Cut a variable differently based on another grouping variable

Question

Example: I have a dataset of heights by gender. I'd like to split the heights into low and high where the cut points are defined as the mean - 2sd within each gender.

example dataset:

set.seed(8)
df = data.frame(sex = c(rep("M",100), rep("F",100)), 
                ht = c(rnorm(100, mean=1.7, sd=.17), rnorm(100, mean=1.6, sd=.16)))

I'd like to do something in a single line of vectorized code because I'm fairly sure that is possible, however, I do not know how to write it. I imagine that there may be a way to use cut(), apply(), and/or dplyr to achieve this.

Answer 1

Just discovered the following solution using base r:

df$ht_grp <- ave(x = df$ht, df$sex, 
                 FUN = function(x) 
                       cut(x, breaks = c(0, (mean(x, na.rm=T) - 2*sd(x, na.rm=T)), Inf)))

This works because I know that 0 and Inf are reasonable bounds, but I could also use min(x), and max(x) as my upper and lower bounds. This results in a factor variable that is split into low, high, and NA.

---

My prior solution: I came up with the following two-step process which is not so bad:

df = merge(df, 
           setNames( aggregate(ht ~ sex, df, FUN = function(x) mean(x)-2*sd(x)), 
                     c("sex", "ht_cutoff")), 
           by = "sex")

df$ht_is_low = ifelse(df$ht <= df$ht_cutoff, 1, 0)