Drop unused factor levels and collapse rare levels into "Other"

Removes unused factor levels from a data frame and renames any level whose frequency is at or below a threshold to "Other", then drops all unused levels.

Usage

drop_levels(df, factor_index = NULL, minimum_frequency = 5)

Arguments

df: A data frame containing one or more factor columns.
factor_index: Integer vector or NULL. Column indices of the factors to process. If NULL, all columns identified by is.factor() are processed. Default is NULL.
minimum_frequency: Integer. Levels with a frequency less than or equal to this value are collapsed into "Other". Default is 5.

Value

A data frame with the same structure as df, with rare factor levels renamed to "Other" and all unused levels dropped.

Examples

factor1 <- factor(c(rep("A", 10), rep("B", 10)), levels = c("A", "B", "C", "D"))
factor2 <- factor(c(rep("A", 10), rep("B", 10)), levels = c("A", "B", "C", "D"))
numeric1 <- c(1:20)
df <- data.frame(numeric1, factor1, factor2)
df$factor1
#>  [1] A A A A A A A A A A B B B B B B B B B B
#> Levels: A B C D
drop_levels(df = df, minimum_frequency = 9)
#>    numeric1 factor1 factor2
#> 1         1       A       A
#> 2         2       A       A
#> 3         3       A       A
#> 4         4       A       A
#> 5         5       A       A
#> 6         6       A       A
#> 7         7       A       A
#> 8         8       A       A
#> 9         9       A       A
#> 10       10       A       A
#> 11       11       B       B
#> 12       12       B       B
#> 13       13       B       B
#> 14       14       B       B
#> 15       15       B       B
#> 16       16       B       B
#> 17       17       B       B
#> 18       18       B       B
#> 19       19       B       B
#> 20       20       B       B
drop_levels(df = df, minimum_frequency = 10)
#>    numeric1 factor1 factor2
#> 1         1   Other   Other
#> 2         2   Other   Other
#> 3         3   Other   Other
#> 4         4   Other   Other
#> 5         5   Other   Other
#> 6         6   Other   Other
#> 7         7   Other   Other
#> 8         8   Other   Other
#> 9         9   Other   Other
#> 10       10   Other   Other
#> 11       11   Other   Other
#> 12       12   Other   Other
#> 13       13   Other   Other
#> 14       14   Other   Other
#> 15       15   Other   Other
#> 16       16   Other   Other
#> 17       17   Other   Other
#> 18       18   Other   Other
#> 19       19   Other   Other
#> 20       20   Other   Other