅▂▂
## 4 Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 3.3 4.4 ▁▂▅▇▃▂▁▁
Individual columns of a data frame can be selected using tidyverse-style selectors:
skim(iris, Sepal.Length, Petal.Length)## Skim summary statistics
## n obs: 150
## n variables: 5
##
## Variable type: numeric
## variable missing complete n mean sd min p25 median p75 max hist
## 1 Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35 5.1 6.9 ▇▁▁▂▅▅▃▁
Skim() can handle data that has been grouped using dplyr::group_by.
iris %>% dplyr::group_by(Species) %>% skim()## Skim summary statistics
## n obs: 150
## n variables: 5
## group variables: Species
##
## Variable type: numeric
## Species variable missing complete n mean sd min p25 median p75 max hist
## 1 setosa Petal.Length 0 50 50 1.46 0.17 1 1.4 1.5 1.58 1.9 ▁▁▅▇▇▅▂▁
## 2 setosa Petal.Width 0 50 50 0.25 0.11 0.1 0.2 0.2 0.3 0.6 ▂▇▁▂▂▁▁▁
## 3 setosa Sepal.Length 0 50 50 5.01 0.35 4.3 4.8 5 5.2 5.8 ▂▃▅▇▇▃▁▂
## 4 setosa Sepal.Width 0 50 50 3.43 0.38 2.3 3.2 3.4 3.68 4.4 ▁▁▃▅▇▃▂▁
## 5 versicolor Petal.Length 0 50 50 4.26 0.47 3 4 4.35 4.6 5.1 ▁▃▂▆▆▇▇▃
## 6 versicolor Petal.Width 0 50 50 1.33 0.2 1 1.2 1.3 1.5 1.8 ▆▃▇▅▆▂▁▁
## 7 versicolor Sepal.Length 0 50 50 5.94 0.52 4.9 5.6 5.9 6.3 7 ▃▂▇▇▇▃▅▂
## 8 versicolor Sepal.Width 0 50 50 2.77 0.31 2 2.52 2.8 3 3.4 ▁▂▃▅▃▇▃▁
## 9 virginica Petal.Length 0 50 50 5.55 0.55 4.5 5.1 5.55 5.88 6.9 ▂▇▃▇▅▂▁▂
## 10 virginica Petal.Width 0 50 50 2.03 0.27 1.4 1.8 2 2.3 2.5 ▂▁▇▃▃▆▅▃
## 11 virginica Sepal.Length 0 50 50 6.59 0.64 4.9 6.23 6.5 6.9 7.9 ▁▁▃▇▅▃▂▃
## 12 virginica Sepal.Width 0 50 50 2.97 0.32 2.2 2.8 3 3.18 3.8 ▁▃▇▇▅▃▁▂
Enhanced print options are available by piping to kable() or pander().
By default skim prints beautifully in the console, but it also produces a long, tidy-format skim_df object that can be computed on.
a <- skim(chickwts)
dim(a)## [1] 23 6
print.data.frame(skim(chickwts))## variable type stat level value formatted
## 1 weight numeric missing .all 0.0000 0
## 2 weight numeric complete .all 71.0000 71
## 3 weight numeric n .all 71.0000 71
## 4 weight numeric mean .all 261.3099 261.31
## 5 weight numeric sd .all 78.0737 78.07
## 6 weight numeric min .all 108.0000 108
## 7 weight numeric p25 .all 204.5000 204.5
## 8 weight numeric median .all 258.0000 258
## 9 weight numeric p75 .all 323.5000 323.5
## 10 weight numeric max .all 423.0000 423
## 11 weight numeric hist .all NA ▃▅▅▇▃▇▂▂
## 12 feed factor missing .all 0.0000 0
## 13 feed factor complete .all 71.0000 71
## 14 feed factor n .all 71.0000 71
## 15 feed factor n_unique .all 6.0000 6
## 16 feed factor top_counts soybean 14.0000 soy: 14
## 17 feed factor top_counts casein 12.0000 cas: 12
## 18 feed factor top_counts linseed 12.0000 lin: 12
## 19 feed factor top_counts sunflower 12.0000 sun: 12
## 20 feed factor top_counts meatmeal 11.0000 mea: 11
## 21 feed factor top_counts horsebean 10.0000 hor: 10
## 22 feed factor top_counts <NA> 0.0000 NA: 0
## 23 feed factor ordered .all 0.0000 FALSE
skim(mtcars) %>% dplyr::filter(stat=="hist")## # A tibble: 11 x 6
## variable type stat level value formatted
## <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 mpg numeric hist .all NA ▃▇▇▇▃▂▂▂
## 2 cyl numeric hist .all NA ▆▁▁▃▁▁▁▇
## 3 disp numeric hist .all NA ▇▆▁▂▅▃▁▂
## 4 hp numeric hist .all NA ▃▇▃▅▂▃▁▁
## 5 drat numeric hist .all NA ▃▇▁▅▇▂▁▁
## 6 wt numeric hist .all NA ▃▃▃▇▆▁▁▂
## 7 qsec numeric hist .all NA ▃▂▇▆▃▃▁▁
## 8 vs numeric hist .all NA ▇▁▁▁▁▁▁▆
## 9 am numeric hist .all NA ▇▁▁▁▁▁▁▆
## 10 gear numeric hist .all NA ▇▁▁▆▁▁▁▂
## 11 carb numeric hist .all NA ▆▇▂▇▁▁▁▁
skim(dplyr::starwars)## Skim summary statistics
## n obs: 87
## n variables: 13
##
## Variable type: character
## variable missing complete n min max empty n_unique
## 1 eye_color 0 87 87 3 13 0 15
## 2 gender 3 84 87 4 13 0 4
## 3 hair_color 5 82 87 4 13 0 12
## 4 homeworld 10 77 87 4 14 0 48
## 5 name 0 87 87 3 21 0 87
## 6 skin_color 0 87 87 3 19 0 31
## 7 species 5 82 87 3 14 0 37
##
## Variable type: integer
## variable missing complete n mean sd min p25 median p75 max hist
## 1 height 6 81 87 174.36 34.77 66 167 180 191 264 ▁▁▁▂▇▃▁▁
##
## Variable type: list
## variable missing complete n n_unique min_length median_length max_length
## 1 films 0 87 87 24 1 1 7
## 2 starships 0 87 87 17 0 0 5
## 3 vehicles 0 87 87 11 0 0 2
##
## Variable type: numeric
## variable missing complete n mean sd min p25 median p75 max hist
## 1 birth_year 44 43 87 87.57 154.69 8 35 52 72 896 ▇▁▁▁▁▁▁▁
## 2 mass 28 59 87 97.31 169.46 15 55.6 79 84.5 1358 ▇▁▁▁▁▁▁▁
Users can add new classes.
funs <- list(iqr = IQR,
quantile = purrr::partial(quantile, probs = .99))
skim_with(numeric = funs, append = FALSE)
skim(iris, Sepal.Length)## Skim summary statistics
## n obs: 150
## n variables: 5
##
## Variable type: factor
## variable missing complete n n_unique top_counts ordered
## 1 Species 0 150 150 3 set: 50, ver: 50, vir: 50, NA: 0 FALSE
##
## Variable type: numeric
## variable iqr quantile
## 1 Petal.Length 3.5 6.7
## 2 Petal.Width 1.5 2.5
## 3 Sepal.Length 1.3 7.7
## 4 Sepal.Width 0.5 4.15
# Restore defaults
skim_with_defaults()We are aware that there are issues with rendering the inline histograms and line charts in various contexts, some of which are described below.
There are known issues with printing the spark-histogram characters when printing a
data-frame. For example, "▂▅▇" is printed as "<U+2582><U+2585><U+2587>". Th
is longstanding problem originates in
the low-level code
for printing dataframes. For example there are reports of this issue in
Emacs ESS.
This means that while skimr can render the histograms to the console and in
kable, it cannot in other circumstances. This includes:
- rendering a
skimrdata frame within pander - converting a
skimrdata frame to a vanilla R data frame, but tibbles render correctly
In previous versions of One workaround for showing these characters in Windows
is to set the CTYPE part of your locale to Chinese/Japanese/Korean with Sys.setlocale("LC_CTYPE", "Chinese"). These values do show up by default
when printing a data-frame created by skim() as a list
(as.list()) or as a matrix (as.matrix()).
Spark-bar and spark-line work in the console but may not work when you knit
them to a specific document format.
The same session that produces a correctly rendered HTML document may
produce an incorrectly rendered PDF,
for example. This issue can generally be addressed by changing fonts to one
with good building block (for
histograms) and braille support (for line graphs). For example,
the open font "DejaVu Sans" from the extra font package supports these.
You may also want to try wrapping your results in knitr::kable().
Please see the vignette on using fonts for details on this.
Displays in documents of different types will vary. For example, one user found that the font "Yu Gothic UI Semilight" produced consistent results for Microsoft Word and Libre Office Write.
We welcome issue reports and pull requests including potentially adding support for different variable classes. Please see the contributing and conduct documents.