R- New column based on present values from other columns

Question

I would like to create a data-frame column based on whether any of the other columns have any present values.

Example: column c was created depending on whether there are any present values in the rest of the row.

   age  bmi hyp chl  c
1    1   NA  NA  NA NA
2    2 22.7   1 187  1
3    1   NA   1 187  1
4    3   NA  NA  NA NA
5    1 20.4   1 113  1
6    3   NA  NA 184  1
7    1 22.5   1 118  1
8    1 30.1   1 187  1
9    2 22.0   1 238  1
10   2   NA  NA  NA NA
11   1   NA  NA  NA NA
12   2   NA  NA  NA NA
13   3 21.7   1 206  1
14   2 28.7   2 204  1
15   1 29.6   1  NA  1
16   1   NA  NA  NA NA
17   3 27.2   2 284  1
18   2 26.3   2 199  1
19   1 35.3   1 218  1
20   3 25.5   2  NA  1
21   1   NA  NA  NA NA
22   1 33.2   1 229  1
23   1 27.5   1 131  1
24   3 24.9   1  NA  1
25   2 27.4   1 186  1

Column c was created using the following bit of code:

df <- transform(df, c=ifelse(!(is.na(bmi)) | !(is.na(hyp)) | !(is.na(chl)),1,NA))

My question is: How can I create a function that does the above without specifying the columns. Ie if I have a dataset with 45 columns, I don't want to name all of them in the ifelse statement.

Many thanks in advance.

Answer 1

We can use rowSums on a logical matrix and then convert it to a vector of NA and 1

df$c <- NA^!rowSums(!is.na(df[-1]))
df$c
#[1] NA  1  1 NA  1  1  1  1  1 NA NA NA  1  1  1 NA  1  1  1  1 NA  1  1  1  1

Answer 2

We can also use the coalesce function from the dplyr package.

dt2 <- dt %>%
  mutate_all(funs(as.numeric(.))) %>%
  mutate(c = coalesce(.$bmi, .$hyp, .$chl)) %>%
  mutate(c = ifelse(!is.na(c), 1, c))

dt2
   age  bmi hyp chl  c
1    1   NA  NA  NA NA
2    2 22.7   1 187  1
3    1   NA   1 187  1
4    3   NA  NA  NA NA
5    1 20.4   1 113  1
6    3   NA  NA 184  1
7    1 22.5   1 118  1
8    1 30.1   1 187  1
9    2 22.0   1 238  1
10   2   NA  NA  NA NA
11   1   NA  NA  NA NA
12   2   NA  NA  NA NA
13   3 21.7   1 206  1
14   2 28.7   2 204  1
15   1 29.6   1  NA  1
16   1   NA  NA  NA NA
17   3 27.2   2 284  1
18   2 26.3   2 199  1
19   1 35.3   1 218  1
20   3 25.5   2  NA  1
21   1   NA  NA  NA NA
22   1 33.2   1 229  1
23   1 27.5   1 131  1
24   3 24.9   1  NA  1
25   2 27.4   1 186  1

DATA

dt <- read.table(text = "   age  bmi hyp chl
1    1   NA  NA  NA
                 2    2 22.7   1 187
                 3    1   NA   1 187
                 4    3   NA  NA  NA
                 5    1 20.4   1 113
                 6    3   NA  NA 184
                 7    1 22.5   1 118
                 8    1 30.1   1 187
                 9    2 22.0   1 238
                 10   2   NA  NA  NA
                 11   1   NA  NA  NA
                 12   2   NA  NA  NA
                 13   3 21.7   1 206
                 14   2 28.7   2 204
                 15   1 29.6   1  NA
                 16   1   NA  NA  NA
                 17   3 27.2   2 284
                 18   2 26.3   2 199
                 19   1 35.3   1 218
                 20   3 25.5   2  NA
                 21   1   NA  NA  NA
                 22   1 33.2   1 229
                 23   1 27.5   1 131
                 24   3 24.9   1  NA
                 25   2 27.4   1 186",
                 header = TRUE, stringsAsFactors = FALSE)