conditionally mutating column values using `dplyr` - r

Question

I am using WRS2 to carry out robust pairwise comparisons. But one problem is that it removes the group level names from the output dataframes and saves it in a different object.
# setup
set.seed(123)
library(WRS2)
library(tidyverse)
# robust pairwise comparisons
x <- lincon(libido ~ dose, data = viagra, tr = 0.1)
# comparisons
x$comp
#> Group Group psihat ci.lower ci.upper p.value
#> [1,] 1 2 -1.0 -3.440879 1.44087853 0.25984505
#> [2,] 1 3 -2.8 -5.536161 -0.06383861 0.04914871
#> [3,] 2 3 -1.8 -4.536161 0.93616139 0.17288911
# vector with group level names
x$fnames
#> [1] "placebo" "low" "high"
I can convert it to a tibble:
# converting to tibble
suppressMessages(as_tibble(x$comp, .name_repair = "unique")) %>%
dplyr::rename(group1 = Group...1, group2 = Group...2)
#> # A tibble: 3 x 6
#> group1 group2 psihat ci.lower ci.upper p.value
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 -1 -3.44 1.44 0.260
#> 2 1 3 -2.8 -5.54 -0.0638 0.0491
#> 3 2 3 -1.8 -4.54 0.936 0.173
I would then like to replace the group column numeric values with actual names included in fnames (so map fnames[1] -> 1, fnames[2] -> 2, and so on).
So the final dataframe should look something like the following-
#> # A tibble: 3 x 6
#> group1 group2 psihat ci.lower ci.upper p.value
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 placebo low -1 -3.44 1.44 0.260
#> 2 placebo high -2.8 -5.54 -0.0638 0.0491
#> 3 low high -1.8 -4.54 0.936 0.173
In this case, it was easy to just copy-paste the three values, but I want to have a generalizable approach where no matter the number of levels, it works. How can I do this using dplyr?

Answer 1

Using a named vector to match with tidyverse. This matches by value and not by the sequence of index i.e. if the value in 'Group' columns are not in a sequence or character, this would still work
library(dplyr)
as_tibble(x$comp, .name_repair = 'unique') %>%
mutate(across(starts_with("Group"),
~ setNames(x$fnames, seq_along(x$fnames))[as.character(.)]))

Answer 2

Does this fullfil your needs :
names <- c("A","B","C")
df = data.frame(group=c(1,2,3))
library(dplyr)
df %>% mutate(group = names[group])
group
1 A
2 B
3 C

Answer 3

Here's an approach using the recode function, with the recoding vector built programmatically from the data:
# Setup
set.seed(123)
library(WRS2)
library(tidyverse)
x <- lincon(libido ~ dose, data = viagra, tr = 0.1)
# Create recoding vector
recode.vec = x$fnames %>% set_names(1:length(x$fnames))
# Recode columns
x.comp = x$comp %>%
as_tibble(.name_repair=make.unique) %>%
mutate(across(starts_with("Group"), ~recode(., !!!recode.vec)))
Output:
x.comp
#> # A tibble: 3 x 6
#> Group Group.1 psihat ci.lower ci.upper p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 placebo low -1 -3.44 1.44 0.260
#> 2 placebo high -2.8 -5.54 -0.0638 0.0491
#> 3 low high -1.8 -4.54 0.936 0.173

Answer 4

Try this tidyverse approach formating data to long after extracting the objects as tibbles. You can use left_join() to get your groups as you want. Here the code to get something close to what you want:
# setup
set.seed(123)
library(WRS2)
library(tidyverse)
# robust pairwise comparisons
x <- lincon(libido ~ dose, data = viagra, tr = 0.1)
#Transform to tibble
df1 <- suppressMessages(as_tibble(x$comp, .name_repair = "unique")) %>%
dplyr::rename(group1 = Group...1, group2 = Group...2)
#Extract labels
df2 <- tibble(treat=x$fnames) %>% mutate(value=1:n())
#Format to long df1
df1 <- df1 %>%
mutate(id=1:n()) %>%
pivot_longer(cols = c(group1,group2)) %>%
rename(group=name) %>% left_join(df2) %>% select(-value) %>%
pivot_wider(names_from = group,values_from=treat) %>% select(-id)
Output:
# A tibble: 3 x 6
psihat ci.lower ci.upper p.value group1 group2
<dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 -1 -3.44 1.44 0.260 placebo low
2 -2.8 -5.54 -0.0638 0.0491 placebo high
3 -1.8 -4.54 0.936 0.173 low high