Construct age-group variable from age column

This method splits an age interval from min_val to max_val into intervals of size step. If the method finds ages greater or equal than max_val it assigns the string ">max_val". By default min_val is set to 0, however it can be assigned by convenience. If the method finds ages lower or equal than min_val it assigns the string "<min_val-1". The function warns when (max_val - min_val) is not an integer multiple of step. In that case the last interval is truncated to the upper value closest to max_val for which (closest_upper - min_val) is multiple of step.

Usage

get_age_group(data_set, col_age, max_val, min_val = 0, step)

Arguments

data_set

data.frame with at least a column containing the age information

col_age

Name of the column containing the age information

max_val

Maximum value of age interval to split

min_val

Minimum value of age interval to split

step

Step used to split the age interval

Value

Column of type factor with the same length as the number of rows in data_set, with levels corresponding to age bins between min_val and max_val. Ages above max_val are represented as >max_val.

Examples

# load data provided with the package
data(cohortdata)

# assign age groups as a column of the `data.frame`
cohortdata$age_group <- get_age_group(
  data_set = cohortdata,
  col_age = "age",
  max_val = 80,
  step = 10
)

# view the `data.frame` with new column
head(cohortdata)
#>         id sex age death_date death_other_causes vaccine_date_1 vaccine_date_2
#> 1 04edf85a   M  50       <NA>               <NA>           <NA>           <NA>
#> 2 c5a83f56   M  66       <NA>               <NA>           <NA>           <NA>
#> 3 82991731   M  81       <NA>               <NA>           <NA>           <NA>
#> 4 afbab268   M  74       <NA>               <NA>     2021-03-30     2021-05-16
#> 5 3faf2474   M  54       <NA>               <NA>     2021-06-01     2021-06-22
#> 6 97df7bdc   M  79       <NA>               <NA>     2021-03-21     2021-05-02
#>   vaccine_1 vaccine_2 age_group
#> 1      <NA>      <NA>     50-59
#> 2      <NA>      <NA>     60-69
#> 3      <NA>      <NA>       >80
#> 4    BRAND2    BRAND2     70-79
#> 5    BRAND1    BRAND1     50-59
#> 6    BRAND2    BRAND2     70-79