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Editorial Team
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Asked: 2026-05-26T14:42:41+00:00

Apologies is this is something a more seasoned R user would know, but I

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Apologies is this is something a more seasoned R user would know, but I just came across this and wanted to ask about proper usage.

It appears to be possible to classify ranges for variables by using as.factor. So, I could group observations into a range. For example, if I were looking at visits by user, it looks that I could write an if/then statement to bin the users by the range of visits they had, then get summary statistics based on the group.

Here is the link where I learned about this: http://programming-r-pro-bro.blogspot.com/2011/10/modelling-with-r-part-2.html

Now, while this function looks easier than grouping data by using plyr and ddply, it does not look to be powerful enough to break the variable into X number of bins (for example 10 for a decile) – You would have to do that yourself.

This leads to my question – Is one better than the other for grouping data, or are there just many ways to tackle grouping like this?

Thanks

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1 Answer

  1. Editorial Team

    I think cut is a better tool for this.

    With some sample data:

    set.seed(123)
age <- round(runif(10,20,50))

    This is what I’d do:

    > cut(age, c(0,30,40,Inf))
 [1] (0,30]   (40,Inf] (30,40]  (40,Inf] (40,Inf] (0,30]   (30,40]  (40,Inf]
 [9] (30,40]  (30,40] 
Levels: (0,30] (30,40] (40,Inf]

    Optionally, setting the factor labels manually:

    > cut(age, c(0,30,40,Inf), labels=c('0-30', '31-40', '40+'))
 [1] 0-30  40+   31-40 40+   40+   0-30  31-40 40+   31-40 31-40
Levels: 0-30 31-40 40+

    To contrast, the linked page suggests this:

    > as.factor(ifelse(age<=30, '0-30', ifelse(age <= 40, '30-40', '40+')))
 [1] 0-30  40+   30-40 40+   40+   0-30  30-40 40+   30-40 30-40
Levels: 0-30 30-40 40+
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