Adding multiple ranking columns in a dataframe in R

We can loop across the columns 'home' to 'work', apply the rank, while creating new column by adding prefix in .names, and probably select to keep the order

library(dplyr)
df1 <- df %>% 
   mutate(across(home:work, ~ rank(-.), .names = "rank_{.col}"))

Or may do this in a loop where it is more flexible in placing the column at a particular position by specifying either .after or .before. Note that we used compound assignment operator (%<>% from magrittr) to do the assignment in place

library(magrittr)
library(stringr)
for(nm in names(df)[4:6]) df %<>%
     mutate(!!str_c("rank_", nm) := rank(-.data[[nm]]), .after = all_of(nm))

-output

df
  id city uf home rank_home money rank_money work rank_work
1 34   LA RJ   10         1     2          6    2         6
2 33   BA TY    7         2     3          5   65         1
3 32   NY BN    4         4     5          4    4         5
4 12   SP SD    3         5     9          2    7         4
5 14   FR DE    1         6     8          3    9         2
6 17   BL DE    5         3    10          1    8         3

NOTE: If the column have ties, then the default method use is "average". So, ties.method can also be an argument in the rank where there are ties.

data

df <- structure(list(id = c(34L, 33L, 32L, 12L, 14L, 17L), city = c("LA", 
"BA", "NY", "SP", "FR", "BL"), uf = c("RJ", "TY", "BN", "SD", 
"DE", "DE"), home = c(10L, 7L, 4L, 3L, 1L, 5L), money = c(2L, 
3L, 5L, 9L, 8L, 10L), work = c(2L, 65L, 4L, 7L, 9L, 8L)), 
class = "data.frame", row.names = c(NA, 
-6L))

A base R option

cbind(
    df,
    setNames(
        lapply(-df[-(1:3)], rank),
        paste0("rank_", names(df)[-(1:3)])
    )
)

gives

  id city uf home money work rank_home rank_money rank_work
1 34   LA RJ   10     2    2         1          6         6
2 33   BA TY    7     3   65         2          5         1
3 32   NY BN    4     5    4         4          4         5
4 12   SP SD    3     9    7         5          2         4
5 14   FR DE    1     8    9         6          3         2
6 17   BL DE    5    10    8         3          1         3

One optional method in data.table

library(data.table)
new <- paste0("rank_",names(dt[,4:6]))
dt[,(new):=lapply(-.SD,frankv),.SDcols=4:6]