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)

Variable type: factor

Variable type: numeric

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
   skim(.data)
}

lm(weight ~ feed, data = chickwts) %>% skim_lm()

Variable type: factor

Variable type: numeric

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$model
   if (fit == TRUE){
   .data <- .data %>% 
            dplyr::bind_cols(
              fitted =
                data.frame(results$fitted.values),
              residuals =
                data.frame(results$residuals))
   }
   skim(.data)
}

skim_lm(results, fit = TRUE)

Variable type: factor

Variable type: numeric

A second example of the need for a special function is with dist objects. The UScitiesD dat 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 comples, 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))
     To <- colnames(.data)

    .data <- lapply(.data[,To], 
                    `class<-`, "distance")
    .data <- dplyr::bind_rows(.data )
    .data<- dplyr::bind_cols(.data)

    .data
}
as.data.frame(UScitiesD)

## # A tibble: 10 × 10
##    Atlanta  Chicago Denver Houston LosAngeles Miami NewYork SanFrancisco Seattle
##    <distan> <dista> <dist> <dista> <distance> <dis> <dista> <distance>   <dista>
##  1    0      587    1212    701    1936        604   748    2139         2182   
##  2  587        0     920    940    1745       1188   713    1858         1737   
##  3 1212      920       0    879     831       1726  1631     949         1021   
##  4  701      940     879      0    1374        968  1420    1645         1891   
##  5 1936     1745     831   1374       0       2339  2451     347          959   
##  6  604     1188    1726    968    2339          0  1092    2594         2734   
##  7  748      713    1631   1420    2451       1092     0    2571         2408   
##  8 2139     1858     949   1645     347       2594  2571       0          678   
##  9 2182     1737    1021   1891     959       2734  2408     678            0   
## 10  543      597    1494   1220    2300        923   205    2442         2329   
## # … with 1 more variable: Washington.DC <distance>

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`.

Variable type: character

The solution to this is to define an sfl (skimr function list) specifically for the distance class.

get_nearest <- function(column) {
  column <- as.numeric(column)
  closest <- min(column[column != 0])
  which_rows <- which(column == closest, arr.ind = TRUE)
  which_rows <- as.vector((which_rows))
  paste(column[which_rows], collapse = ",")
}

get_furthest <- function(column) {
  column <- as.numeric(column)
  furthest <- max(column[column != 0])
  which_rows <- which(column == furthest, arr.ind = TRUE)
  which_rows <- as.vector(which_rows)
  paste(column[which_rows], collapse = ",")

}

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)

#' @importFrom skimr get_skimmers
#' @export
get_skimmers.distance <- function(column) {
  sfl(
    skim_type = "distance",
    nearest = get_nearest,
    furthest = get_furthest
  )
}

get_default_skimmer_names()

## $AsIs
## [1] "n_unique"   "min_length" "max_length"
## 
## $character
## [1] "min"        "max"        "empty"      "n_unique"   "whitespace"
## 
## $complex
## [1] "mean"
## 
## $Date
## [1] "min"      "max"      "median"   "n_unique"
## 
## $difftime
## [1] "min"      "max"      "median"   "n_unique"
## 
## $distance
## [1] "nearest"  "furthest"
## 
## $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"
## 
## $POSIXct
## [1] "min"      "max"      "median"   "n_unique"
## 
## $Timespan
## [1] "min"      "max"      "median"   "n_unique"
## 
## $ts
##  [1] "start"      "end"        "frequency"  "deltat"     "mean"      
##  [6] "sd"         "min"        "max"        "median"     "line_graph"

skim(UScitiesD)