Compact and Flexible Summaries of Data

A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.



output: html_document: keep_md: yes

## Dev mode: ON

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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 and 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.

Installation

# install.packages("devtools")
devtools::install_github("ropenscilabs/skimr")

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")

Skim statistics in the console

  • added 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 ▇▁▁▂▅▅▃▁
## 2 Sepal.Length       0      150 150 5.84 0.83 4.3 5.1   5.8  6.4 7.9 ▂▇▅▇▆▅▂▂

Handles grouped data

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 ▁▃▇▇▅▃▁▂

Options for kable and pander

Enhanced print options are available by piping to kable() or pander().

skim_df object (long format)

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

Compute on the full skim_df object

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  ▆▇▂▇▁▁▁▁

Works with strings, lists and other column classes.

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.

Specify your own statistics

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: numeric 
##       variable iqr quantile
## 1 Sepal.Length 1.3      7.7
# Restore defaults
  skim_with_defaults()

Limitations of current version

We are aware that there are issues with rendering the inline histograms and line charts in various contexts, some of which are described below.

Windows support for spark histograms

Windows cannot print the spark-histogram characters when printing a data-frame. For example, "▂▅▇" is printed as "<U+2582><U+2585><U+2587>". This longstanding problem originates in the low-level code for printing dataframes. 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()).

Printing spark histograms and line graphs in knitted documents

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.

Contributing

We welcome issue reports and pull requests including potentially adding support for different variable classes. Please see the contributing.md document.

News

skimr 1.0.1 (2018-01-xx)

NEW FEATURES

  • Add support for spark plots on Windows

MAJOR CHANGES

  • spark_line() and spark_bar() are no longer exported
  • Default statistics for numeric changed from min(x) and max(x) to quantile(x, probs= c(0)) and quantile(x, probs= c(1))

MINOR IMPROVEMENTS

  • Add minimimum required version for stringr
  • Improve documentation related to fonts

BUG FIXES

  • Fix issue where a histogram for data with all NAs threw an error
  • Suppress progress bars from dplyr::do

skimr 0.92 (2017-12-19)

MAJOR CHANGES

  • skim_v() is no longer exported. Vectors are now directly supported by skim() via skim.default().
  • Change license to GPL 3

NEW FEATURES

  • Add support for kable() and pander() for skim_df objects.
  • Add summary method for skim_df objects.
  • Add support for tidy select to skim specific columns of a data frame.
  • Add support for skimming individual vectors via skim.default().

skimr 0.91 (2017-10-14)

NEW FEATURES

  • Handling of grouped data (generated by dplyr::group_by())
  • Printing for all column classes
  • Add indicator of if a factor is ordered to skim object for factor
  • Introduction of flexible formatting
  • Easy dropping of individual functions
  • Vignettes for basic use and use with specialized object types
  • Updated readme and added CONTRIBUTING.md and CONDUCT.md
  • New public get_skimmers function to access skim functions
  • Support for difftime class

MINOR IMPROVEMENTS

  • Add header to print providing summary information about data.

BUG FIXES

  • Change from Colformat to Pillar.

skimr 0.900 (2017-07-16)

BUG FIXES

  • Fix documentation for get_fun_names()
  • Fix test and build errors and notes

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("skimr")

1.0.3 by Elin Waring, a month ago


https://github.com/ropenscilabs/skimr


Report a bug at https://github.com/ropenscilabs/skimr/issues


Browse source code at https://github.com/cran/skimr


Authors: Amelia McNamara [aut], Eduardo Arino de la Rubia [aut], Hao Zhu [aut], Julia Lowndes [ctb], Shannon Ellis [aut], Elin Waring [cre], Michael Quinn [aut], Hope McLeod [ctb], Hadley Wickham [ctb], Kirill Müller [ctb], RStudio, Inc. [cph] (Spark functions), Connor Kirkpatrick [ctb], Scott Brenstuhl [ctb], Patrick Schratz [ctb], lbusett [ctb], Mikko Korpela [ctb], Jennifer Thompson [ctb], Harris McGehee [ctb]


Documentation:   PDF Manual  


GPL-3 license


Imports cli, dplyr, magrittr, pander, purrr, rlang, stats, stringr, knitr, tibble, tidyr, tidyselect

Suggests extrafont, rmarkdown, testthat, withr, covr


Imported by codebook, groupedstats.

Suggested by nlaR.


See at CRAN