## Dev mode: OFF
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.
See our blog post here.
# install.packages("devtools")
devtools::install_github("hadley/pillar")
devtools::install_github("ropenscilabs/skimr")Compared to other summary() type functions skimr
- added missing, complete, n, sd
- reports numeric/int/double separately from factor/chr
- handles dates, logicals and other classes
- uses Hadley Wickham's pillar package, specifically `pillar::spark-bar
- can be extended to new data classes within data frames and new object types
Nicely separates numeric and factor variables:
skim(chickwts)## Skim summary statistics
## n obs: 71
## n variables: 2
##
## Variable type: factor
## var 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
## var 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 ▃▅▅▇▃▇▂▂
Many numeric variables:
skim(mtcars)## Skim summary statistics
## n obs: 32
## n variables: 11
##
## Variable type: numeric
## var missing complete n mean sd min p25 median p75 max hist
## 1 am 0 32 32 0.41 0.5 0 0 0 1 1 ▇▁▁▁▁▁▁▆
## 2 carb 0 32 32 2.81 1.62 1 2 2 4 8 ▆▇▂▇▁▁▁▁
## 3 cyl 0 32 32 6.19 1.79 4 4 6 8 8 ▆▁▁▃▁▁▁▇
## 4 disp 0 32 32 230.72 123.94 71.1 120.83 196.3 326 472 ▇▆▁▂▅▃▁▂
## 5 drat 0 32 32 3.6 0.53 2.76 3.08 3.7 3.92 4.93 ▃▇▁▅▇▂▁▁
## 6 gear 0 32 32 3.69 0.74 3 3 4 4 5 ▇▁▁▆▁▁▁▂
## 7 hp 0 32 32 146.69 68.56 52 96.5 123 180 335 ▃▇▃▅▂▃▁▁
## 8 mpg 0 32 32 20.09 6.03 10.4 15.43 19.2 22.8 33.9 ▃▇▇▇▃▂▂▂
## 9 qsec 0 32 32 17.85 1.79 14.5 16.89 17.71 18.9 22.9 ▃▂▇▆▃▃▁▁
## 10 vs 0 32 32 0.44 0.5 0 0 0 1 1 ▇▁▁▁▁▁▁▆
## 11 wt 0 32 32 3.22 0.98 1.51 2.58 3.33 3.61 5.42 ▃▃▃▇▆▁▁▂
Another example:
skim(iris)## Skim summary statistics
## n obs: 150
## n variables: 5
##
## Variable type: factor
## var 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
## var 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 ▁▂▅▇▃▂▁▁
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 var 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 ▁▃▇▇▅▃▁▂
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))## var 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
## var 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
## var 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
## var 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
## var 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
## var 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 ▇▁▁▁▁▁▁▁
funs <- list(iqr = IQR,
quantile = purrr::partial(quantile, probs = .99))
skim_with(numeric = funs, append = FALSE)
skim_v(iris$Sepal.Length)## # A tibble: 2 x 5
## type stat level value formatted
## <chr> <chr> <chr> <dbl> <chr>
## 1 numeric iqr .all 1.3 1.3
## 2 numeric quantile 99% 7.7 7.7
# Restore defaults
skim_with_defaults() funs <- list(iqr = IQR,
quantile = purrr::partial(quantile, probs = .99))
skim_with(numeric = funs, append = FALSE)
skim_v(iris$Sepal.Length)## # A tibble: 2 x 5
## type stat level value formatted
## <chr> <chr> <chr> <dbl> <chr>
## 1 numeric iqr .all 1.3 1.3
## 2 numeric quantile 99% 7.7 7.7
# Restore defaults
skim_with_defaults()Currently the print methods are still in early stages of development. Printing is limited to numeric, character, and factor data types. Therefore although additional types that are supported by skim() and skim_v() will not display with the default printing. To view these you may view and manipulate the skim object.
At the moment in addition to the three types with print support complex, logical, Date, POSIXct, and ts classes are supported with skim_v methods and the results are in the skim object.
We are also aware that both print.skim and print.data.frame (used for the skim object) do not handle significant digits correctly.
The develop branch currently addresses some of these issues. To install the develop branch:
install_github("ropenscilabs/skimr", ref = "develop")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()).
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
histogtams) 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().
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 adding support for different variable classes.
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