descriptives.RdThe 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)"
)A data.frame.
A list of functions to evaluate on all columns.
A list of functions to evaluate on numeric_types columns.
Character vector of data types that should be evaluated by f_numeric.
A list of functions to evaluate on categorical_types columns.
Character vector of data types that should be evaluated by f_categorical.
A list of functions to evaluate on remaining columns.
See table for details. Defaults to "ifany".
Digit to round numeric data. Defaults to 2.
String to fill in NA names.
The following fun_key's are available by default for the specified types:
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
#Default
heart_disease %>%
descriptives()
#> # A tibble: 111 × 9
#> fun_eval fun_key col_ind col_lab val_ind val_lab val_dbl val_chr val_cbn
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <chr> <chr>
#> 1 all available 1 Age 1 NA 303 NA 303
#> 2 all available 2 Sex 1 NA 303 NA 303
#> 3 all available 3 ChestPain 1 NA 303 NA 303
#> 4 all available 4 BP 1 NA 303 NA 303
#> 5 all available 5 Cholester… 1 NA 303 NA 303
#> 6 all available 6 BloodSugar 1 NA 303 NA 303
#> 7 all available 7 MaximumHR 1 NA 303 NA 303
#> 8 all available 8 ExerciseI… 1 NA 303 NA 303
#> 9 all available 9 HeartDise… 1 NA 303 NA 303
#> 10 all class 1 Age 1 NA NA numeric numeric
#> # … with 101 more rows
#Allow logicals as categorical
heart_disease %>%
descriptives(
categorical_types = c("logical", "factor")
) %>%
#Extract info from the column
dplyr::filter(
col_lab == "BloodSugar"
)
#> # A tibble: 11 × 9
#> fun_eval fun_key col_ind col_lab val_ind val_lab val_dbl val_chr val_cbn
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <chr> <chr>
#> 1 all available 6 BloodS… 1 NA 303 NA 303
#> 2 all class 6 BloodS… 1 NA NA logical logical
#> 3 all length 6 BloodS… 1 NA 303 NA 303
#> 4 all missing 6 BloodS… 1 NA 0 NA 0
#> 5 all unique 6 BloodS… 1 NA 2 NA 2
#> 6 categorical count 6 BloodS… 1 FALSE 258 NA 258
#> 7 categorical count 6 BloodS… 2 TRUE 45 NA 45
#> 8 categorical percent 6 BloodS… 1 FALSE 85.1 NA 85.15
#> 9 categorical percent 6 BloodS… 2 TRUE 14.9 NA 14.85
#> 10 categorical proporti… 6 BloodS… 1 FALSE 0.851 NA 0.85
#> 11 categorical proporti… 6 BloodS… 2 TRUE 0.149 NA 0.15
#Nothing treated as numeric
heart_disease %>%
descriptives(
numeric_types = NULL
)
#> # A tibble: 75 × 9
#> fun_eval fun_key col_ind col_lab val_ind val_lab val_dbl val_chr val_cbn
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <chr> <chr>
#> 1 all available 1 Age 1 NA 303 NA 303
#> 2 all available 2 Sex 1 NA 303 NA 303
#> 3 all available 3 ChestPain 1 NA 303 NA 303
#> 4 all available 4 BP 1 NA 303 NA 303
#> 5 all available 5 Cholester… 1 NA 303 NA 303
#> 6 all available 6 BloodSugar 1 NA 303 NA 303
#> 7 all available 7 MaximumHR 1 NA 303 NA 303
#> 8 all available 8 ExerciseI… 1 NA 303 NA 303
#> 9 all available 9 HeartDise… 1 NA 303 NA 303
#> 10 all class 1 Age 1 NA NA numeric numeric
#> # … with 65 more rows
#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"
)
#> # A tibble: 4 × 9
#> fun_eval fun_key col_ind col_lab val_ind val_lab val_dbl val_chr val_cbn
#> <chr> <chr> <int> <chr> <int> <chr> <dbl> <chr> <chr>
#> 1 numeric cv 1 Age 1 NA 0.166 NA 0.17
#> 2 numeric cv 4 BP 1 NA 0.134 NA 0.13
#> 3 numeric cv 5 Cholesterol 1 NA 0.210 NA 0.21
#> 4 numeric cv 7 MaximumHR 1 NA 0.153 NA 0.15