Adding numbers based on a condition

Question

my_df <- tibble(
  b1 = c(2, 6, 3, 6, 4, 2, 1, 9, NA), 
  b2 = c(NA, 4, 6, 2, 6, 6, 1, 1, 7), 
  b3 = c(5, 9, 8, NA, 2, 3, 9, 5, NA), 
  b4 = c(NA, 6, NA, 10, 12, 8, 3, 6, 2),
  b5 = c(2, 12, 1, 7, 8, 5, 5, 6, NA),
  b6 = c(9, 2, 4, 6, 7, 6, 6, 7, 9),
  b7 = c(1, 3, 7, 7, 4, 2, 2, 9, 5),
  b8 = c(NA, 8, 4, 5, 1, 4, 1, 3, 6),
  b9 = c(4, 5, 7, 9, 5, 1, 1, 2, NA),
  b10 = c(14, 2, 4, 2, 1, 1, 1, 1, 5))

Hi Guys,

Hope you are all good. I have a df like this (very big one), and I want to tell R to add 10 to the values in b1 if there is 2 in either b6, 67, b8 or b9. Thanks once again in anticipation.

Answer 1

We can create a logical condition in case_when by taking the row sums of subset of columns b6:b9 to find if the row have at least 2 in any of the row then add 10 to b1 or else return the original column

library(dplyr)
my_df <- my_df %>% 
    mutate(b1 = case_when(rowSums(select(cur_data(), b6:b9) == 2, 
        na.rm = TRUE) > 0 ~ b1 + 10, TRUE ~ b1))

-output

my_df
# A tibble: 9 x 10
     b1    b2    b3    b4    b5    b6    b7    b8    b9   b10
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     2    NA     5    NA     2     9     1    NA     4    14
2    16     4     9     6    12     2     3     8     5     2
3     3     6     8    NA     1     4     7     4     7     4
4     6     2    NA    10     7     6     7     5     9     2
5     4     6     2    12     8     7     4     1     5     1
6    12     6     3     8     5     6     2     4     1     1
7    11     1     9     3     5     6     2     1     1     1
8    19     1     5     6     6     7     9     3     2     1
9    NA     7    NA     2    NA     9     5     6    NA     5

---

Or may also use if_any

my_df %>% 
    mutate(b1 = case_when(if_any(b6:b9, `%in%`, 2) ~ b1 + 10, TRUE ~ b1))

-output

# A tibble: 9 x 10
     b1    b2    b3    b4    b5    b6    b7    b8    b9   b10
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     2    NA     5    NA     2     9     1    NA     4    14
2    16     4     9     6    12     2     3     8     5     2
3     3     6     8    NA     1     4     7     4     7     4
4     6     2    NA    10     7     6     7     5     9     2
5     4     6     2    12     8     7     4     1     5     1
6    12     6     3     8     5     6     2     4     1     1
7    11     1     9     3     5     6     2     1     1     1
8    19     1     5     6     6     7     9     3     2     1
9    NA     7    NA     2    NA     9     5     6    NA     5

---

Or the same in base R

i1 <- rowSums(my_df[6:9] == 2, na.rm = TRUE) > 0
my_df$b1[i1] <- my_df$b1[i1] + 10

---

Or with Reduce/lapply and %in%

i1 <- Reduce(`|`, lapply(my_df[6:9], `%in%`, 2))
my_df$b1[i1] <- my_df$b1[i1] + 10

Answer 2

You can also use the following solution:

library(dplyr)
library(purrr)

my_df %>%
  pmap_df(~ {x <- c(...)[6:9];
  y <- c(...)[1]
  if(any(2 %in% x[!is.na(x)])) {
    y + 10
    } else {
      y
      }
  }) %>%
  bind_cols(my_df[-1])

# A tibble: 9 x 10
     b1    b2    b3    b4    b5    b6    b7    b8    b9   b10
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     2    NA     5    NA     2     9     1    NA     4    14
2    16     4     9     6    12     2     3     8     5     2
3     3     6     8    NA     1     4     7     4     7     4
4     6     2    NA    10     7     6     7     5     9     2
5     4     6     2    12     8     7     4     1     5     1
6    12     6     3     8     5     6     2     4     1     1
7    11     1     9     3     5     6     2     1     1     1
8    19     1     5     6     6     7     9     3     2     1
9    NA     7    NA     2    NA     9     5     6    NA     5

Or we can use this thanks to a great suggestion by dear @akrun:

my_df %>%
  mutate(b1 = ifelse(pmap_lgl(select(cur_data(), b6:b9), ~ 2 %in% c(...)), b1 + 10, b1))