Replace multiple columns to missing based on value of another column

Question

I would like an efficient way to replace values of many columns to NAif they are bigger than the value of a different column.

For example: replace all values in columns height1:height5 with NA if these values are >height6

set.seed(1)

# example data
N <- 200
data <- data.frame(id = 1:N,
               height1 = rnorm(N,50:60),
               height2 = rnorm(N,60:70),
               height3 = rnorm(N,70:80),
               height4 = rnorm(N,80:90),
               height5 = rnorm(N,90:100),
               height6 = rnorm(N,60:110))

Your question is unclear. If Row 1 looks like [1,55,65,75,85,95,90] Then height5 is greater than height6, so do you want to replace all values in height5 with NA, or just the value on that row? And do you want to replace all values in the row with NA (like height1, height2, ..., height5) with NA? Please be more specific by giving an example, if needed. — Corey Levinson
– Corey Levinson, Commented Dec 11, 2018 at 15:55
I would like to replace just the value for the row for the specific column that are greater than height6. I.e for row 1 the value of height5 would be NA — aelhak
– aelhak, Commented Dec 11, 2018 at 15:58

hdkrgr · Accepted Answer · 2018-12-11 16:03:31Z

4

Using dplyr you can do

data %>% mutate_at(
   vars(height1:height5),
   ~ifelse(.x > data$height6, NA, .x)
   )

edited Dec 11, 2018 at 16:03

answered Dec 11, 2018 at 15:57

hdkrgr

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