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skimrExtra

The goal of skimrExtra is to provide examples of extension to the skimr package.

Installation

You can install the released version of skimrExtra from github.

devtools::install_github("elinw/skimrExtra")

Using skimrExtra

The skimr package provides a compact summary of data in a data frame or object that can be coerced to a data frame. The summary provides an an opinionated list of statistics for many of the most commonly used data types (based on the class() of a variable). This package, skimrExtra, extends this to some additional types both as examples of how to do this and in response to some common requests for additional data types.

For example, objects using produced by the sf (simple features) package include (one or more) columns representing geometries. These are in geographic formats and not standard. In skimr these fall back to the default type of character, while skimrExtra supports them directly, assuming that the sf package is installed.

library(skimr)
if (requireNamespace("sf", quietly = TRUE)){
  library(sf)
  nc <- st_read(system.file("shape/nc.shp", package = "sf"))
  skim(nc)
}
#> Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
#> Reading layer `nc' from data source `/Users/elinwaring/Library/R/3.6/library/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> epsg (SRID):    4267
#> proj4string:    +proj=longlat +datum=NAD27 +no_defs
#> Warning: Couldn't find skimmers for class: sfc_MULTIPOLYGON, sfc; No user-
#> defined `sfl` provided. Falling back to `character`.
Name nc
Number of rows 100
Number of columns 15
_______________________
Column type frequency:
character 1
factor 2
numeric 12
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
geometry 0 1 232 1965 0 100 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
NAME 0 1 FALSE 100 Ala: 1, Ale: 1, All: 1, Ans: 1
FIPS 0 1 FALSE 100 370: 1, 370: 1, 370: 1, 370: 1

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
AREA 0 1 0.13 0.05 0.04 0.09 0.12 0.15 0.24 ▆▇▆▃▂
PERIMETER 0 1 1.67 0.48 1.00 1.32 1.61 1.86 3.64 ▇▇▂▁▁
CNTY_ 0 1 1985.96 106.52 1825.00 1902.25 1982.00 2067.25 2241.00 ▇▆▆▅▁
CNTY_ID 0 1 1985.96 106.52 1825.00 1902.25 1982.00 2067.25 2241.00 ▇▆▆▅▁
FIPSNO 0 1 37100.00 58.02 37001.00 37050.50 37100.00 37149.50 37199.00 ▇▇▇▇▇
CRESS_ID 0 1 50.50 29.01 1.00 25.75 50.50 75.25 100.00 ▇▇▇▇▇
BIR74 0 1 3299.62 3848.17 248.00 1077.00 2180.50 3936.00 21588.00 ▇▁▁▁▁
SID74 0 1 6.67 7.78 0.00 2.00 4.00 8.25 44.00 ▇▂▁▁▁
NWBIR74 0 1 1050.81 1432.91 1.00 190.00 697.50 1168.50 8027.00 ▇▁▁▁▁
BIR79 0 1 4223.92 5179.46 319.00 1336.25 2636.00 4889.00 30757.00 ▇▁▁▁▁
SID79 0 1 8.36 9.43 0.00 2.00 5.00 10.25 57.00 ▇▂▁▁▁
NWBIR79 0 1 1352.81 1976.00 3.00 250.50 874.50 1406.75 11631.00 ▇▁▁▁▁

Support for variables of the class haven_labelled is also included. This support simply identifies the underlying data type using typeof() and assumes the user will manage further processing if desired.

library(skimrExtra)
## basic example code
if (requireNamespace("sf", quietly = TRUE)){
  skim(nc)
}
Name nc
Number of rows 100
Number of columns 15
_______________________
Column type frequency:
factor 2
numeric 12
sfc_MULTIPOLYGON 1
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
NAME 0 1 FALSE 100 Ala: 1, Ale: 1, All: 1, Ans: 1
FIPS 0 1 FALSE 100 370: 1, 370: 1, 370: 1, 370: 1

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
AREA 0 1 0.13 0.05 0.04 0.09 0.12 0.15 0.24 ▆▇▆▃▂
PERIMETER 0 1 1.67 0.48 1.00 1.32 1.61 1.86 3.64 ▇▇▂▁▁
CNTY_ 0 1 1985.96 106.52 1825.00 1902.25 1982.00 2067.25 2241.00 ▇▆▆▅▁
CNTY_ID 0 1 1985.96 106.52 1825.00 1902.25 1982.00 2067.25 2241.00 ▇▆▆▅▁
FIPSNO 0 1 37100.00 58.02 37001.00 37050.50 37100.00 37149.50 37199.00 ▇▇▇▇▇
CRESS_ID 0 1 50.50 29.01 1.00 25.75 50.50 75.25 100.00 ▇▇▇▇▇
BIR74 0 1 3299.62 3848.17 248.00 1077.00 2180.50 3936.00 21588.00 ▇▁▁▁▁
SID74 0 1 6.67 7.78 0.00 2.00 4.00 8.25 44.00 ▇▂▁▁▁
NWBIR74 0 1 1050.81 1432.91 1.00 190.00 697.50 1168.50 8027.00 ▇▁▁▁▁
BIR79 0 1 4223.92 5179.46 319.00 1336.25 2636.00 4889.00 30757.00 ▇▁▁▁▁
SID79 0 1 8.36 9.43 0.00 2.00 5.00 10.25 57.00 ▇▂▁▁▁
NWBIR79 0 1 1352.81 1976.00 3.00 250.50 874.50 1406.75 11631.00 ▇▁▁▁▁

Variable type: sfc_MULTIPOLYGON

skim_variable n_missing complete_rate n_unique valid simple n_empty
geometry 0 1 100 100 100 0

Generally speaking, the skimr API should be extended in packages that wish to use it rather than relying on the skimr or skimrExtra maintainers. Doing so allows much greater customization. Instructions for this are included in the skimr "Supporting additional objects" vignette.

Utility functions

The package also includes a utility function skim_to_var_table() which produces a more compact data frame than does the standard skim() function by casting statistics with shared names to strings and placing them in a single column.

skim_to_var_table(CO2) %>% knitr::kable()
skim_variable n_missing complete_rate ordered n_unique top_counts data_type n mean sd p0 p25 p50 p75 p100 hist
Plant 0 1 TRUE 12 Qn1: 7, Qn2: 7, Qn3: 7, Qc1: 7 factor 84 NA NA NA NA NA NA NA NA
Type 0 1 FALSE 2 Que: 42, Mis: 42 factor 84 NA NA NA NA NA NA NA NA
Treatment 0 1 FALSE 2 non: 42, chi: 42 factor 84 NA NA NA NA NA NA NA NA
conc 0 1 NA NA NA numeric 84 435 296 95 175 350 675 1000 ▇▂▂▂▂
uptake 0 1 NA NA NA numeric 84 27 11 7.7 18 28 37 46 ▇▇▅▇▇

compared to

skim(CO2)
Name CO2
Number of rows 84
Number of columns 5
_______________________
Column type frequency:
factor 3
numeric 2
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
Plant 0 1 TRUE 12 Qn1: 7, Qn2: 7, Qn3: 7, Qc1: 7
Type 0 1 FALSE 2 Que: 42, Mis: 42
Treatment 0 1 FALSE 2 non: 42, chi: 42

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
conc 0 1 435.00 295.92 95.0 175.0 350.0 675.00 1000.0 ▇▂▂▂▂
uptake 0 1 27.21 10.81 7.7 17.9 28.3 37.12 45.5 ▇▇▅▇▇

Plans

It may be that this package will add support for other data types, sets of skimmers, statistics and utilities. Pull requests in these categories are welcome and will be considered on a case by case basis. Contributions should include full documentation and tests.

Please note that the 'skimrExtra' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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