How to use dplyr rename in R

06.08.2021

Intro

The rename method allows you to quickly rename columns in your data set. This is a common task when you have obscure or large names and want to rename for clarity. In this article, we will learn how to use dplyr rename in R.

If you are in a hurry

If you don’t have time to read, here is a quick code snippet for you.

library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

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

Loading the Library

We can load the dplyr package directly, but I recommend loading the tidyverse package as we will use some other features in side.

library(tidyverse)

Loading the Dataset

For this tutorial, we will use the mtcars data set the comes with tidyverse. We take a look at this data set below.

data(mtcars)

glimpse(mtcars)
## Rows: 32
## Columns: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,~
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,~
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16~
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180~
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,~
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.~
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18~
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,~
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,~
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,~
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,~

Basic dplyr Rename

To use the rename method, we pass our data set as the first argument, followed by a named parameter mapping a new name to our existing column. In this example, we rename the hp column to horsePower.

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

When working with dplyr and the tidyverse, we often use the pipe, %>% operator. With this, we can send the data set to our method to use. Here is a rewrite of the code above.

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

Renaming Multiple Columns

We can rename multiple columns at once by passing named parameters containing a map from the new name to old columns. This is more convenient than calling rename multiple times.

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

Mapping Functions with rename_with

We can use the rename_with method to apply transformations to multiple columns. In this example, we apply the toupper method to all columns.

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

We can use select helpers, https://tidyselect.r-lib.org/reference/select_helpers.html, to add extra conditions when apply transformations. In this example, we apply the toupper method to colummns that start with a “c”.

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