Replace multiple columns in a dataframe with a new column that indicates if the original columns contained any non-missing data

I have a dataframe that resembles the simplified one below. I am hoping to replace columns A:C with a new column (new_column) that provides a 1 for a row with data and an NA for a row without data.

A  B  C
NA NA NA
1  0  0
0  1  0
0  0  1

The outcome would look something like this:

new_column
NA
1
1
1

I tried using the mutate command in dplyr

library(dplyr)

df %>%
  mutate(new_column = apply(is.na(df[, c("A","B","C")]), 1, all),
         .keep = "unused",
         .before = "D" ) # where D is the next column in the data frame

Please try the below code

library(tidyverse)

data %>% mutate(new=ifelse(!is.na(rowSums(across(c(A:C)))),1,NA))


   A  B  C new
1 NA NA NA  NA
2  1  0  0   1
3  0  1  0   1
4  0  0  1   1
5  0  0  0   1

base r approach

data$new_column <- ifelse(rowSums(is.na(data)) == ncol(data), NA, 1)

You can use if_all() + is.na:

library(dplyr)

df %>%
  mutate(new_column = ifelse(if_all(A:C, is.na), NA, 1),
         .keep = "unused")

#   new_column
# 1         NA
# 2          1
# 3          1
# 4          1

Data

df <- structure(list(A = c(NA, 1, 0, 0), B = c(NA, 0, 1, 0), C = c(NA, 
0, 0, 1)), class = "data.frame", row.names = c(NA, -4L))