## Introduction

The skim() function summarizes data types contained within data frames. It comes with a set of default summary functions for a wide variety of data types, but this is not comprehensive. Package authors can add support for skimming their specific data types in their packages, and they can provide different defaults in their own summary functions.

This example will illustrate this by creating support for the sf object produced by the “sf: Simple Features for R” package. For any object this involves two required elements and one optional element.

• experiment with interactive changes
• create methods to get_skimmers for different objects within this package
• if needed, define any custom statistics

If you are adding skim support to a package you will also need to add skimr to the list of imports. Note that in this vignette the actual analysis will not be run because that would require importing the sf package just for this example. However to run it on your own you can install sf and then run the following code. Note that code in this vignette was not evaluated when rendering the vignette in order to avoid forcing installation of sf.

library(skimr)
library(sf)
## Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0
nc <- st_read(system.file("shape/nc.shp", package = "sf"))
## 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
class(nc)
## [1] "sf"         "data.frame"

Unlike the example of having a new type of data in a column of a simple data frame in the “Using skimr” vignette, this is a different type of object with special attributes.

In this object there is also a column of a class that does not have default skimmers. By default, skimr falls back to use the sfl for character variables.

skim(nc$geometry) ## Warning: Couldn't find skimmers for class: sfc_MULTIPOLYGON, sfc; No user- ## defined sfl provided. Falling back to character.  Name nc$geometry Number of rows 100 Number of columns 1 _______________________ Column type frequency: character 1 ________________________ 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

## Experiment interactively

skimr has an opinionated list of functions for each class (e.g. numeric, factor) of data. The core package supports many commonly used classes, but there are many others. You can investigate these defaults by calling get_default_skimmer_names().

What if your data type isn’t covered by defaults? skimr usually falls back to treating the type as a character, which isn’t necessarily helpful. In this case, you’re best off adding your data type with skim_with().

Before we begin, we’ll be using the following custom summary statistic throughout. It’s a naive example, but covers the requirements of what we need.

funny_sf <- function(x) {
length(x) + 1
}

This function, like all summary functions used by skimr has two notable features.

• It accepts a vector as its single argument
• It returns a scalar

There are a lot of functions that fulfill these criteria:

• existing functions from base, stats, or other packages,
• lambda’s created using the Tidyverse-style syntax
• custom functions that have been defined in the skimr package
• custom functions that you have defined.

Not fulfilling the two criteria can lead to some very confusing behavior within skimr. Beware! An example of this issue is the base quantile() function in default skimr percentiles are returned by using quantile() five times.

Next, we create a custom skimming function. To do this, we need to think about the many specific classes of data in the sf package. The following example will build support for sfc_MULTIPOLYGON, but note that we’ll have to eventually think about sfc_LINESTRING, sfc_POLYGON, sfc_MULTIPOINT and others if we want to fully support sf.

skim_sf <- skim_with(
sfc_MULTIPOLYGON = sfl(
n_unique = n_unique,
valid = ~ sum(sf::st_is_valid(.)),
funny = funny_sf
)
)
## Creating new skimming functions for the following classes: sfc_MULTIPOLYGON.
## They did not have recognized defaults. Call get_default_skimmers() for more information.

The example above creates a new function, and you can call that function on a specific column with sfc_MULTIPOLYGON data to get the appropriate summary statistics.

skim_sf(nc$geometry)  Name nc$geometry Number of rows 100 Number of columns 1 _______________________ Column type frequency: sfc_MULTIPOLYGON 1 ________________________ Group variables None

Variable type: sfc_MULTIPOLYGON

skim_variable n_missing complete_rate n_unique valid funny
geometry 0 1 100 100 101

Creating a function that is a method of the skim_by_type generic for the data type allows skimming of an entire data frame that contains some columns of that type.

skim_by_type.sfc_MULTIPOLYGON <- function(mangled, columns, data) {
skimmed <- dplyr::summarize_at(data, columns, mangled$funs) build_results(skimmed, columns, NULL) } skim_sf(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 funny geometry 0 1 100 100 101 Sharing these functions within a separate package requires an export. The simplest way to do this is with Roxygen. #' Skimming functions for sfc_MULTIPOLYGON objects. #' @export skim_sf <- skim_with( sfc_MULTIPOLYGON = sfl( missing = n_missing, n = length, n_unique = n_unique, valid = ~ sum(sf::st_is_valid(.)), funny = funny_sf ) ) ## Creating new skimming functions for the following classes: sfc_MULTIPOLYGON. ## They did not have recognized defaults. Call get_default_skimmers() for more information. #' A skim_by_type function for sfc_MULTIPOLYGON objects. #' @export skim_by_type.sfc_MULTIPOLYGON <- function(mangled, columns, data) { skimmed <- dplyr::summarize_at(data, columns, mangled$funs)
skimr::build_results(skimmed, columns, NULL)
}

While this works within any package, there is an even better approach in this case. To take full advantage of skimr, we’ll dig a bit into its API.

skimr has a lookup mechanism, based on the function get_skimmers(), to find default summary functions for each class. This is based on the S3 class system. You can learn more about it in Advanced R.

To export a new set of defaults for a data type, create a method for the generic function get_skimmers. Each of those methods returns an sfl, a skimr function list. This is the same list-like data structure used in the skim_with() example above. But note! There is one key difference. When adding a generic we also want to identify the skim_type in the sfl.

#' @importFrom skimr get_skimmers
#' @export
get_skimmers.sfc_MULTIPOLYGON <- function(column) {
sfl(
skim_type = "sfc_MULTIPOLYGON",
n_unique = n_unique,
valid = ~ sum(sf::st_is_valid(.)),
funny = funny_sf
)
}

The same strategy follows for other data types.

• Create a method
• return an sfl
• make sure that the skim_type is there
#' @export
get_skimmers.sfc_POINT <- function(column) {
sfl(
skim_type = "sfc_POINT",
n_unique = n_unique,
valid = ~ sum(sf::st_is_valid(.))
)
}

Users of your package should load skimr to get the skim() function. Once loaded, a call to get_default_skimmer_names() will return defaults for your data types as well!

get_default_skimmer_names()
## $AsIs ## [1] "n_unique" "min_length" "max_length" ## ##$Date
## [1] "min"      "max"      "median"   "n_unique"
##
## $POSIXct ## [1] "min" "max" "median" "n_unique" ## ##$character
## [1] "min"        "max"        "empty"      "n_unique"   "whitespace"
##
## $complex ## [1] "mean" ## ##$difftime
## [1] "min"      "max"      "median"   "n_unique"
##
## $factor ## [1] "ordered" "n_unique" "top_counts" ## ##$list
## [1] "n_unique"   "min_length" "max_length"
##
## $logical ## [1] "mean" "count" ## ##$numeric
## [1] "mean" "sd"   "p0"   "p25"  "p50"  "p75"  "p100" "hist"
##
## $sfc_MULTIPOLYGON ## [1] "n_unique" "valid" "funny" ## ##$sfc_POINT
## [1] "n_unique" "valid"
##
## \$ts
##  [1] "start"      "end"        "frequency"  "deltat"     "mean"
##  [6] "sd"         "min"        "max"        "median"     "line_graph"



## Conclusion

This is a very simple example. For a package such as sf the custom statistics will likely be much more complex. The flexibility of skimr allows you to manage that.

Thanks to Jakub Nowosad, Tiernan Martin, Edzer Pebesma and Michael Sumner for inspiring and helping with the development of this code.