Using Skimr

Introduction

skimr is designed to provide summary statistics about variables in data frames, tibbles, data tables and vectors. It is opinionated in its defaults, but easy to modify.

In base R, the most similar functions are summary() for vectors and data frames and fivenum() for numeric vectors:

summary(iris)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

summary(iris$Sepal.Length)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.300   5.100   5.800   5.843   6.400   7.900

fivenum(iris$Sepal.Length)

## [1] 4.3 5.1 5.8 6.4 7.9

summary(iris$Species)

##     setosa versicolor  virginica 
##         50         50         50

The `skim()` function

The core function of skimr is skim(), which is designed to work with (grouped) data frames, and will try coerce other objects to data frames if possible. Like summary(), skim()’s method for data frames presents results for every column; the statistics it provides depend on the class of the variable.

Skimming data frames

By design, the main focus of skimr is on data frames; it is intended to fit well within a data pipeline and relies extensively on tidyverse vocabulary, which focuses on data frames.

Results of skim() are printed horizontally, with one section per variable type and one row per variable.

library(skimr)
skim(iris)

## ── Data Summary ────────────────────────
##                            Values
## 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
## 1 Species               0             1 FALSE          3
##   top_counts               
## 1 set: 50, ver: 50, vir: 50
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean    sd  p0 p25  p50 p75 p100 hist 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8  6.4  7.9 ▆▇▇▅▂
## 2 Sepal.Width           0             1 3.06 0.436 2   2.8 3    3.3  4.4 ▁▆▇▂▁
## 3 Petal.Length          0             1 3.76 1.77  1   1.6 4.35 5.1  6.9 ▇▁▆▇▂
## 4 Petal.Width           0             1 1.20 0.762 0.1 0.3 1.3  1.8  2.5 ▇▁▇▅▃

The format of the results are a single wide data frame combining the results, with some additional attributes and two metadata columns:

skim_variable: name of the original variable
skim_type: class of the variable

Unlike many other objects within R, these columns are intrinsic to the skim_df class. Dropping these variables will result in a coercion to a tibble. The is_skim_df() function is used to assert that an object is a skim_df.

skim(iris) %>% is_skim_df()

## [1] TRUE
## attr(,"message")
## character(0)

skim(iris) %>%
  dplyr::select(-skim_type, -skim_variable) %>% is_skim_df()

## [1] FALSE
## attr(,"message")
## [1] "Object is not a `skim_df`: missing column `skim_type`; missing column `skim_variable`"

skim(iris) %>%
  dplyr::select(-n_missing) %>% is_skim_df()

## [1] TRUE
## attr(,"message")
## character(0)

In order to avoid type coercion, columns for summary statistics for different types are prefixed with the corresponding skim_type. This means that the columns of the skim_df are somewhat sparse, with quite a few missing values. This is because for some statistics the representations for different types of variables is different. For example, the mean of a Date variable and of a numeric variable are represented differently when printing, but this cannot be supported in a single vector. The exception to this are n_missing and complete_rate (missing/number of observations) which are the same for all types of variables.

skim(iris) %>%
  tibble::as_tibble()

## # A tibble: 5 × 15
##   skim_type skim_variable n_missing complete_rate factor.ordered factor.n_unique
##   <chr>     <chr>             <int>         <dbl> <lgl>                    <int>
## 1 factor    Species               0             1 FALSE                        3
## 2 numeric   Sepal.Length          0             1 NA                          NA
## 3 numeric   Sepal.Width           0             1 NA                          NA
## 4 numeric   Petal.Length          0             1 NA                          NA
## 5 numeric   Petal.Width           0             1 NA                          NA
## # … with 9 more variables: factor.top_counts <chr>, numeric.mean <dbl>,
## #   numeric.sd <dbl>, numeric.p0 <dbl>, numeric.p25 <dbl>, numeric.p50 <dbl>,
## #   numeric.p75 <dbl>, numeric.p100 <dbl>, numeric.hist <chr>

This is in contrast to summary.data.frame(), which stores statistics in a table. The distinction is important, because the skim_df object is pipeable and easy to use for additional manipulation: for example, the user could select all of the variable means, or all summary statistics for a specific variable.

skim(iris) %>%
  dplyr::filter(skim_variable == "Petal.Length")

## ── Data Summary ────────────────────────
##                            Values
## 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 mean   sd p0 p25  p50 p75 p100 hist 
## 1 Petal.Length          0             1 3.76 1.77  1 1.6 4.35 5.1  6.9 ▇▁▆▇▂

Most dplyr verbs should work as expected.

skim(iris) %>%
  dplyr::select(skim_type, skim_variable, n_missing)

## ── Data Summary ────────────────────────
##                            Values
## 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
## 1 Species               0
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing
## 1 Sepal.Length          0
## 2 Sepal.Width           0
## 3 Petal.Length          0
## 4 Petal.Width           0

The base skimmers n_missing and complete_rate are computed for all of the columns in the data. But all other type-based skimmers have a namespace. You need to use a skim_type prefix to refer to correct column.

skim(iris) %>%
  dplyr::select(skim_type, skim_variable, numeric.mean)

## ── Data Summary ────────────────────────
##                            Values
## Name                       iris  
## Number of rows             150   
## Number of columns          5     
## _______________________          
## Column type frequency:           
##   factor                   1     
##   numeric                  4     
## ________________________         
## Group variables            None  
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable mean
## 1 Sepal.Length  5.84
## 2 Sepal.Width   3.06
## 3 Petal.Length  3.76
## 4 Petal.Width   1.20

skim() also supports grouped data created by dplyr::group_by(). In this case, one additional column for each grouping variable is added to the skim_df object.

iris %>%
  dplyr::group_by(Species) %>%
  skim()

## ── Data Summary ────────────────────────
##                            Values    
## 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
##  1 Sepal.Length  setosa             0             1 5.01  0.352 4.3 4.8  5   
##  2 Sepal.Length  versicolor         0             1 5.94  0.516 4.9 5.6  5.9 
##  3 Sepal.Length  virginica          0             1 6.59  0.636 4.9 6.22 6.5 
##  4 Sepal.Width   setosa             0             1 3.43  0.379 2.3 3.2  3.4 
##  5 Sepal.Width   versicolor         0             1 2.77  0.314 2   2.52 2.8 
##  6 Sepal.Width   virginica          0             1 2.97  0.322 2.2 2.8  3   
##  7 Petal.Length  setosa             0             1 1.46  0.174 1   1.4  1.5 
##  8 Petal.Length  versicolor         0             1 4.26  0.470 3   4    4.35
##  9 Petal.Length  virginica          0             1 5.55  0.552 4.5 5.1  5.55
## 10 Petal.Width   setosa             0             1 0.246 0.105 0.1 0.2  0.2 
## 11 Petal.Width   versicolor         0             1 1.33  0.198 1   1.2  1.3 
## 12 Petal.Width   virginica          0             1 2.03  0.275 1.4 1.8  2   
##     p75 p100 hist 
##  1 5.2   5.8 ▃▃▇▅▁
##  2 6.3   7   ▂▇▆▃▃
##  3 6.9   7.9 ▁▃▇▃▂
##  4 3.68  4.4 ▁▃▇▅▂
##  5 3     3.4 ▁▅▆▇▂
##  6 3.18  3.8 ▂▆▇▅▁
##  7 1.58  1.9 ▁▃▇▃▁
##  8 4.6   5.1 ▂▂▇▇▆
##  9 5.88  6.9 ▃▇▇▃▂
## 10 0.3   0.6 ▇▂▂▁▁
## 11 1.5   1.8 ▅▇▃▆▁
## 12 2.3   2.5 ▂▇▆▅▇

Individual columns from a data frame may be selected using tidyverse-style selectors.

skim(iris, Sepal.Length, Species)

## ── Data Summary ────────────────────────
##                            Values
## Name                       iris  
## Number of rows             150   
## Number of columns          5     
## _______________________          
## Column type frequency:           
##   factor                   1     
##   numeric                  1     
## ________________________         
## Group variables            None  
## 
## ── Variable type: factor ───────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate ordered n_unique
## 1 Species               0             1 FALSE          3
##   top_counts               
## 1 set: 50, ver: 50, vir: 50
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean    sd  p0 p25 p50 p75 p100 hist 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8 6.4  7.9 ▆▇▇▅▂

Or with common select helpers.

skim(iris, starts_with("Sepal"))

## ── Data Summary ────────────────────────
##                            Values
## 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 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8 6.4  7.9 ▆▇▇▅▂
## 2 Sepal.Width           0             1 3.06 0.436 2   2.8 3   3.3  4.4 ▁▆▇▂▁

If an individual column is of an unsupported class, it is treated as a character variable with a warning.

Skimming vectors

In skimr v2, skim() will attempt to coerce non-data frames (such as vectors and matrices) to data frames. In most cases with vectors, the object being evaluated should be equivalent to wrapping the object in as.data.frame().

For example, the lynx data set is class ts.

skim(lynx)

## ── Data Summary ────────────────────────
##                            Values
## Name                       lynx  
## Number of rows             114   
## Number of columns          1     
## _______________________          
## Column type frequency:           
##   ts                       1     
## ________________________         
## Group variables            None  
## 
## ── Variable type: ts ───────────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate start  end frequency deltat  mean    sd
## 1 x                     0             1  1821 1934         1      1 1538. 1586.
##   min  max median line_graph
## 1  39 6991    771 ⡈⢄⡠⢁⣀⠒⣀⠔

Which is the same as coercing to a data frame.

all.equal(skim(lynx), skim(as.data.frame(lynx)))

## [1] "Attributes: < Component \"df_name\": 1 string mismatch >"

Skimming matrices

skimr does not support skimming matrices directly but coerces them to data frames. Columns in the matrix become variables. This behavior is similar to summary.matrix()). Three possible ways to handle matrices with skim() parallel the three variations of the mean function for matrices.

m <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrow = 4, ncol = 3)
m

##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12

Skimming the matrix produces similar results to colMeans().

colMeans(m)

## [1]  2.5  6.5 10.5

skim(m) # Similar to summary.matrix and colMeans()

## ── Data Summary ────────────────────────
##                            Values
## Name                       m     
## Number of rows             4     
## Number of columns          3     
## _______________________          
## Column type frequency:           
##   numeric                  3     
## ________________________         
## Group variables            None  
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean   sd p0  p25  p50   p75 p100 hist 
## 1 V1                    0             1  2.5 1.29  1 1.75  2.5  3.25    4 ▇▇▁▇▇
## 2 V2                    0             1  6.5 1.29  5 5.75  6.5  7.25    8 ▇▇▁▇▇
## 3 V3                    0             1 10.5 1.29  9 9.75 10.5 11.2    12 ▇▇▁▇▇

Skimming the transpose of the matrix will give row-wise results.

rowMeans(m)

## [1] 5 6 7 8

skim(t(m))

## ── Data Summary ────────────────────────
##                            Values
## Name                       t(m)  
## Number of rows             3     
## Number of columns          4     
## _______________________          
## Column type frequency:           
##   numeric                  4     
## ________________________         
## Group variables            None  
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist 
## 1 V1                    0             1    5  4  1   3   5   7    9 ▇▁▇▁▇
## 2 V2                    0             1    6  4  2   4   6   8   10 ▇▁▇▁▇
## 3 V3                    0             1    7  4  3   5   7   9   11 ▇▁▇▁▇
## 4 V4                    0             1    8  4  4   6   8  10   12 ▇▁▇▁▇

And call c() on the matrix to get results across all columns.

skim(c(m))

## ── Data Summary ────────────────────────
##                            Values
## Name                       c(m)  
## Number of rows             12    
## Number of columns          1     
## _______________________          
## Column type frequency:           
##   numeric                  1     
## ________________________         
## Group variables            None  
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean   sd p0  p25 p50  p75 p100 hist 
## 1 data                  0             1  6.5 3.61  1 3.75 6.5 9.25   12 ▇▅▅▅▇

mean(m)

## [1] 6.5

Skimming without modification

skim_tee() produces the same printed version as skim() but returns the original, unmodified data frame. This allows for continued piping of the original data.

iris_setosa <- iris %>%
  skim_tee() %>%
  dplyr::filter(Species == "setosa")

## ── Data Summary ────────────────────────
##                            Values
## Name                       data  
## 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
## 1 Species               0             1 FALSE          3
##   top_counts               
## 1 set: 50, ver: 50, vir: 50
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean    sd  p0 p25  p50 p75 p100 hist 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8  6.4  7.9 ▆▇▇▅▂
## 2 Sepal.Width           0             1 3.06 0.436 2   2.8 3    3.3  4.4 ▁▆▇▂▁
## 3 Petal.Length          0             1 3.76 1.77  1   1.6 4.35 5.1  6.9 ▇▁▆▇▂
## 4 Petal.Width           0             1 1.20 0.762 0.1 0.3 1.3  1.8  2.5 ▇▁▇▅▃

head(iris_setosa)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

Note, that skim_tee() is customized differently than skim itself. See below for more details.

Reshaping the results from `skim()`

As noted above, skim() returns a wide data frame. This is usually the most sensible format for the majority of operations when investigating data, but the package has some other functions to help with edge cases.

First, partition() returns a named list of the wide data frames for each data type. Unlike the original data the partitioned data only has columns corresponding to the skimming functions used for this data type. These data frames are, therefore, not skim_df objects.

iris %>%
  skim() %>%
  partition()

## $factor
## 
## ── Variable type: factor ───────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate ordered n_unique top_counts             
## 1 Species               0             1 FALSE          3 set: 50, ver: 50, vir:…
## 
## $numeric
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean    sd  p0 p25  p50 p75 p100 hist 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8  6.4  7.9 ▆▇▇▅▂
## 2 Sepal.Width           0             1 3.06 0.436 2   2.8 3    3.3  4.4 ▁▆▇▂▁
## 3 Petal.Length          0             1 3.76 1.77  1   1.6 4.35 5.1  6.9 ▇▁▆▇▂
## 4 Petal.Width           0             1 1.20 0.762 0.1 0.3 1.3  1.8  2.5 ▇▁▇▅▃

Alternatively, yank() selects only the subtable for a specific type. Think of it like dplyr::select on column types in the original data. Again, unsuitable columns are dropped.

iris %>%
  skim() %>%
  yank("numeric")

## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing complete_rate mean    sd  p0 p25  p50 p75 p100 hist 
## 1 Sepal.Length          0             1 5.84 0.828 4.3 5.1 5.8  6.4  7.9 ▆▇▇▅▂
## 2 Sepal.Width           0             1 3.06 0.436 2   2.8 3    3.3  4.4 ▁▆▇▂▁
## 3 Petal.Length          0             1 3.76 1.77  1   1.6 4.35 5.1  6.9 ▇▁▆▇▂
## 4 Petal.Width           0             1 1.20 0.762 0.1 0.3 1.3  1.8  2.5 ▇▁▇▅▃

to_long() returns a single long data frame with columns variable, type, statistic and formatted. This is similar but not identical to the skim_df object in skimr v1.

iris %>%
  skim() %>%
  to_long() %>% 
  head()

## # A tibble: 6 × 4
##   skim_type skim_variable stat          formatted
##   <chr>     <chr>         <chr>         <chr>    
## 1 factor    Species       n_missing     0        
## 2 numeric   Sepal.Length  n_missing     0        
## 3 numeric   Sepal.Width   n_missing     0        
## 4 numeric   Petal.Length  n_missing     0        
## 5 numeric   Petal.Width   n_missing     0        
## 6 factor    Species       complete_rate 1

Since the skim_variable and skim_type columns are a core component of the skim_df class, it’s possible to get unwanted side effects when using dplyr::select(). Instead, use focus() to select columns of the skimmed results and keep them as a skim_df; it always keeps the metadata column.

iris %>%
  skim() %>%
  focus(n_missing, numeric.mean)

## ── Data Summary ────────────────────────
##                            Values    
## Name                       Piped data
## 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
## 1 Species               0
## 
## ── Variable type: numeric ──────────────────────────────────────────────────────
##   skim_variable n_missing mean
## 1 Sepal.Length          0 5.84
## 2 Sepal.Width           0 3.06
## 3 Petal.Length          0 3.76
## 4 Petal.Width           0 1.20

Rendering the results of `skim()`

The skim_df object is a wide data frame. The display is created by default using print.skim_df(); users can specify additional options by explicitly calling print([skim_df object], ...).

For documents rendered by knitr, the package provides a custom knit_print method. To use it, the final line of your code chunk should have a skim_df object.

skim(Orange)

Data summary
Name	Orange
Number of rows	35
Number of columns	3
_______________________
Column type frequency:
factor	1
numeric	2
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
Tree	0	1	TRUE	5	3: 7, 1: 7, 5: 7, 2: 7

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	0	1	922.14	491.86	118	484.0	1004	1372.0	1582	▃▇▁▇▇
circumference	0	1	115.86	57.49	30	65.5	115	161.5	214	▇▃▇▇▅

The same type of rendering is available from reshaped skim_df objects, those generated by partition() and yank() in particular.

skim(Orange) %>%
  yank("numeric")

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
age	0	1	922.14	491.86	118	484.0	1004	1372.0	1582	▃▇▁▇▇
circumference	0	1	115.86	57.49	30	65.5	115	161.5	214	▇▃▇▇▅

Customizing print options

Although its not a common use case outside of writing vignettes about skimr, you can fall back to default printing methods by adding the chunk option render = knitr::normal_print.

You can also disable the skimr summary by setting the chunk option skimr_include_summary = FALSE.

You can change the number of digits shown in the columns of generated statistics by changing the skimr_digits chunk option.

Modifying `skim()`

skimr is opinionated in its choice of defaults, but users can easily add, replace, or remove the statistics for a class. For interactive use, you can create your own skimming function with the skim_with() factory. skimr also has an API for extensions in other packages. Working with that is covered later.

To add a statistic for a data type, create an sfl() (a skimr function list) for each class that you want to change:

my_skim <- skim_with(numeric = sfl(new_mad = mad))
my_skim(faithful)

Data summary
Name	faithful
Number of rows	272
Number of columns	2
_______________________
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	new_mad
eruptions	0	1	3.49	1.14	1.6	2.16	4	4.45	5.1	▇▂▂▇▇	0.95
waiting	0	1	70.90	13.59	43.0	58.00	76	82.00	96.0	▃▃▂▇▂	11.86

As the previous example suggests, the default is to append new summary statistics to the preexisting set. This behavior isn’t always desirable, especially when you want lots of changes. To stop appending, set append = FALSE.

my_skim <- skim_with(numeric = sfl(new_mad = mad), append = FALSE)
my_skim(faithful)

Data summary
Name	faithful
Number of rows	272
Number of columns	2
_______________________
Column type frequency:
numeric	2
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	new_mad
eruptions	0	1	0.95
waiting	0	1	11.86

You can also use skim_with() to remove specific statistics by setting them to NULL. This is commonly used to disable the inline histograms and spark graphs.

no_hist <- skim_with(ts = sfl(line_graph = NULL))
no_hist(Nile)

Data summary
Name	Nile
Number of rows	100
Number of columns	1
_______________________
Column type frequency:
ts	1
________________________
Group variables	None

Variable type: ts

skim_variable	n_missing	complete_rate	start	end	frequency	deltat	mean	sd	min	max	median
x	0	1	1871	1970	1	1	919.35	169.23	456	1370	893.5

The same pattern applies to changing skimmers for multiple classes simultaneously. If you want to partially-apply function arguments, use the Tidyverse lambda syntax.

my_skim <- skim_with(
  numeric = sfl(total = ~ sum(., na.rm = TRUE)),
  factor = sfl(missing = ~ sum(is.na(.))),
  append = FALSE
)

my_skim(iris)

Data summary
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	missing
Species	0	1	0

Variable type: numeric

skim_variable	complete_rate	total
Sepal.Length	1	876.5
Sepal.Width	1	458.6
Petal.Length	1	563.7
Petal.Width	1	179.9

To modify the “base” skimmers, refer to them in a similar manner. Since base skimmers are usually a small group, they must return the same type for all data types in R, append doesn’t apply here.

my_skim <- skim_with(base = sfl(length = length))
my_skim(faithful)

Data summary
Name	faithful
Number of rows	272
Number of columns	2
_______________________
Column type frequency:
numeric	2
________________________
Group variables	None

Variable type: numeric

skim_variable	length	mean	sd	p0	p25	p50	p75	p100	hist
eruptions	272	3.49	1.14	1.6	2.16	4	4.45	5.1	▇▂▂▇▇
waiting	272	70.90	13.59	43.0	58.00	76	82.00	96.0	▃▃▂▇▂

Extending `skimr`

Packages may wish to export their own skim() functions. Use skim_with() for this. In fact, this is how skimr generates its version of skim().

#' @export
my_package_skim <- skim_with()

Alternatively, defaults for another data types can be added to skimr with the get_skimmers generic. The method for your data type should return an sfl(). Unlike the sfl() used interactively, you also need to set the skim_type argument. It should match the method type in the function signature.

get_skimmers.my_type <- function(column) {
  sfl(
    skim_type = "my_type",
    total = sum
  )
}

my_data <- data.frame(
  my_type = structure(1:3, class = c("my_type", "integer"))
)
skim(my_data)

Data summary
Name	my_data
Number of rows	3
Number of columns	1
_______________________
Column type frequency:
my_type	1
________________________
Group variables	None

Variable type: my_type

skim_variable	n_missing	complete_rate	total
my_type	0	1	6

An extended example is available in the vignette Supporting additional objects.

Solutions to common rendering problems

The details of rendering are dependent on the operating system R is running on, the locale of the installation, and the fonts installed. Rendering may also differ based on whether it occurs in the console or when knitting to specific types of documents such as HTML and PDF.

The most commonly reported problems involve rendering the spark graphs (inline histogram and line chart) on Windows. One common fix is to switch your locale. The function fix_windows_histograms() does this for you.

In order to render the sparkgraphs in html or PDF histogram you may need to change fonts to one that supports blocks or Braille (depending on which you need). Please review the separate vignette and associated template for details.

Using Skimr

2022-12-23

Introduction

The skim() function

Skimming data frames

Skimming vectors

Skimming matrices

Skimming without modification

Reshaping the results from skim()

Rendering the results of skim()

Customizing print options

Modifying skim()

Extending skimr

Solutions to common rendering problems

The `skim()` function

Reshaping the results from `skim()`

Rendering the results of `skim()`

Modifying `skim()`

Extending `skimr`