Cut age into groups.

cutage(
  x,
  method = "distance",
  length = 5,
  maxage = 85,
  sep_zero = TRUE,
  breaks = c(seq(0, 85, 5)),
  labels = NULL,
  lang = "cn",
  label_tail = NULL,
  right = FALSE
)

Arguments

x: Vector contains the ages.
method: The method to use for age grouping. Options are "interval", "distance", or "quantile".
length: The length of intervals for age grouping when method is set to "distance".
maxage: The maximum age for age grouping.
sep_zero: A logical value indicating whether to include a separate group for age 0.
breaks: Custom breakpoints for the "interval" method.
labels: labels for the levels of the resulting category. By default, labels are constructed using "(a,b]" interval notation. If labels = FALSE, simple integer codes are returned instead of a factor.
lang: Language used for output. Options are 'cn' or 'en'. Default is 'cn.'.
label_tail: A string to be appended at the end of each label.
right: A logical value indicating whether the intervals are right-closed or right-open.

Value

Factor of age groups.

Examples

age <- sample(0:101, 200, replace = TRUE)
agegrp <- cutage(age,
  method = "distance", length = 5,
  maxage = 60, sep_zero = TRUE)
agegrp
#>   [1] 60+ 岁   1-4 岁   60+ 岁   55-59 岁 40-44 岁 30-34 岁 15-19 岁 60+ 岁  
#>   [9] 10-14 岁 30-34 岁 60+ 岁   60+ 岁   60+ 岁   55-59 岁 20-24 岁 5-9 岁  
#>  [17] 40-44 岁 60+ 岁   55-59 岁 60+ 岁   55-59 岁 60+ 岁   60+ 岁   30-34 岁
#>  [25] 40-44 岁 45-49 岁 35-39 岁 15-19 岁 60+ 岁   60+ 岁   60+ 岁   60+ 岁  
#>  [33] 60+ 岁   5-9 岁   50-54 岁 60+ 岁   35-39 岁 60+ 岁   60+ 岁   60+ 岁  
#>  [41] 10-14 岁 60+ 岁   15-19 岁 10-14 岁 60+ 岁   30-34 岁 20-24 岁 35-39 岁
#>  [49] 60+ 岁   50-54 岁 60+ 岁   25-29 岁 35-39 岁 15-19 岁 50-54 岁 60+ 岁  
#>  [57] 30-34 岁 25-29 岁 20-24 岁 30-34 岁 60+ 岁   60+ 岁   50-54 岁 30-34 岁
#>  [65] 25-29 岁 1-4 岁   15-19 岁 60+ 岁   30-34 岁 45-49 岁 60+ 岁   15-19 岁
#>  [73] 60+ 岁   20-24 岁 60+ 岁   60+ 岁   60+ 岁   10-14 岁 40-44 岁 20-24 岁
#>  [81] 50-54 岁 15-19 岁 1-4 岁   60+ 岁   60+ 岁   60+ 岁   20-24 岁 20-24 岁
#>  [89] 60+ 岁   45-49 岁 10-14 岁 25-29 岁 60+ 岁   20-24 岁 20-24 岁 60+ 岁  
#>  [97] 5-9 岁   5-9 岁   5-9 岁   1-4 岁   10-14 岁 5-9 岁   10-14 岁 30-34 岁
#> [105] 15-19 岁 60+ 岁   60+ 岁   60+ 岁   10-14 岁 5-9 岁   60+ 岁   60+ 岁  
#> [113] 60+ 岁   60+ 岁   60+ 岁   60+ 岁   25-29 岁 10-14 岁 60+ 岁   60+ 岁  
#> [121] 25-29 岁 45-49 岁 25-29 岁 60+ 岁   60+ 岁   60+ 岁   45-49 岁 60+ 岁  
#> [129] 60+ 岁   20-24 岁 60+ 岁   10-14 岁 25-29 岁 5-9 岁   60+ 岁   20-24 岁
#> [137] 40-44 岁 40-44 岁 50-54 岁 60+ 岁   60+ 岁   60+ 岁   30-34 岁 60+ 岁  
#> [145] 5-9 岁   60+ 岁   55-59 岁 25-29 岁 30-34 岁 40-44 岁 60+ 岁   50-54 岁
#> [153] 50-54 岁 40-44 岁 10-14 岁 25-29 岁 60+ 岁   60+ 岁   60+ 岁   40-44 岁
#> [161] 10-14 岁 5-9 岁   40-44 岁 55-59 岁 60+ 岁   60+ 岁   30-34 岁 15-19 岁
#> [169] 60+ 岁   25-29 岁 60+ 岁   55-59 岁 30-34 岁 15-19 岁 60+ 岁   40-44 岁
#> [177] 10-14 岁 60+ 岁   10-14 岁 60+ 岁   60+ 岁   60+ 岁   55-59 岁 60+ 岁  
#> [185] 0 岁     15-19 岁 40-44 岁 30-34 岁 60+ 岁   35-39 岁 10-14 岁 40-44 岁
#> [193] 60+ 岁   30-34 岁 20-24 岁 5-9 岁   40-44 岁 60+ 岁   60+ 岁   35-39 岁
#> 14 Levels: 0 岁 1-4 岁 5-9 岁 10-14 岁 15-19 岁 20-24 岁 25-29 岁 ... 60+ 岁