# Extending skimr

## Introduction

The skim() function summarizes data types contained within data frames and objects that have as.data.frame() methods to coerce them into 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 (and advanced users) can add support for skimming their specific non-data-frame objects in their packages, and they can provide different defaults in their own summary functions. This will require including skimr as a dependency.

# Skimming objects that are not coercible to data frames

This example will illustrate this by creating support for the lm object produced by lm(). For any object this involves two required elements and one optional element. This is a simple example, but for other types of objects there may be much more complexity

If you are adding skim support to a package you will also need to add skimr to the list of imports.

library(skimr)

The lm() function produces a complex object with class “lm”.

results <- lm(weight ~ feed, data = chickwts)
class(results)
## [1] "lm"
attributes(results)
## $names ## [1] "coefficients" "residuals" "effects" "rank" ## [5] "fitted.values" "assign" "qr" "df.residual" ## [9] "contrasts" "xlevels" "call" "terms" ## [13] "model" ## ##$class
## [1] "lm"

There is no as.data.frame method for an lm object.

as.data.frame(results)
#> Error in as.data.frame.default(results) :
#>  cannot coerce class ‘"lm"’ to a data.frame

Unlike the example of having a new type of data in a column of a simple data frame (for which we would create a sfl) frame in the “Using skimr” vignette, this is a different type of challenge: an object that we might wish to skim, but that cannot be directly skimmed. Therefore we need to make it into an object that is either a data frame or coercible to a data frame.

In the case of the lm object, the model attribute is already a data frame. So a very simple way to solve the challenge is to skim results$model directly. skim(results$model)
 Name results$model Number of rows 71 Number of columns 2 _______________________ Column type frequency: factor 1 numeric 1 ________________________ Group variables None Variable type: factor skim_variable n_missing complete_rate ordered n_unique top_counts feed 0 1 FALSE 6 soy: 14, cas: 12, lin: 12, sun: 12 Variable type: numeric skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist weight 0 1 261.31 78.07 108 204.5 258 323.5 423 ▆▆▇▇▃ This is works, but we could go one step further and create a new function for doing this directly. skim_lm <- function(.data) { .data <- .data$model
skimr::skim(.data)
}

lm(weight ~ feed, data = chickwts) %>% skim_lm()
 Name Piped data Number of rows 71 Number of columns 2 _______________________ Column type frequency: factor 1 numeric 1 ________________________ Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
feed 0 1 FALSE 6 soy: 14, cas: 12, lin: 12, sun: 12

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
weight 0 1 261.31 78.07 108 204.5 258 323.5 423 ▆▆▇▇▃

If desired, a more complex function can be created. For example, the lm object also contains fitted values and residuals. We could incorporate these in the data frame.

skim_lm <- function(.data, fit = FALSE) {
.data <- .data$model if (fit) { .data <- .data %>% dplyr::bind_cols( fitted = data.frame(results$fitted.values),
residuals = data.frame(results$residuals) ) } skimr::skim(.data) } skim_lm(results, fit = TRUE)  Name Piped data Number of rows 71 Number of columns 4 _______________________ Column type frequency: factor 1 numeric 3 ________________________ Group variables None Variable type: factor skim_variable n_missing complete_rate ordered n_unique top_counts feed 0 1 FALSE 6 soy: 14, cas: 12, lin: 12, sun: 12 Variable type: numeric skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist weight 0 1 261.31 78.07 108.00 204.50 258.00 323.50 423.00 ▆▆▇▇▃ results.fitted.values 0 1 261.31 57.46 160.20 218.75 246.43 323.58 328.92 ▃▃▅▃▇ results.residuals 0 1 0.00 52.86 -123.91 -34.41 1.57 38.17 103.09 ▂▅▇▅▃ A second example of the need for a special function is with dist objects. The UScitiesD data set is an example of this. class(UScitiesD) ## [1] "dist" UScitiesD ## Atlanta Chicago Denver Houston LosAngeles Miami NewYork ## Chicago 587 ## Denver 1212 920 ## Houston 701 940 879 ## LosAngeles 1936 1745 831 1374 ## Miami 604 1188 1726 968 2339 ## NewYork 748 713 1631 1420 2451 1092 ## SanFrancisco 2139 1858 949 1645 347 2594 2571 ## Seattle 2182 1737 1021 1891 959 2734 2408 ## Washington.DC 543 597 1494 1220 2300 923 205 ## SanFrancisco Seattle ## Chicago ## Denver ## Houston ## LosAngeles ## Miami ## NewYork ## SanFrancisco ## Seattle 678 ## Washington.DC 2442 2329 A dist object is most often, as in this case, lower triange matrices of distances, which can be measured in various ways. There are many packages that produce dist objects and/or take dist objects as inputs, including those for cluster analysis and multidimensional scaling. A simple solution to this is to follow a similar design to that for lm objects. skim_dist <- function(.data) { .data <- data.frame(as.matrix(.data)) skimr::skim(.data) } However, this has the limitation of treating the dist data as though it is simple numeric data. What we might want to do, instead, is to create a new class, for example, “distance” that is specifically for distance data. This will allow it to have its own sfl or skimr function list. As handling gets more complex, rather than make a new function it can be more powerful to define an as.data.frame S3 method for dist objects, which will allow it to integrate with skimr more completely and uses to use the skim() function directly. In a package you will want to export this. as.data.frame.dist <- function(.data) { .data <- data.frame(as.matrix(.data)) .data[] <- lapply(.data, structure, class = "distance", nms = names(.data)) .data } as.data.frame(UScitiesD) ## Atlanta Chicago Denver Houston LosAngeles Miami NewYork ## Atlanta 0 587 1212 701 1936 604 748 ## Chicago 587 0 920 940 1745 1188 713 ## Denver 1212 920 0 879 831 1726 1631 ## Houston 701 940 879 0 1374 968 1420 ## LosAngeles 1936 1745 831 1374 0 2339 2451 ## Miami 604 1188 1726 968 2339 0 1092 ## NewYork 748 713 1631 1420 2451 1092 0 ## SanFrancisco 2139 1858 949 1645 347 2594 2571 ## Seattle 2182 1737 1021 1891 959 2734 2408 ## Washington.DC 543 597 1494 1220 2300 923 205 ## SanFrancisco Seattle Washington.DC ## Atlanta 2139 2182 543 ## Chicago 1858 1737 597 ## Denver 949 1021 1494 ## Houston 1645 1891 1220 ## LosAngeles 347 959 2300 ## Miami 2594 2734 923 ## NewYork 2571 2408 205 ## SanFrancisco 0 678 2442 ## Seattle 678 0 2329 ## Washington.DC 2442 2329 0 However, until an sfl is created, skimr will not recognize the class and fall back to treating the data as if it were character data. skim(UScitiesD) ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character. ## Warning: Couldn't find skimmers for class: distance; No user-defined sfl ## provided. Falling back to character.  Name UScitiesD Number of rows 10 Number of columns 10 _______________________ Column type frequency: character 10 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_unique whitespace Atlanta 0 1 1 4 0 10 0 Chicago 0 1 1 4 0 10 0 Denver 0 1 1 4 0 10 0 Houston 0 1 1 4 0 10 0 LosAngeles 0 1 1 4 0 10 0 Miami 0 1 1 4 0 10 0 NewYork 0 1 1 4 0 10 0 SanFrancisco 0 1 1 4 0 10 0 Seattle 0 1 1 4 0 10 0 Washington.DC 0 1 1 4 0 10 0 The solution to this is to define an sfl (skimr function list) specifically for the distance class. ## Defining sfl’s for a package 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, like distance, 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 statistics throughout. These functions find the nearest and furthest other location for each location. One thing that is important to be aware of when creating statistics functions for skimr is that skimr largely uses tibbles rather than base data frames. This means that many base operations do not work as expected. get_nearest <- function(column) { closest <- which.min(column[column != 0]) cities <- attr(column, "nms")[column != 0] toString(cities[closest]) } get_furthest <- function(column) { furthest <- which.max(column[column != 0]) cities <- attr(column, "nms")[column != 0] toString(cities[furthest]) } 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, or in R terminology, a vector of length 1. 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. In the case of these functions, there could be ties which would result in returning vectors that have length greater than 1. This is handled by collapsing all of the tied values into a single string. Notice, also, that in the case of distance data we may wish to exclude distances of 0, which indicate the distance from a place to itself. In finding the minimum our function looks only at the distance to other places. There are at least two ways that you might want to customize skimr handling of a special data type within a package or your own work. The first is to create a custom skimming function. skim_with_dist <- skim_with( distance = sfl( nearest = get_nearest, furthest = get_furthest ) ) ## Creating new skimming functions for the following classes: distance. ## They did not have recognized defaults. Call get_default_skimmers() for more information. skim_with_dist(UScitiesD)  Name UScitiesD Number of rows 10 Number of columns 10 _______________________ Column type frequency: distance 10 ________________________ Group variables None Variable type: distance skim_variable n_missing complete_rate nearest furthest Atlanta 0 1 Washington.DC Seattle Chicago 0 1 Atlanta SanFrancisco Denver 0 1 LosAngeles Miami Houston 0 1 Atlanta Seattle LosAngeles 0 1 SanFrancisco NewYork Miami 0 1 Atlanta Seattle NewYork 0 1 Washington.DC SanFrancisco SanFrancisco 0 1 LosAngeles Miami Seattle 0 1 SanFrancisco Miami Washington.DC 0 1 NewYork SanFrancisco The example above creates a new function, and you can call that function on a specific column with distance data to get the appropriate summary statistics. The skim_with factory also uses the default skimrs for things like factors, characters, and numerics. Therefore our skim_with_dist is like the regular skim function with the added ability to summarize distance columns. While this works for any data type and you can also include it within any package (assuming your users load skimr), there is a second, even better, approach. To take full advantage of skimr, we’ll dig a bit into its API. ## Adding new methods 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. This requires that you add skimr to your list of dependencies. 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 (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. You will probably want to use skimr::get_skimmers.distance() but that will not work in a vignette. In a package you will want to export this. #' @importFrom skimr get_skimmers #' @export get_skimmers.distance <- function(column) { sfl( skim_type = "distance", nearest = get_nearest, furthest = get_furthest ) } The same strategy follows for other data types. • Create a method • return an sfl • make sure that the skim_type is included. Users of your package should load skimr to get the skim() function (although you could import and reexport it). 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"
##
## $Timespan ## [1] "min" "max" "median" "n_unique" ## ##$character
## [1] "min"        "max"        "empty"      "n_unique"   "whitespace"
##
## $complex ## [1] "mean" ## ##$difftime
## [1] "min"      "max"      "median"   "n_unique"
##
## $distance ## [1] "nearest" "furthest" ## ##$factor
## [1] "ordered"    "n_unique"   "top_counts"
##
## $haven_labelled ## [1] "mean" "sd" "p0" "p25" "p50" "p75" "p100" "hist" ## ##$list
## [1] "n_unique"   "min_length" "max_length"
##
## $logical ## [1] "mean" "count" ## ##$numeric
## [1] "mean" "sd"   "p0"   "p25"  "p50"  "p75"  "p100" "hist"
##
## \$ts
##  [1] "start"      "end"        "frequency"  "deltat"     "mean"
##  [6] "sd"         "min"        "max"        "median"     "line_graph"

They will then be able to use skim() directly.

skim(UScitiesD)
 Name UScitiesD Number of rows 10 Number of columns 10 _______________________ Column type frequency: distance 10 ________________________ Group variables None

Variable type: distance

skim_variable n_missing complete_rate nearest furthest
Atlanta 0 1 Washington.DC Seattle
Chicago 0 1 Atlanta SanFrancisco
Denver 0 1 LosAngeles Miami
Houston 0 1 Atlanta Seattle
LosAngeles 0 1 SanFrancisco NewYork
Miami 0 1 Atlanta Seattle
NewYork 0 1 Washington.DC SanFrancisco
SanFrancisco 0 1 LosAngeles Miami
Seattle 0 1 SanFrancisco Miami
Washington.DC 0 1 NewYork SanFrancisco

## Conclusion

This is a very simple example. For some packages 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, Michael Sumner, and Kyle Butts for inspiring and helping with the development of this code.