descriptives | R Documentation |
The user can specify an unlimited number of functions to evaluate and the types of data that each set of functions will be applied to (including the default; see "Details").
descriptives( data, f_all = NULL, f_numeric = NULL, numeric_types = "numeric", f_categorical = NULL, categorical_types = "factor", f_other = NULL, useNA = c("ifany", "no", "always"), round = 2, na_string = "(missing)" )
data |
A |
f_all |
A |
f_numeric |
A |
numeric_types |
Character vector of data types that should be evaluated by |
f_categorical |
A |
categorical_types |
Character vector of data types that should be evaluated by |
f_other |
A |
useNA |
See |
round |
Digit to round numeric data. Defaults to |
na_string |
String to fill in |
The following fun_key
's are available by default for the specified types:
ALL: length, missing, available, class, unique
Numeric: mean, sd, min, q1, median, q3, max, iqr, range
Categorical: count, proportion, percent
A tibble::tibble
with the following columns:
fun_eval
: Column types function was applied to
fun_key
: Name of function that was evaluated
col_ind
: Index from input dataset
col_lab
: Label of the column
val_ind
: Index of the value within the function result
val_lab
: Label extracted from the result with names
val_dbl
: Numeric result
val_chr
: Non-numeric result
val_cbn
: Combination of (rounded) numeric and non-numeric values
Alex Zajichek
#Default heart_disease %>% descriptives() #Allow logicals as categorical heart_disease %>% descriptives( categorical_types = c("logical", "factor") ) %>% #Extract info from the column dplyr::filter( col_lab == "BloodSugar" ) #Nothing treated as numeric heart_disease %>% descriptives( numeric_types = NULL ) #Evaluate a custom function heart_disease %>% descriptives( f_numeric = list( cv = function(x) sd(x, na.rm = TRUE)/mean(x, na.rm = TRUE) ) ) %>% #Extract info from the custom function dplyr::filter( fun_key == "cv" )
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