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Create a Factor Variable by Cutting an Age Variable

Source: R/cutAge.R
cutAge.Rd

Dividing the range of an age variable x into intervals is a frequent task in data analysis. The commonly used function cut() has unfavourable default values for this. cutAge() is a convenient wrapper for cutting age variables in groups of e.g. 10 years with more suitable defaults.

Usage

cutAge(
  x,
  breaks = c(seq(from = 0, to = 90, by = 10), Inf),
  right = FALSE,
  ordered_result = TRUE,
  full = TRUE,
  labels = NULL,
  ...
)

Arguments

x

continuous variable

breaks

either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut. Default is 10-year intervals from 0 to 90.

right

logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa. Default is FALSE - unlike in cut()!

ordered_result

logical: should the result be an ordered factor? Default is TRUE - unlike in cut()!

full

logical; whether to retain empty levels at the edges of the distribution

labels

labels for the levels. When set to TRUE the age ranges will be 00-09, 10-19, 20-29, etc.

...

further arguments passed to cut(), for example to change the labels

Value

a factor, or an integer vector of level codes when labels = FALSE

Values which fall outside the range of breaks are coded as NA, as are NaN and NA values.

See also

cut(), seq()

Other cut: cut.integer(), cutQ()

Examples


set.seed(1)
desc(cutAge(sample(0:100, size = 100, replace = TRUE)))
#> ────────────────────────────────────────────────────────────────────────────── 
#> cutAge(sample(0:100, size = 100, replace = TRUE)) (ordered, factor)
#> 
#>   length      n    NAs unique levels  dupes
#>      100    100      0     10     10      y
#>          100.0%   0.0%                     
#> 
#>     level  freq   perc  cumfreq  cumperc
#> 1     0-9     6   6.0%        6     6.0%
#> 2   10-19     9   9.0%       15    15.0%
#> 3   20-29    11  11.0%       26    26.0%
#> 4   30-39    15  15.0%       41    41.0%
#> 5   40-49    12  12.0%       53    53.0%
#> 6   50-59     8   8.0%       61    61.0%
#> 7   60-69     8   8.0%       69    69.0%
#> 8   70-79     8   8.0%       77    77.0%
#> 9   80-89    16  16.0%       93    93.0%
#> 10  90-..     7   7.0%      100   100.0%
#> 


# readable labels
table(cutAge(c(3, 17, 42, 67, 95), labels = TRUE))
#> 
#> 00-09 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-.. 
#>     1     1     0     0     1     0     1     0     0     1 

# drop the empty groups at both ends
table(cutAge(c(42, 47, 51), labels = TRUE, full = FALSE))
#> 
#> 40-49 50-59 
#>     2     1