Categorize Age and Create Age-Related Variables

This function provides two main functionalities:

add_age_cat(): Adds age categories to a dataframe based on specified breaks.
add_age_18_cat(): Adds age categories and a dummy variable for below and above 18 years old.

Usage

add_age_cat(
  df,
  age_col,
  breaks = c(0, 18, 60, 120),
  labels = NULL,
  int_undefined = c(-999, 999),
  char_undefined = "undefined",
  new_colname = NULL
)

add_age_18_cat(
  df,
  age_col,
  int_undefined = c(-999, 999),
  char_undefined = "undefined",
  new_colname = NULL
)

Arguments

df: A dataframe.
age_col: The column name to recategorize.
breaks: A vector of cut points.
labels: A vector of labels. If NULL, the labels will be the breaks.
int_undefined: A vector of values undefined (such as -999, 999) to replace by char_undefined.
char_undefined: A character to replace int_undefined values, often values corresponding to Don't know or Prefer not to answer.
new_colname: The name of the new column. If NULL, it adds "_cat" to the age_col (or "_18_cat for add_age_18_cat()).

Value

For add_age_cat(): A dataframe with an additional column containing the age categories.
For add_age_18_cat(): A dataframe with two additional columns: one with categories (below_18, above_18) and one with a dummy variable (0 for below 18, 1 for above 18).