Create a Factor Variable by Cutting an Age Variable

Dividing the range of an age variable x into intervals is a frequent task. 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.
CutGen yields the generation of a person based on the year of birth.

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

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, setting to FALSE will remove 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.
...: the dots are passed on to the underlying function cut(). Use these for e.g. change the labels.
vintage: year of birth

Details

The generations are defined as:

1946-1964 Babyboomer
1965-1979 Generation X
1980-1995 Generation Y – also known as Millennials
1996-2010 Generation Z
2011-.... Generation Alpha

Value

A factor is returned, unless labels = FALSE which results in an integer vector of level codes.

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

Author

Andri Signorell <andri@signorell.net>

Examples

Desc(CutAge(sample(100, 100)))
#> ────────────────────────────────────────────────────────────────────────────── 
#> CutAge(sample(100, 100)) (ordered, factor)
#> 
#>   length      n    NAs unique levels  dupes
#>      100    100      0     10     10      y
#>          100.0%   0.0%                     
#> 
#>        level  freq   perc  cumfreq  cumperc
#> 1     [0,10)     9   9.0%        9     9.0%
#> 2    [10,20)    10  10.0%       19    19.0%
#> 3    [20,30)    10  10.0%       29    29.0%
#> 4    [30,40)    10  10.0%       39    39.0%
#> 5    [40,50)    10  10.0%       49    49.0%
#> 6    [50,60)    10  10.0%       59    59.0%
#> 7    [60,70)    10  10.0%       69    69.0%
#> 8    [70,80)    10  10.0%       79    79.0%
#> 9    [80,90)    10  10.0%       89    89.0%
#> 10  [90,Inf)    11  11.0%      100   100.0%
#>