Mutate ifelse on a vector - r

Question

Let's say I have this data frame:
set.seed(2)
df <- iris[c(1:5,51:55,101:105),]
df_long <- gather(df, key = "flower_att", value = "measurement",
Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)
df_long$setosa_sub <-sample(5,size = 60, replace = TRUE)
df_long$versicolor_sub <-sample(5,size = 60, replace = TRUE)
df_long$virginica_sub <-sample(5,size = 60, replace = TRUE)
df_long$sub_q<-0
Now I want to copy a value to sub_q variable based on Species variable and sub values.
I know how to do it one by one:
df_long2 <- df_long %>%
mutate(sub_q =ifelse(Species =="setosa", setosa_sub,sub_q)) %>%
mutate(sub_q =ifelse(Species =="versicolor", versicolor_sub,sub_q)) %>%
mutate(sub_q =ifelse(Species =="virginica", virginica_sub,sub_q))
But I can't figure out what is the right way to apply on a vector of the Species values instead.
species_vector <- c("setosa","versicolor","virginica")
I'm actually not sure if I need to make new function or just loop it somehow. Hope it's make sense...

Answer 1

I don't see anything wrong with the way you are doing it. Another way, using an apply function (sapply in this case) would work like this:
# a helper function to find the right value for the xth row
get_correct_sub <- function(x){
col_name = paste0(df_long$Species[x],'_sub')
df_long[[ col_name ]][x] }
# apply each row index to the helper function
df_long2 = df_long
df_long2$sub_q = sapply(1:nrow(df_long), get_correct_sub)
The helper function adds "_sub" to the species name, treats that as a column name, and then gets the value for that column.

Answer 2

Here is a datastep() solution. I created a vector lookup to map the Species to the desired column, then step through the data row by row and assign the value using the lookup. data is the input dataset and n. is the current row number:
library(libr)
# Create vector lookup
species_vector <- c("setosa" = "setosa_sub", "versicolor" = "versicolor_sub", "virginica" = "virginica_sub")
# Step through data row by row, and assign value using lookup
df_long2 <- df_long %>%
datastep({
sub_q <- data[n., species_vector[Species]]
})