R - Skip columns in pmax command if they do not exist - r

Question

I'd like to use the pmax command to create a new column. My code Looks like this:
Master <- Master %>%
mutate(RAM = pmax(RAM1, RAM2, RAM3, RAM4, RAM5, RAM6, RAM7, RAM8, RAM9, RAM10,
RAM11, RAM12, RAM13, RAM14, RAM15, RAM16, RAM17, RAM18,
RAM19, RAM20, RAM21, RAM22, RAM23, RAM24, RAM25, RAM26,
RAM27, RAM28, RAM29, RAM30, RAM31, RAM32, RAM33, RAM34,
RAM35, RAM36, RAM37, RAM38, RAM39, RAM40, RAM41, RAM42,
RAM43, RAM44, RAM45, RAM46, RAM47, RAM48, RAM49, RAM50,
RAM51, RAM52, RAM53, RAM54, RAM55, RAM56, RAM57, RAM58,
RAM59, RAM60, RAM61, RAM62, RAM63, RAM64, RAM65, RAM66,
RAM67, RAM68, RAM69, RAM70, RAM71, RAM72, RAM73, RAM74,
RAM75, RAM76, RAM77, RAM78, RAM79, RAM80, RAM81, RAM82,
RAM83, RAM84, RAM85, RAM86, RAM87, RAM88, RAM89, RAM90,
RAM91, RAM92, na.rm =T))
In my current data base, however, only the columns RAM1 to RAM8 exist. In this case, I want R to skip all the other columns mentioned in the Statement and to only use column RAM1 to RAM8 (it is okay if R displays an error message, but I don't want the program to interrupt running the code).
Any ideas how to do so?
Thanks!

Answer 1

One way to do this would be as follows:
Set up some data to make a reproducible example
set.seed(0)
Master <- data.frame(Other=100,RAM1=1:10, RAM2=1:10, RAM3=1:10, RAM4=1:10,
RAM5=1:10, RAM6=1:10, RAM7=1:10, RAM8=rnorm(10)+5)
Master[5,5] <- NA
Select required columns of the dataframe:
Master[colnames(Master) %in% paste0("RAM",1:92)]
Use do.call to run pmax using the selected columns as arguments, and adding the argument na.rm=TRUE
Master$RAM <- do.call(pmax, c(Master[colnames(Master) %in% paste0("RAM",1:92)], na.rm=TRUE))
Sample output:
Master
# Other RAM1 RAM2 RAM3 RAM4 RAM5 RAM6 RAM7 RAM8 RAM
#1 100 1 1 1 1 1 1 1 6.262954 6.262954
#2 100 2 2 2 2 2 2 2 4.673767 4.673767
#3 100 3 3 3 3 3 3 3 6.329799 6.329799
#4 100 4 4 4 4 4 4 4 6.272429 6.272429
#5 100 5 5 5 NA 5 5 5 5.414641 5.414641
#6 100 6 6 6 6 6 6 6 3.460050 6.000000
#7 100 7 7 7 7 7 7 7 4.071433 7.000000
#8 100 8 8 8 8 8 8 8 4.705280 8.000000
#9 100 9 9 9 9 9 9 9 4.994233 9.000000
#10 100 10 10 10 10 10 10 10 7.404653 10.000000