Categorise continuous age into age groups

Usage

as_age_groups(
  var,
  min = 0,
  max = 120,
  by = 10,
  grouping_method = c("user_defined", "ons_1", "ons_2", "nhs_survey")
)

Arguments

var: Name of variable or vector
min: Numeric, specifying the minimum age of the first age group. Age values lower than this will be returned as missing values (NA)
max: Numeric, specifying the upper end of the last age group
by: Numeric, increment of the age categories
grouping_method: String, specifying the method to be used for grouping age into categories. Details about the different methods are available here: "user_defined", "ons_1", "ons_2", "nhs_survey" ... TODO

Examples

# Example using a vector:
set.seed(123)
age <- sample(1:100, 100, replace = TRUE)
as_age_groups(age)
#>   [1] 30-39 70-79 50-59 10-19 60-69 40-49 50-59 40-49 10-19 20-29 90-99 90-99
#>  [13] 60-69 90-99 50-59 90-99 0-9   90-99 90-99 70-79 20-29 0-9   40-49 0-9  
#>  [25] 80-89 30-39 70-79 80-89 40-49 70-79 10-19 30-39 0-9   0-9   40-49 70-79
#>  [37] 20-29 20-29 60-69 50-59 0-9   50-59 20-29 90-99 30-39 80-89 30-39 90-99
#>  [49] 60-69 70-79 70-79 60-69 10-19 80-89 90-99 90-99 20-29 30-39 20-29 70-79
#>  [61] 40-49 40-49 90-99 60-69 90-99 10-19 90-99 0-9   70-79 80-89 80-89 30-39
#>  [73] 30-39 80-89 50-59 30-39 0-9   10-19 60-69 20-29 50-59 20-29 80-89 30-39
#>  [85] 20-29 80-89 30-39 40-49 30-39 10-19 30-39 30-39 60-69 90-99 10-19 90-99
#>  [97] 90-99 70-79 60-69 20-29
#> 13 Levels: 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 ... 120+

# Example using a data frame
tibble::tibble(age = sample(1:115, 100, replace = TRUE)) %>%
  dplyr::mutate(age_groups = as_age_groups(age, min = 10, max = 100))
#> # A tibble: 100 × 2
#>      age age_groups
#>    <int> <fct>     
#>  1    79 70-79     
#>  2    85 80-89     
#>  3    37 30-39     
#>  4     8 NA        
#>  5    51 50-59     
#>  6    74 70-79     
#>  7    50 50-59     
#>  8   106 100+      
#>  9   108 100+      
#> 10    98 90-99     
#> # … with 90 more rows