How do I add a prefix to several variable names using dplyr?

Question

I'm trying to add a common prefix to each of the variable names in a data.frame. For example, using the mtcars data, I could add the prefix "cars." using the following code:

> data(mtcars)
> names(mtcars)
 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"  
 [9] "am"   "gear" "carb"
> names(mtcars) <- paste0("cars.", names(mtcars))
> names(mtcars)
 [1] "cars.mpg"  "cars.cyl"  "cars.disp" "cars.hp"  
 [5] "cars.drat" "cars.wt"   "cars.qsec" "cars.vs"  
 [9] "cars.am"   "cars.gear" "cars.carb"

However, I would like to do this as part of a piped operation (i.e., a series of functions strung together using %>%), using some of the dplyr syntax. It seems like some combination of rename and everything() should do the trick, but I don't know how to make it work. Does anyone have any ideas?

Answer 1

Indeed, you can use rename_ (NSE rename itself doesn’t work):

data %>% rename_(.dots = setNames(names(.), paste0('cars.', names(.))))

… but honestly, why? Just assigning names directly is shorter and more readable:

data %>% setNames(paste0('cars.', names(.)))

Answer 2

The latest solution (2020) seems to use rename_with, which is available in dplyr 1.0.0 and higher:

mtcars %>% rename_with(.fn = ~ paste0("Myprefix_", .x, "_Mypostfix")) -> mtcars.custom

Use the .cols = argument to specify a subset of variables, it defaults to everything().

Answer 3

For future readers, dplyr now can do this with the select_if, select_at, and select_all functions:

dplyr::select_all(mtcars, .funs = funs(paste0("cars.", .)))

Answer 4

Another dplyr solution:

I find it easiest with the dplyr rename_all, rename_at, rename_if which from v.1.0.4. have been superseded by rename_with...

Try this for renaming all column names:

mtcars %>% rename_all(function(x){paste0("cars.", x)}) # older dplyr versions
mtcars %>% rename_with(.cols = everything(), function(x){paste0("cars.", x)}) # v.1.0.4.

Try this for renaming "some" column names:

mtcars %>% rename_at(vars(hp:wt) ,function(x){paste0("cars.", x)}) # older dplyr versions
mtcars %>% rename_with(.cols = hp:wt, function(x){paste0("cars.", x)}) # v.1.0.4.

Answer 5

dplyr now expects lists and will throw a warning:

Warning message:
funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

you can solve this example as follows:

dplyr::select_all(mtcars, list(~ paste0("cars.", .)))
#>                     cars.mpg cars.cyl cars.disp cars.hp cars.drat cars.wt
#> Mazda RX4               21.0        6     160.0     110      3.90   2.620
#> Mazda RX4 Wag           21.0        6     160.0     110      3.90   2.875
#> Datsun 710              22.8        4     108.0      93      3.85   2.320
#> Hornet 4 Drive          21.4        6     258.0     110      3.08   3.215
#> Hornet Sportabout       18.7        8     360.0     175      3.15   3.440
#> Valiant                 18.1        6     225.0     105      2.76   3.460
#> Duster 360              14.3        8     360.0     245      3.21   3.570
#> Merc 240D               24.4        4     146.7      62      3.69   3.190
#> Merc 230                22.8        4     140.8      95      3.92   3.150
#> Merc 280                19.2        6     167.6     123      3.92   3.440
#> Merc 280C               17.8        6     167.6     123      3.92   3.440
#> Merc 450SE              16.4        8     275.8     180      3.07   4.070
#> Merc 450SL              17.3        8     275.8     180      3.07   3.730
#> Merc 450SLC             15.2        8     275.8     180      3.07   3.780
#> Cadillac Fleetwood      10.4        8     472.0     205      2.93   5.250
#> Lincoln Continental     10.4        8     460.0     215      3.00   5.424
#> Chrysler Imperial       14.7        8     440.0     230      3.23   5.345
#> Fiat 128                32.4        4      78.7      66      4.08   2.200
#> Honda Civic             30.4        4      75.7      52      4.93   1.615
#> Toyota Corolla          33.9        4      71.1      65      4.22   1.835
#> Toyota Corona           21.5        4     120.1      97      3.70   2.465
#> Dodge Challenger        15.5        8     318.0     150      2.76   3.520
#> AMC Javelin             15.2        8     304.0     150      3.15   3.435
#> Camaro Z28              13.3        8     350.0     245      3.73   3.840
#> Pontiac Firebird        19.2        8     400.0     175      3.08   3.845
#> Fiat X1-9               27.3        4      79.0      66      4.08   1.935
#> Porsche 914-2           26.0        4     120.3      91      4.43   2.140
#> Lotus Europa            30.4        4      95.1     113      3.77   1.513
#> Ford Pantera L          15.8        8     351.0     264      4.22   3.170
#> Ferrari Dino            19.7        6     145.0     175      3.62   2.770
#> Maserati Bora           15.0        8     301.0     335      3.54   3.570
#> Volvo 142E              21.4        4     121.0     109      4.11   2.780
#>                     cars.qsec cars.vs cars.am cars.gear cars.carb
#> Mazda RX4               16.46       0       1         4         4
#> Mazda RX4 Wag           17.02       0       1         4         4
#> Datsun 710              18.61       1       1         4         1
#> Hornet 4 Drive          19.44       1       0         3         1
#> Hornet Sportabout       17.02       0       0         3         2
#> Valiant                 20.22       1       0         3         1
#> Duster 360              15.84       0       0         3         4
#> Merc 240D               20.00       1       0         4         2
#> Merc 230                22.90       1       0         4         2
#> Merc 280                18.30       1       0         4         4
#> Merc 280C               18.90       1       0         4         4
#> Merc 450SE              17.40       0       0         3         3
#> Merc 450SL              17.60       0       0         3         3
#> Merc 450SLC             18.00       0       0         3         3
#> Cadillac Fleetwood      17.98       0       0         3         4
#> Lincoln Continental     17.82       0       0         3         4
#> Chrysler Imperial       17.42       0       0         3         4
#> Fiat 128                19.47       1       1         4         1
#> Honda Civic             18.52       1       1         4         2
#> Toyota Corolla          19.90       1       1         4         1
#> Toyota Corona           20.01       1       0         3         1
#> Dodge Challenger        16.87       0       0         3         2
#> AMC Javelin             17.30       0       0         3         2
#> Camaro Z28              15.41       0       0         3         4
#> Pontiac Firebird        17.05       0       0         3         2
#> Fiat X1-9               18.90       1       1         4         1
#> Porsche 914-2           16.70       0       1         5         2
#> Lotus Europa            16.90       1       1         5         2
#> Ford Pantera L          14.50       0       1         5         4
#> Ferrari Dino            15.50       0       1         5         6
#> Maserati Bora           14.60       0       1         5         8
#> Volvo 142E              18.60       1       1         4         2

^{Created on 2019-07-31 by the reprex package (v0.3.0)}