These are convenience functions for license data, similar to factor_var
but operate on data frames (useful for piping) and produce a check summary.
df_factor_var(df, var, levels, labels, suppress_check = TRUE, ...) df_factor_sex(df, var = "sex", levels = 1:2, labels = c("Male", "Female"), ...) df_factor_res(df, var = "res", levels = c(1, 0), labels = c("Resident", "Nonresident"), ...) df_factor_R3(df, var = "R3", levels = 1:4, labels = c("Carry", "Renew", "Reactivate", "Recruit"), ...) df_factor_age(df, var = "age", levels = 1:7, labels = c("0-17", "18-24", "25-34", "35-44", "45-54", "55-64", "65+"), ...)
| df | data frame: Input data frame |
|---|---|
| var | character: Name of numeric variable to convert |
| levels | numeric: Levels for input numeric vector |
| labels | labels: Labels to use for output factor vector |
| suppress_check | logical: If TRUE, does not print a coding summary |
| ... | Other arguments passed to |
Other functions for working with category variables: factor_var,
label_categories,
recode_agecat
library(dplyr) data(history) x <- history %>% df_factor_R3(suppress_check = FALSE) %>% df_factor_res(suppress_check = FALSE) %>% df_factor_sex(suppress_check = FALSE)#> # A tibble: 5 x 3 #> new old n #> <fct> <int> <int> #> 1 Carry 1 10476 #> 2 Renew 2 21134 #> 3 Reactivate 3 8941 #> 4 Recruit 4 13534 #> 5 <NA> NA 37821 #> #> # A tibble: 2 x 3 #> new old n #> <fct> <int> <int> #> 1 Resident 1 71345 #> 2 Nonresident 0 20561 #> #> # A tibble: 3 x 3 #> new old n #> <fct> <int> <int> #> 1 Male 1 71930 #> 2 Female 2 18474 #> 3 <NA> NA 1502 #>