structure(list(Primary.Warning.Vertical = c("N/A", "N/A", "N/A",
"N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A"), Primary.Warning.Horizontal = c("2",
"2", "1.1", "2", "2", "2", "1.7", "2", "2", "2"), Secondary.Sensor.Warning.Vertical = c("N/A",
"N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A"
), Secondary.Sensor.Warning.Horizontal = c("N/A", "N/A", "N/A",
"N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A")), row.names = c(NA,
10L), class = "data.frame")
throughout the larger df one of these 4 columns will have a value in it. I am trying to create a column that will coalesce the non NA values into one
I've tried:
df %>% mutate(TotalWarning = coalesce(Primary.Warning.Vertical, Primary.Warning.Horizontal, Secondary.Sensor.Warning.Vertical, Secondary.Sensor.Warning.Horizontal))
but the TotalWarning column is full of NA's.
Thanks for the help!
coalesce() only works on "real" missing values. In your data, "N/A" is character, so at first you need to convert them to NA.
library(dplyr)
df %>%
mutate(across(where(is.character), na_if, "N/A"),
TotalWarning = coalesce(Primary.Warning.Vertical,
Primary.Warning.Horizontal,
Secondary.Sensor.Warning.Vertical,
Secondary.Sensor.Warning.Horizontal))
# Primary.Warning.Vertical Primary.Warning.Horizontal Secondary.Sensor.Warning.Vertical Secondary.Sensor.Warning.Horizontal TotalWarning
# 1 <NA> 2 <NA> <NA> 2
# 2 <NA> 2 <NA> <NA> 2
# 3 <NA> 1.1 <NA> <NA> 1.1
# 4 <NA> 2 <NA> <NA> 2
# 5 <NA> 2 <NA> <NA> 2
# 6 <NA> 2 <NA> <NA> 2
# 7 <NA> 1.7 <NA> <NA> 1.7
# 8 <NA> 2 <NA> <NA> 2
# 9 <NA> 2 <NA> <NA> 2
# 10 <NA> 2 <NA> <NA> 2
Your variable names are too tedious. To simplify the code, you can also do this:
df %>%
mutate(across(where(is.character), na_if, "N/A"),
TotalWarning = do.call(coalesce, cur_data()))