skimr v1.0.3

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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.

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skimr

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skimr provides a frictionless approach to summary statistics which conforms to the principle of least surprise, displaying summary statistics the user can skim quickly to understand their data. It handles different data types and returns a skim_df object which can be included in a pipeline or displayed nicely for the human reader.

Installation

The current released version of skimr can be installed 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.

Skim statistics in the console

skimr:

  • Provides a larger set of statistics than summary(), including missing, complete, n, and sd.
  • reports each data types separately
  • handles dates, logicals, and a variety of other types
  • supports spark-bar and spark-line based on Hadley Wickham's pillar package.

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
##      feed       0       71 71        6 soy: 14, cas: 12, lin: 12, sun: 12   FALSE
## ── Variable type:numeric ───────────────────────────────────────────
##  variable missing complete  n   mean    sd  p0   p25 p50   p75 p100     hist
##    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
##   Species       0      150 150        3 set: 50, ver: 50, vir: 50, NA: 0   FALSE
## ── Variable type:numeric ───────────────────────────────────────────
##      variable missing complete   n mean   sd  p0 p25  p50 p75 p100     hist
##  Petal.Length       0      150 150 3.76 1.77 1   1.6 4.35 5.1  6.9 ▇▁▁▂▅▅▃▁
##   Petal.Width       0      150 150 1.2  0.76 0.1 0.3 1.3  1.8  2.5 ▇▁▁▅▃▃▂▂
##  Sepal.Length       0      150 150 5.84 0.83 4.3 5.1 5.8  6.4  7.9 ▂▇▅▇▆▅▂▂
##   Sepal.Width       0      150 150 3.06 0.44 2   2.8 3    3.3  4.4 ▁▂▅▇▃▂▁▁

Built in support for 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
##   eye_color       0       87 87   3  13     0       15
##      gender       3       84 87   4  13     0        4
##  hair_color       5       82 87   4  13     0       12
##   homeworld      10       77 87   4  14     0       48
##        name       0       87 87   3  21     0       87
##  skin_color       0       87 87   3  19     0       31
##     species       5       82 87   3  14     0       37
## ── Variable type:integer ───────────────────────────────────────────
##  variable missing complete  n   mean    sd p0 p25 p50 p75 p100     hist
##    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
##      films       0       87 87       24          1             1          7
##  starships       0       87 87       17          0             0          5
##   vehicles       0       87 87       11          0             0          2
## ── Variable type:numeric ───────────────────────────────────────────
##    variable missing complete  n  mean     sd p0  p25 p50  p75 p100     hist
##  birth_year      44       43 87 87.57 154.69  8 35    52 72    896 ▇▁▁▁▁▁▁▁
##        mass      28       59 87 97.31 169.46 15 55.6  79 84.5 1358 ▇▁▁▁▁▁▁▁

Has a useful summary function

skim(iris) %>% summary()

## A skim object    
## 
## Name: iris   
## Number of Rows: 150   
## Number of Columns: 5    
##     
## Column type frequency    
## factor: 1   
## numeric: 4

Individual columns 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  p0 p25  p50 p75 p100     hist
##  Petal.Length       0      150 150 3.76 1.77 1   1.6 4.35 5.1  6.9 ▇▁▁▂▅▅▃▁
##  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  p0  p25  p50  p75 p100     hist
##      setosa Petal.Length       0       50 50 1.46 0.17 1   1.4  1.5  1.58  1.9 ▁▁▅▇▇▅▂▁
##      setosa  Petal.Width       0       50 50 0.25 0.11 0.1 0.2  0.2  0.3   0.6 ▂▇▁▂▂▁▁▁
##      setosa Sepal.Length       0       50 50 5.01 0.35 4.3 4.8  5    5.2   5.8 ▂▃▅▇▇▃▁▂
##      setosa  Sepal.Width       0       50 50 3.43 0.38 2.3 3.2  3.4  3.68  4.4 ▁▁▃▅▇▃▂▁
##  versicolor Petal.Length       0       50 50 4.26 0.47 3   4    4.35 4.6   5.1 ▁▃▂▆▆▇▇▃
##  versicolor  Petal.Width       0       50 50 1.33 0.2  1   1.2  1.3  1.5   1.8 ▆▃▇▅▆▂▁▁
##  versicolor Sepal.Length       0       50 50 5.94 0.52 4.9 5.6  5.9  6.3   7   ▃▂▇▇▇▃▅▂
##  versicolor  Sepal.Width       0       50 50 2.77 0.31 2   2.52 2.8  3     3.4 ▁▂▃▅▃▇▃▁
##   virginica Petal.Length       0       50 50 5.55 0.55 4.5 5.1  5.55 5.88  6.9 ▂▇▃▇▅▂▁▂
##   virginica  Petal.Width       0       50 50 2.03 0.27 1.4 1.8  2    2.3   2.5 ▂▁▇▃▃▆▅▃
##   virginica Sepal.Length       0       50 50 6.59 0.64 4.9 6.23 6.5  6.9   7.9 ▁▁▃▇▅▃▂▃
##   virginica  Sepal.Width       0       50 50 2.97 0.32 2.2 2.8  3    3.18  3.8 ▁▃▇▇▅▃▁▂

Knitted results

Simply skimming a data frame will produce the horizontal print layout shown above. When knitting you can also used enhanced rendering with kable and pander implementations.

Options for kable and pander

Enhanced print options are available by piping to kable() or pander(). These build on the pander package and the kable function of the knitr package These examples show how the enhanced options should appear after knitting, however your results may differ (see vignettes for details).

Option for kable.

Note that the results='asis' chunk option is used and the skimr:: namespace is used to prevent it being replaced by knitr::kable (which will result in the long skim_df object being printed.)

skim(iris) %>% skimr::kable()

Skim summary statistics
n obs: 150
n variables: 5

Variable type: factor

variable missing complete n n_unique top_counts ordered
Species 0 150 150 3 set: 50, ver: 50, vir: 50, NA: 0 FALSE

Variable type: numeric

variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35 5.1 6.9 ▇▁▁▂▅▅▃▁
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 1.8 2.5 ▇▁▁▅▃▃▂▂
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 6.4 7.9 ▂▇▅▇▆▅▂▂
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 3.3 4.4 ▁▂▅▇▃▂▁▁

Options for pander

At times you may need panderOptions('knitr.auto.asis', FALSE).

skim(iris) %>% pander()

Skim summary statistics
n obs: 150
n variables: 5

Table continues below
variable missing complete n n_unique
Species 0 150 150 3
top_counts ordered
set: 50, ver: 50, vir: 50, NA: 0 FALSE
Table continues below
variable missing complete n mean sd p0 p25 p50 p75
Petal.Length 0 150 150 3.76 1.77 1 1.6 4.35 5.1
Petal.Width 0 150 150 1.2 0.76 0.1 0.3 1.3 1.8
Sepal.Length 0 150 150 5.84 0.83 4.3 5.1 5.8 6.4
Sepal.Width 0 150 150 3.06 0.44 2 2.8 3 3.3
p100 hist
6.9 ▇▁▁▂▅▅▃▁
2.5 ▇▁▁▅▃▃▂▂
7.9 ▂▇▅▇▆▅▂▂
4.4 ▁▂▅▇▃▂▁▁

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         p0      .all 108.0000       108
## 7    weight numeric        p25      .all 204.5000     204.5
## 8    weight numeric        p50      .all 258.0000       258
## 9    weight numeric        p75      .all 323.5000     323.5
## 10   weight numeric       p100      .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 ▆▇▂▇▁▁▁▁

Customizing skimr

Although skimr provides opinionated defaults, it is highly customizable. Users can specify their own statistics, change the formatting of results, create statistics for new classes and develop skimmers for data structures that are not data frames.

Specify your own statistics and classes

Users can specify their own statistics using a list combined with the skim_with() function. This can support any named class found in your data.

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
##  Sepal.Length 1.3      7.7

# Restore defaults
skim_with_defaults()

Change formatting

Skimr provides a set of default formats that allow decimals in columns to be aligned, a reasonable number of decimal places for numeric data, and a representation of dates. Users can view thes with show_formats() and modify them with skim_format().

Skimming other objects

Procedures for developing skim functions for other objects are described in the vignette Supporting additional objects.

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.

Support for spark histograms

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>". This longstanding problem originates in the low-level code for printing dataframes. While some cases have been addressed, there are, for example, 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 skimr data frame within pander()
  • converting a skimr data frame to a vanilla R data frame, but tibbles render correctly

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 extrafont package supports these. You may also want to try wrapping your results in knitr::kable(). Please see the vignette on using fonts for details.

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 commonly used variable classes. However, in general, we encourage users to take advantage of skimr's flexibility to add their own customized classes. Please see the contributing and conduct documents.

Functions in skimr

Name Description
fix_windows_histograms Fix unicode histograms on Windows
skim Skim a data frame, getting useful summary statistics
skimr-package Skim a data frame
kable Create tables in LaTeX, HTML, Markdown and reStructuredText
print Print skim objects
skim_with Set or add the summary functions for a particular type of data
stats Summary statistic functions
reexports Objects exported from other packages
summary.skim_df Summary function for skim_df
pander Render data frames as markdown
wide Working with skimr's printed output
skim_format Change the formatting options for printed skim objects
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Vignettes of skimr

Name
Skimr_defaults.Rmd
Supporting_additional_objects.Rmd
Using_fonts.Rmd
Using_skimr.Rmd
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Details

License GPL-3
Encoding UTF-8
LazyData true
URL https://github.com/ropenscilabs/skimr
BugReports https://github.com/ropenscilabs/skimr/issues
VignetteBuilder knitr
RoxygenNote 6.0.1
Collate 'skimr-package.R' 'formats.R' 'stats.R' 'functions.R' 'kable.R' 'options.R' 'pander.R' 'skim.R' 'skim_print.R' 'skim_v.R' 'summary.R' 'utils.R' 'wide.R'
NeedsCompilation no
Packaged 2018-06-07 02:23:40 UTC; elinwaring
Repository CRAN
Date/Publication 2018-06-07 17:43:08 UTC

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