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rdplyraggregatetapplysplit-apply-combine
Brian D
Brian D

Reputation: 2719

Cut a variable differently based on another grouping variable

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.

Upvotes: 0

Views: 495

Answers (3)

Brian D
Brian D

Reputation: 2719

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)

Upvotes: 0

HNSKD
HNSKD

Reputation: 1644

In the code below, I created 2 new variables. Both were created by grouping the sex variable and filtering the different ranges of ht. 

 library(dplyr)
 df_low <- df %>% group_by(sex) %>% filter(ht<(mean(ht)-2*sd(ht)))
 df_high<- df %>% group_by(sex) %>% filter(ht>(mean(ht)+2*sd(ht)))

Upvotes: 0

989
989

Reputation: 12937

How about this using cut from base R:

sapply(c("F", "M"), function(s){
    dfF <- df[df$sex==s,] # filter out per gender
    cut(dfF$ht, breaks = c(0, mean(dfF$ht)-2*sd(dfF$ht), Inf), labels = c("low", "high"))
})
# dfF$ht heights per gender
# mean(dfF$ht)-2*sd(dfF$ht) cut point

Upvotes: 1

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