DISPLAY A BEAUTIFUL SUMMARY STATISTICS IN R USING SKIMR PACKAGE
This article describes how to quickly display summary statistics using the R package skimr.
skimr handles different data types and returns a skim_df object which can be included in a tidyverse pipeline or displayed nicely for the human reader.
Key features of skimr:
- Provides a larger set of statistics than the R base function
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
Contents:
- Prerequisite
- Summarize a whole dataset
- Select specific columns to summarize
- Handle grouped data
- Specify your own statistics and classes
Prerequisite
Install the stable version from CRAN:
install.packages("skimr")Load the package:
library(skimr)Summarize a whole dataset
skim(iris)| Name | iris |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| SKIM_VARIABLE | N_MISSING | COMPLETE_RATE | ORDERED | N_UNIQUE | TOP_COUNTS |
|---|---|---|---|---|---|
| Species | 0 | 1 | FALSE | 3 | set: 50, ver: 50, vir: 50 |
Variable type: numeric
| SKIM_VARIABLE | N_MISSING | COMPLETE_RATE | MEAN | SD | P0 | P25 | P50 | P75 | P100 | HIST |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.1 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Sepal.Width | 0 | 1 | 3.06 | 0.44 | 2.0 | 2.8 | 3.00 | 3.3 | 4.4 | ▁▆▇▂▁ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.6 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
| Petal.Width | 0 | 1 | 1.20 | 0.76 | 0.1 | 0.3 | 1.30 | 1.8 | 2.5 | ▇▁▇▅▃ |
Select specific columns to summarize
skim(iris, Sepal.Length, Petal.Length)| Name | iris |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| SKIM_VARIABLE | N_MISSING | COMPLETE_RATE | MEAN | SD | P0 | P25 | P50 | P75 | P100 | HIST |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.1 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.6 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
Handle grouped data
skim() can handle data that has been grouped using dplyr::group_by.
iris %>%
dplyr::group_by(Species) %>%
skim() | Name | Piped data |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 4 |
| ________________________ | |
| Group variables | Species |
Variable type: numeric
| SKIM_VARIABLE | SPECIES | N_MISSING | COMPLETE_RATE | MEAN | SD | P0 | P25 | P50 | P75 | P100 | HIST |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | setosa | 0 | 1 | 5.01 | 0.35 | 4.3 | 4.80 | 5.00 | 5.20 | 5.8 | ▃▃▇▅▁ |
| Sepal.Length | versicolor | 0 | 1 | 5.94 | 0.52 | 4.9 | 5.60 | 5.90 | 6.30 | 7.0 | ▂▇▆▃▃ |
| Sepal.Length | virginica | 0 | 1 | 6.59 | 0.64 | 4.9 | 6.23 | 6.50 | 6.90 | 7.9 | ▁▃▇▃▂ |
| Sepal.Width | setosa | 0 | 1 | 3.43 | 0.38 | 2.3 | 3.20 | 3.40 | 3.68 | 4.4 | ▁▃▇▅▂ |
| Sepal.Width | versicolor | 0 | 1 | 2.77 | 0.31 | 2.0 | 2.52 | 2.80 | 3.00 | 3.4 | ▁▅▆▇▂ |
| Sepal.Width | virginica | 0 | 1 | 2.97 | 0.32 | 2.2 | 2.80 | 3.00 | 3.18 | 3.8 | ▂▆▇▅▁ |
| Petal.Length | setosa | 0 | 1 | 1.46 | 0.17 | 1.0 | 1.40 | 1.50 | 1.58 | 1.9 | ▁▃▇▃▁ |
| Petal.Length | versicolor | 0 | 1 | 4.26 | 0.47 | 3.0 | 4.00 | 4.35 | 4.60 | 5.1 | ▂▂▇▇▆ |
| Petal.Length | virginica | 0 | 1 | 5.55 | 0.55 | 4.5 | 5.10 | 5.55 | 5.88 | 6.9 | ▃▇▇▃▂ |
| Petal.Width | setosa | 0 | 1 | 0.25 | 0.11 | 0.1 | 0.20 | 0.20 | 0.30 | 0.6 | ▇▂▂▁▁ |
| Petal.Width | versicolor | 0 | 1 | 1.33 | 0.20 | 1.0 | 1.20 | 1.30 | 1.50 | 1.8 | ▅▇▃▆▁ |
| Petal.Width | virginica | 0 | 1 | 2.03 | 0.27 | 1.4 | 1.80 | 2.00 | 2.30 | 2.5 | ▂▇▆▅▇ |
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.
my_skim <- skim_with(
numeric = sfl(iqr = IQR, mad = mad, p99 = ~ quantile(., probs = .99)),
append = FALSE
)
my_skim(iris, Sepal.Length)| Name | iris |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| SKIM_VARIABLE | N_MISSING | COMPLETE_RATE | IQR | MAD | P99 |
|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 1.3 | 1.04 | 7.7 |
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