Create new variables based on variable names rule in R-CodePudding

I want to create new variables based on the following rules:

Var doesn't start with "sym"
Var doesn't end with "pct"

And the new var is the previous var with a "_ln" string added. This is the dataset (My real dataset has 184 vars, that's why I want a function)

library(dplyr)
library(tidyr)

df <- data.frame(kg_chicken = c(1,2,3,4,5,6),
                 kg_chicken_pct = c(0.1,0.2,0.3,0.4,0.5,0.6),
                 sym_kg_chicken = c(-0.25,-0.15,-0.05,0.05,0.15,0.25))
df
  kg_chicken kg_chicken_pct sym_kg_chicken
1          1            0.1          -0.25
2          2            0.2          -0.15
3          3            0.3          -0.05
4          4            0.4           0.05
5          5            0.5           0.15
6          6            0.6           0.25

This is what I tried:

df_final <- df %>%
  mutate_if(!starts_with("sym") & !ends_with("pct"),~ paste0(.,"_ln") = log(.))

But I get this error:

Error: unexpected '=' in:
"df_final <- df %>%
  mutate_if(!starts_with("sym") & !ends_with("pct"),~ paste0(.,"_ln") ="

This is my expected result:

df_final
  kg_chicken kg_chicken_pct sym_kg_chicken kg_chicken_ln
1          1            0.1          -0.25         0.000
2          2            0.2          -0.15         0.693
3          3            0.3          -0.05         1.098
4          4            0.4           0.05         1.386
5          5            0.5           0.15         1.609
6          6            0.6           0.25         1.791

Any help will be greatly appreciated (even if it's on rbase).

CodePudding user response：

mutate_if has been superseded by using across inside mutate, so the way to do this now would be:

df %>%
  mutate(across(!starts_with("sym") & !ends_with("pct"), .fns = log, .names = "{.col}_ln"))

#>   kg_chicken kg_chicken_pct sym_kg_chicken kg_chicken_ln
#> 1          1            0.1          -0.25     0.0000000
#> 2          2            0.2          -0.15     0.6931472
#> 3          3            0.3          -0.05     1.0986123
#> 4          4            0.4           0.05     1.3862944
#> 5          5            0.5           0.15     1.6094379
#> 6          6            0.6           0.25     1.7917595