for loop combined with if statement in R-CodePudding

I am working in R and I want to iterate over every unique/distinct name in this table and if A=="yes" | B=="yes" it should create another column C==TRUE for all entries with the same Name, else C==FALSE. I dont know how to combine a for loop with this if statement, I am always getting error messages, although it should be a simple task to do...

Name	A	B
Jordan	yes	no
Pascal	yes	no
Nando	no	yes
Nando	no	no
Nico	no	no
Nico	no	no

This should be the result:

Name	A	B	C
Jordan	yes	no	TRUE
Pascal	yes	no	TRUE
Nando	no	yes	TRUE
Nando	no	no	TRUE
Nico	no	no	FALSE
Nico	no	no	FALSE

CodePudding user response：

For-loops are often not needed in R.

library(dplyr)

dat |>
        group_by(Name) |>
        mutate(C = if_else("yes" %in% c(A, B), TRUE, FALSE))

#> # A tibble: 6 x 4
#> # Groups:   Name [4]
#>   Name   A     B     C    
#>   <chr>  <chr> <chr> <lgl>
#> 1 Jordan yes   no    TRUE 
#> 2 Pascal yes   no    TRUE 
#> 3 Nando  no    yes   TRUE 
#> 4 Nando  no    no    TRUE 
#> 5 Nico   no    no    FALSE
#> 6 Nico   no    no    FALSE

^{Created on 2022-07-05 by the reprex package (v2.0.1)}

CodePudding user response：

R is a language that prefers vector calculations over loops, so the more common way in R would be

df <- data.frame(
  Name = c("Jordan","Pascal","Nando","Nando","Nico","Nico"),
  A = c("yes","yes","no","no","no","no"),
  B = c("no","no","yes","no","no","no")
)
df$C <- df$Name %in% df$Name[df$A == "yes" | df$B == "yes"]

This solution does not rely on any additional library.

If you feel strongly about looping, you could loop over unique(df$Name) or you could aggregate by df$Name, but all of those are much more involved and inefficient techniques.