These functions are used to set the default skimming functions for a data type. They are combined with the base skim function list (sfl) in skim_with(), to create the summary tibble for each type.

get_skimmers(column)

# S3 method for default
get_skimmers(column)

# S3 method for numeric
get_skimmers(column)

# S3 method for factor
get_skimmers(column)

# S3 method for character
get_skimmers(column)

# S3 method for logical
get_skimmers(column)

# S3 method for complex
get_skimmers(column)

# S3 method for Date
get_skimmers(column)

# S3 method for POSIXct
get_skimmers(column)

# S3 method for difftime
get_skimmers(column)

# S3 method for Timespan
get_skimmers(column)

# S3 method for ts
get_skimmers(column)

# S3 method for list
get_skimmers(column)

# S3 method for AsIs
get_skimmers(column)

modify_default_skimmers(skim_type, new_skim_type = NULL, new_funs = list())

Arguments

column

An atomic vector or list. A column from a data frame.

skim_type

A character scalar. The class of the object with default skimmers.

new_skim_type

The type to assign to the looked up set of skimmers.

new_funs

Replacement functions for those in

Value

A skim_function_list object.

Details

When creating your own set of skimming functions, call sfl() within a get_skimmers() method for your particular type. Your call to sfl() should also provide a matching class in the skim_type argument. Otherwise, it will not be possible to dynamically reassign your default functions when working interactively.

Call get_default_skimmers() to see the functions for each type of summary function currently supported. Call get_default_skimmer_names() to just see the names of these functions. Use modify_default_skimmers() for a method for changing the skim_type or functions for a default sfl. This is useful for creating new default sfl's.

Methods (by class)

See also

Examples

# Defining default skimming functions for a new class, `my_class`.
# Note that the class argument is required for dynamic reassignment.
get_skimmers.my_class <- function(column) {
  sfl(
    skim_type = "my_class",
    mean,
    sd
  )
}

# Integer and double columns are both "numeric" and are treated the same
# by default. To switch this behavior in another package, add a method.
get_skimmers.integer <- function(column) {
  sfl(
    skim_type = "integer",
    p50 = ~ stats::quantile(
      .,
      probs = .50, na.rm = TRUE, names = FALSE, type = 1
    )
  )
}
x <- mtcars[c("gear", "carb")]
class(x$carb) <- "integer"
skim(x)
#> ── Data Summary ────────────────────────
#>                            Values
#> Name                       x     
#> Number of rows             32    
#> Number of columns          2     
#> _______________________          
#> Column type frequency:           
#>   numeric                  2     
#> ________________________         
#> Group variables            None  
#> 
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#> 
if (FALSE) {
# In a package, to revert to the V1 behavior of skimming separately with the
# same functions, assign the numeric `get_skimmers`.
get_skimmers.integer <- skimr::get_skimmers.numeric

# Or, in a local session, use `skim_with` to create a different `skim`.
new_skim <- skim_with(integer = skimr::get_skimmers.numeric())

# To apply a set of skimmers from an old type to a new type
get_skimmers.new_type <- function(column) {
  modify_default_skimmers("old_type", new_skim_type = "new_type")
}
}