Applying conditional functions on multiple columns of a dataframe, based on values of a variable

I have a dataframe with a factor variable identifying my groups (here y), and multiple numerical variables (to simplify, here I only show two x and z):

df = tribble(
  ~x,     ~y,     ~z,
  1,     "a",     5,   
  2,     "b",     6,   
  3,     "a",     7,    
  4,     "b",     8,  
  5,     "a",     9,  
  6,     "b",     10
)

I want to add new columns to my dataframe in which I apply different mathematical functions on those numerical variables (x and z), based on the values of the factor variable (y). For the example dataframe above: all observations with y == "a" are added with 1, and the ones with y == "b" are added with 2.

This is my code to do it:

df %>% mutate(x_new = case_when(grepl("a", y) ~ x + 1,
                                grepl("b", y) ~ x + 2))

### Output
# A tibble: 6 × 4
      x y         z     x_new
  <dbl> <chr> <dbl> <dbl>
1     1 a         5     2
2     2 b         6     4
3     3 a         7     4
4     4 b         8     6
5     5 a         9     6
6     6 b        10     8

My code works OK for adding one variable, but I want to apply the same functions for ALL the numerical variables, so in the example I want to apply the functions to the "z" variable as well and store the values in another new column. Since I have many numerical columns I don’t want to manually mutate them one by one with the approach above. Any advice on how to do this? (specially tidyverse solutions but any help is very appreciated)

>Solution :

Make a lookup to match a, b with 1,2. Then add to df excluding y column. Finally, suffix with "_new" and column bind back to original dataframe:

ll <- setNames(c(1, 2), c("a", "b"))

x <- df[, -2 ] + ll[ df$y ]
colnames(x) <- paste0(colnames(x), "_new")

cbind(df, x)
#   x y  z x_new z_new
# 1 2 a  6     3     7
# 2 4 b  8     6    10
# 3 4 a  8     5     9
# 4 6 b 10     8    12
# 5 6 a 10     7    11
# 6 8 b 12    10    14