Retrieve the summary functions for a specific data type
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) ## Default S3 method: get_skimmers(column) ## S3 method for class 'numeric' get_skimmers(column) ## S3 method for class 'factor' get_skimmers(column) ## S3 method for class 'character' get_skimmers(column) ## S3 method for class 'logical' get_skimmers(column) ## S3 method for class 'complex' get_skimmers(column) ## S3 method for class 'Date' get_skimmers(column) ## S3 method for class 'POSIXct' get_skimmers(column) ## S3 method for class 'difftime' get_skimmers(column) ## S3 method for class 'Timespan' get_skimmers(column) ## S3 method for class 'ts' get_skimmers(column) ## S3 method for class 'list' get_skimmers(column) ## S3 method for class 'AsIs' get_skimmers(column) modify_default_skimmers(skim_type, new_skim_type = NULL, new_funs = list())
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 |
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.
A skim_function_list object.
default: The default method for skimming data. Only used when
a column's data type doesn't match currently installed types. Call
get_default_skimmer_names to see these defaults.
numeric: Summary functions for numeric columns, covering both
double() and integer() classes: mean(), sd(), quantile() and
inline_hist().
factor: Summary functions for factor columns:
is.ordered(), n_unique() and top_counts().
character: Summary functions for character columns. Also, the
default for unknown columns: min_char(), max_char(), n_empty(),
n_unique() and n_whitespace().
logical: Summary functions for logical/ boolean columns:
mean(), which produces rates for each value, and top_counts().
complex: Summary functions for complex columns: mean().
Date: Summary functions for Date columns: min(),
max(), median() and n_unique().
POSIXct: Summary functions for POSIXct columns: min(),
max(), median() and n_unique().
difftime: Summary functions for difftime columns: min(),
max(), median() and n_unique().
Timespan: Summary functions for Timespan columns: min(),
max(), median() and n_unique().
ts: Summary functions for ts columns: min(),
max(), median() and n_unique().
list: Summary functions for list columns: n_unique(),
list_min_length() and list_max_length().
AsIs: Summary functions for AsIs columns: n_unique(),
list_min_length() and list_max_length().
# 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)
## Not run:
# 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")
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.