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skimr

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.

Installation

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

Skim statistics in the console

  • added missing, complete, n, sd
  • reports numeric/int/double separately from factor/chr
  • handles dates, logicals
  • uses Hadley's colformats, specifically colformats::spark-bar()

Nicely separates numeric and factor variables:



Many numeric variables:



Another example:



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] 22  5
View(a)

Compute on the full skim_df object

> skim(mtcars) %>% filter(stat=="hist")
# A tibble: 11 × 5
     var    type  stat      level value
   <chr>   <chr> <chr>      <chr> <dbl>
1    mpg numeric  hist ▂▅▇▇▇▃▁▁▂▂     0
2    cyl numeric  hist ▆▁▁▁▃▁▁▁▁▇     0
3   disp numeric  hist ▇▇▅▁▁▇▃▂▁▃     0
4     hp numeric  hist ▆▆▇▂▇▂▃▁▁▁     0
5   drat numeric  hist ▃▇▂▂▃▆▅▁▁▁     0
6     wt numeric  hist ▂▂▂▂▇▆▁▁▁▂     0
7   qsec numeric  hist ▂▃▇▇▇▅▅▁▁▁     0
8     vs numeric  hist ▇▁▁▁▁▁▁▁▁▆     0
9     am numeric  hist ▇▁▁▁▁▁▁▁▁▆     0
10  gear numeric  hist ▇▁▁▁▆▁▁▁▁▂     0
11  carb numeric  hist ▆▇▂▁▇▁▁▁▁▁     0

Works with strings!

Specify your own statistics

 funs <- list(iqr = IQR,
    quantile = purrr::partial(quantile, probs = .99))
  skim_with(numeric = funs, append = FALSE)
  skim_v(iris$Sepal.Length)
  
#  A tibble: 2 × 4
#      type     stat level value
#     <chr>    <chr> <chr> <dbl>
# 1 numeric      iqr  .all   1.3
# 2 numeric quantile   99%   7.7