How to perform a loop with sym() in r?

Question

I have a data frame with several continuous variables. I need to generate the logarithm of each continuous variable. The names of each new variables must be made up of the original variable name adding _ln. I would like to generate a loop to do this task. The code to do it for a single variable was:

dat <- data.frame(fev1 = c(3.4, 5.8, 3.1, NA),
                  fvc = c(3.9, 6.2, 4.5, 6.0),
                  rat = c(0.8, 0.91, 0.9, NA),
                  sex = c(0, 1, 0, 1))

var <- sym('fev1')

dat <- 
  dat %>% 
  mutate('{{vari}}_ln':= log({{vari}}))

names(dat)

[1] "fev1"    "fvc"     "rat"     "sex"     "fev1_ln"

I used the following code to make a loop:

vars <- c('fev1', 'fvc', 'rat')

purrr::map(dat[vars], ~.x %>% 
             vs <- sym(~.x) %>% 
           mutate('{{vs}}_ln':= log({{vs}})))

However, it did not work. The error message was:

Error in sym(): ! Can't convert a object to a symbol.

What I want to get is a data frame with the original variables plus the new variables.

dat

fev1 fvc  rat sex  fev1_ln   fvc_ln      rat_ln
1  3.4 3.9 0.80   0 1.223775 1.360977 -0.22314355
2  5.8 6.2 0.91   1 1.757858 1.824549 -0.09431068
3  3.1 4.5 0.90   0 1.131402 1.504077 -0.10536052
4   NA 6.0   NA   1       NA 1.791759          NA

How could I make a loop for this task?

Thanks

Answer 1

dat %>%
  mutate(across(all_of(vars), log, .names = "{col}_ln"), .keep = 'none')

   fev1_ln   fvc_ln      rat_ln
1 1.223775 1.360977 -0.22314355
2 1.757858 1.824549 -0.09431068
3 1.131402 1.504077 -0.10536052
4       NA 1.791759          NA

---

 dat %>%
   mutate(across(all_of(vars), log, .names = "{col}_ln"), .keep = 'unused')

  sex  fev1_ln   fvc_ln      rat_ln
1   0 1.223775 1.360977 -0.22314355
2   1 1.757858 1.824549 -0.09431068
3   0 1.131402 1.504077 -0.10536052
4   1       NA 1.791759          NA

---

dat %>%
  mutate(across(all_of(vars), log, .names = "{col}_ln"), .keep = 'used')

  fev1 fvc  rat  fev1_ln   fvc_ln      rat_ln
1  3.4 3.9 0.80 1.223775 1.360977 -0.22314355
2  5.8 6.2 0.91 1.757858 1.824549 -0.09431068
3  3.1 4.5 0.90 1.131402 1.504077 -0.10536052
4   NA 6.0   NA       NA 1.791759          NA

---

dat %>%
 mutate(across(all_of(vars), log, .names = "{col}_ln"))

  fev1 fvc  rat sex  fev1_ln   fvc_ln      rat_ln
1  3.4 3.9 0.80   0 1.223775 1.360977 -0.22314355
2  5.8 6.2 0.91   1 1.757858 1.824549 -0.09431068
3  3.1 4.5 0.90   0 1.131402 1.504077 -0.10536052
4   NA 6.0   NA   1       NA 1.791759          NA

Answer 2

I'm guessing what you're looking for is this:

library(tidyverse)

setNames(purrr::map_df(dat[vars], log), paste0(vars, "_ln"))

Output:

# A tibble: 4 × 3
  fev1_ln fvc_ln  rat_ln
    <dbl>  <dbl>   <dbl>
1    1.22   1.36 -0.223 
2    1.76   1.82 -0.0943
3    1.13   1.50 -0.105 
4   NA      1.79 NA     

A good first step when using a map function is to figure out what it is mapping over. In the case of a dataframe, it's mapping over the columns. Each .x or .y is a vector or list, and you don't call mutate on either of those.

I would give more specific advice, but it's hard to tell what is going on in your code sorry!!