Get column name based on row values matching in DF in R (more than 3 column names)

Question

I have a table like below, would like to crate suggestions based on row value in R studio.

This is what I have :

id	A	B	C	D	E	F	G
A	0.98	0.48	0.21	0.97	0.47	0.20	0.19
B	0.22	0.31	0.41	0.11	0.42	0.32	0.23
C	0.70	0.81	0.61	0.21	0.82	0.71	0.62

I would like to have names of the column for top1 top2.. top7 .

id	A	B	C	D	E	F	G	top1name	top2name	top3name	top4name	top5name	top6name	top7name
A	0.98	0.48	0.21	0.97	0.47	0.20	0.19	A	D	B	E	C	F	G
B	0.22	0.31	0.41	0.11	0.42	0.32	0.23	E	C	F	B	G	A	D
C	0.70	0.81	0.61	0.21	0.82	0.71	0.62	E	B	F	A	G	C	D

Answer 1

There's a better solution that can be solved by using across - someone will post that answer soon.

library(tidyverse)

df <- tibble::tribble(
  ~id,   ~A,   ~B,   ~C,   ~D,   ~E,   ~F,   ~G,
  "A", 0.98, 0.48, 0.21, 0.97, 0.47,  0.2, 0.19,
  "B", 0.22, 0.31, 0.41, 0.11, 0.42, 0.32, 0.23,
  "C",  0.7, 0.81, 0.61, 0.21, 0.82, 0.71, 0.62
  )

df %>% left_join (df %>% 
  pivot_longer(cols = A:G,
               names_to = "letters",
               values_to = "values") %>% 
  group_by(id) %>% 
  mutate(rank = dense_rank(desc(values))) %>% 
  arrange(id, rank) %>% 
  mutate(rank = glue::glue("top{rank}name") %>% as.character()) %>% 
  select(id, rank, letters) %>% 
  pivot_wider(names_from = "rank", values_from = "letters")
)
#> Joining, by = "id"
#> # A tibble: 3 x 15
#>   id        A     B     C     D     E     F     G top1name top2name top3name
#>   <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>    <chr>    <chr>   
#> 1 A      0.98  0.48  0.21  0.97  0.47  0.2   0.19 A        D        B       
#> 2 B      0.22  0.31  0.41  0.11  0.42  0.32  0.23 E        C        F       
#> 3 C      0.7   0.81  0.61  0.21  0.82  0.71  0.62 E        B        F       
#> # ... with 4 more variables: top4name <chr>, top5name <chr>, top6name <chr>,
#> #   top7name <chr>

^{Created on 2021-08-16 by the reprex package (v1.0.0)}

Answer 2

In base R you can try this, with df as your data.frame. Use apply to row-wise go through your data. You can sort each row and include the corresponding column names. The setNames allows you to rename these new resultant columns.

cbind(
  df,
  setNames(
    data.frame(t(apply(df[-1], 1, function(x) names(sort(x, decreasing = T))))),
    paste0("top", 1:(ncol(df) - 1), "name")
  )
)