I have a dataframe where missingness in indicated by “Z” (there may also be some “z” and NA entries present in the data), and values are entered as characters (“0”, “1”, etc). I need to create scores (“updrs1”, “updrs2″, updrs3”) that add up the non-missing values across columns selected by colname prefix (“NP1”, “NP2”, “NP3”).

dummy data:

dummy_df <- data.frame(
  subject_id = seq(1,6,1), 
  OTHERV1 = c(1,1,0,0,1,1),
  NP1VAR1 = c("Z","0","Z","Z","Z","Z"),
  NP1VAR2 = c("Z","0","Z","Z","Z","Z"),
  NP1VAR3 = c("Z","3","Z","Z","Z","Z"),
  NP2VAR1 = c("Z","2","Z","Z","Z","Z"), 
  NP2VAR2 = c("Z","0","Z","Z","Z","Z"),
  NP2VAR3 = c("Z","0","Z","Z","Z","Z"),
  NP3VAR1 = c("Z","4","Z","Z","Z","Z"),
  NP3VAR2 = c("Z","0","Z","Z","z","Z"),
  NP3VAR3 = c("Z","0","Z","Z","Z",NA),
  OTHERV2 = c(NA,NA,NA,NA,NA,NA)
)

desired output:

subject_id updrs1 updrs2 updrs3
1 1 Z Z Z
2 2 3 2 4
3 3 Z Z Z
4 4 Z Z Z
5 5 Z Z Z
6 6 Z Z Z

I’ve tried variations on this answer, with no luck.

I think I want to use a combination of tidyr::pivot_longer(), dplyr::group_by(), and sum().