I want to use dplyr mutate across, and refer to another static column to be used for all mutate functions.
df <- data.frame(baseline = c(1,2,3), day1 = c(NA,2,2), day2 = c(2,3,4), day3= c(5,4,6))
I want to make a new column 'fc' for the change from each day over the baseline. I think I might need a combination of 'sym' and !! around baseline to make it work but haven't figured it out.
df %>% mutate(fc = mutate(across(starts_with('day')), ./baseline))
gives the error
Warning message: In format.data.frame(if (omit) x[seq_len(n0), , drop = FALSE] else x, : corrupt data frame: columns will be truncated or padded with NAs
I have some missing values in each day column so have edited the code above. How can I incorporate giving NAs in the output when there is an NA in the input, instead of failing?
Try this:
library(dplyr)
#Code
df2 <- df %>% mutate(across(day1:day3,.fns = list(fc = ~ ./baseline)))
Output:
baseline day1 day2 day3 day1_fc day2_fc day3_fc
1 1 2 2 5 2.0000000 2.000000 5
2 2 2 3 4 1.0000000 1.500000 2
3 3 2 4 6 0.6666667 1.333333 2
Or keeping the same variables:
#Code 2
df <- df %>% mutate(across(day1:day3,~ ./baseline))
Output:
baseline day1 day2 day3
1 1 2.0000000 2.000000 5
2 2 1.0000000 1.500000 2
3 3 0.6666667 1.333333 2
With the new data added you will get this:
#Code 3
df2 <- df %>% mutate(across(day1:day3,.fns = list(fc = ~ ./baseline)))
Output:
baseline day1 day2 day3 day1_fc day2_fc day3_fc
1 1 NA 2 5 NA 2.000000 5
2 2 2 3 4 1.0000000 1.500000 2
3 3 2 4 6 0.6666667 1.333333 2