Set or add the summary functions for a particular type of data

While skim is designed around having an opinionated set of defaults, you can use this function to change the summary statistics that it returns.

Usage

skim_with(
  ...,
  base = sfl(n_missing = n_missing, complete_rate = complete_rate),
  append = TRUE
)

Arguments

...

One or more (sfl) skimmer_function_list objects, with an argument name that matches a particular data type.

base

An sfl that sets skimmers for all column types.

append

Whether the provided options should be in addition to the defaults already in skim. Default is TRUE.

Value

A new skim() function. This is callable. See skim() for more details.

Details

skim_with() is a closure: a function that returns a new function. This lets you have several skimming functions in a single R session, but it also means that you need to assign the return of skim_with() before you can use it.

You assign values within skim_with by using the sfl() helper (skimr function list). This helper behaves mostly like dplyr::funs(), but lets you also identify which skimming functions you want to remove, by setting them to NULL. Assign an sfl to each column type that you wish to modify.

Functions that summarize all data types, and always return the same type of value, can be assigned to the base argument. The default base skimmers compute the number of missing values n_missing() and the rate of values being complete, i.e. not missing, complete_rate().

When append = TRUE and local skimmers have names matching the names of entries in the default skim_function_list, the values in the default list are overwritten. Similarly, if NULL values are passed within sfl(), these default skimmers are dropped. Otherwise, if append = FALSE, only the locally-provided skimming functions are used.

Note that append only applies to the typed skimmers (i.e. non-base). See get_default_skimmer_names() for a list of defaults.

Examples

# Use new functions for numeric functions. If you don't provide a name,
# one will be automatically generated.
my_skim <- skim_with(numeric = sfl(median, mad), append = FALSE)
my_skim(faithful)
#> ── Data Summary ────────────────────────
#>                            Values  
#> 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 median    mad
#> 1 eruptions             0             1      4  0.951
#> 2 waiting               0             1     76 11.9  

# If you want to remove a particular skimmer, set it to NULL
# This removes the inline histogram
my_skim <- skim_with(numeric = sfl(hist = NULL))
my_skim(faithful)
#> ── Data Summary ────────────────────────
#>                            Values  
#> 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
#> 1 eruptions             0             1  3.49  1.14  1.6  2.16   4  4.45  5.1
#> 2 waiting               0             1 70.9  13.6  43   58     76 82    96  

# This works with multiple skimmers. Just match names to overwrite
my_skim <- skim_with(numeric = sfl(iqr = IQR, p25 = NULL, p75 = NULL))
my_skim(faithful)
#> ── Data Summary ────────────────────────
#>                            Values  
#> 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 p50 p100 hist    iqr
#> 1 eruptions             0             1  3.49  1.14  1.6   4  5.1 ▇▂▂▇▇  2.29
#> 2 waiting               0             1 70.9  13.6  43    76 96   ▃▃▂▇▂ 24   

# Alternatively, set `append = FALSE` to replace the skimmers of a type.
my_skim <- skim_with(numeric = sfl(mean = mean, sd = sd), append = FALSE)

# Skimmers are unary functions. Partially apply arguments during assigment.
# For example, you might want to remove NA values.
my_skim <- skim_with(numeric = sfl(iqr = ~ IQR(., na.rm = TRUE)))

# Set multiple types of skimmers simultaneously.
my_skim <- skim_with(numeric = sfl(mean), character = sfl(length))

# Or pass the same as a list, unquoting the input.
my_skimmers <- list(numeric = sfl(mean), character = sfl(length))
my_skim <- skim_with(!!!my_skimmers)

# Use the v1 base skimmers instead.
my_skim <- skim_with(base = sfl(
  missing = n_missing,
  complete = n_complete,
  n = length
))

# Remove the base skimmers entirely
my_skim <- skim_with(base = NULL)