Consider this sample data
# A tibble: 10 x 3
x y z
<int> <dbl> <dbl>
1 1 1 5
2 2 3 6
3 3 4 7
4 4 7 8
5 5 NA 9
6 6 12 10
7 7 NA NA
8 8 2 20
9 9 5.5 25
10 10 NA 8
I would like to mutate a new column value that rowSums if there is no NAs present in any of the columns.
If there are, take the highest value in the row times 1.2.
BUT, if there are only one column with a value, take that value
Finally, another column NA_column with the names of columns containing NA in that row!
What I have in mind, but could not figure out the rest.
df %>%
mutate(
value = case_when(any_vars(is.na()) ~ BIGGEST VALUE * 1.2,
TRUE ~ rowsum()),
NA_column = columns that has NA in that row
)
DATA
df <- tibble(
x = 1:10,
y = c(1, 3, 4, 7, NA, 12, NA, 2, 5.5, NA),
z = c(5:10, NA, 20, 25, 8)
)
Use rowwise and c_across to perform operations
library(dplyr)
df_out <- df %>%
rowwise() %>%
mutate(
value = ifelse(anyNA(c_across(everything())), max(c_across(everything()), na.rm = T) * 1.2, x), # or 'y' or 'z' instead of x
NA_column = paste(colnames(df)[is.na(c_across(x:z))], collapse = " | ")
) %>%
ungroup()
df_out
# A tibble: 10 × 5
x y z value NA_column
<int> <dbl> <dbl> <dbl> <chr>
1 1 1 5 1 ""
2 2 3 6 2 ""
3 3 4 7 3 ""
4 4 7 8 4 ""
5 5 NA 9 10.8 "y"
6 6 12 10 6 ""
7 7 NA NA 8.4 "y | z"
8 8 2 20 8 ""
9 9 5.5 25 9 ""
10 10 NA 8 12 "y"