How to use dplyr mutate in R
06.07.2021
Intro
The mutate method in dplyr allows you to add new variables, especially computed ones, while preserving existing columns. A common data wrangling task is to create new columns using computations on existing columns. In this article, we will learn how to use the dplyr mutate method.
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 %>% mutate(mpg_avg = mean(mpg))## 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
## mpg_avg
## Mazda RX4 20.09062
## Mazda RX4 Wag 20.09062
## Datsun 710 20.09062
## Hornet 4 Drive 20.09062
## Hornet Sportabout 20.09062
## Valiant 20.09062
## Duster 360 20.09062
## Merc 240D 20.09062
## Merc 230 20.09062
## Merc 280 20.09062
## Merc 280C 20.09062
## Merc 450SE 20.09062
## Merc 450SL 20.09062
## Merc 450SLC 20.09062
## Cadillac Fleetwood 20.09062
## Lincoln Continental 20.09062
## Chrysler Imperial 20.09062
## Fiat 128 20.09062
## Honda Civic 20.09062
## Toyota Corolla 20.09062
## Toyota Corona 20.09062
## Dodge Challenger 20.09062
## AMC Javelin 20.09062
## Camaro Z28 20.09062
## Pontiac Firebird 20.09062
## Fiat X1-9 20.09062
## Porsche 914-2 20.09062
## Lotus Europa 20.09062
## Ford Pantera L 20.09062
## Ferrari Dino 20.09062
## Maserati Bora 20.09062
## Volvo 142E 20.09062Loading 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 Mutate
The basic use of mutate is to pass our data set and a parameter with the new column we would like. For example, let’s create an avg mpg column.
mutate(mtcars, mpg_hp = mpg / hp)## 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
## mpg_hp
## Mazda RX4 0.19090909
## Mazda RX4 Wag 0.19090909
## Datsun 710 0.24516129
## Hornet 4 Drive 0.19454545
## Hornet Sportabout 0.10685714
## Valiant 0.17238095
## Duster 360 0.05836735
## Merc 240D 0.39354839
## Merc 230 0.24000000
## Merc 280 0.15609756
## Merc 280C 0.14471545
## Merc 450SE 0.09111111
## Merc 450SL 0.09611111
## Merc 450SLC 0.08444444
## Cadillac Fleetwood 0.05073171
## Lincoln Continental 0.04837209
## Chrysler Imperial 0.06391304
## Fiat 128 0.49090909
## Honda Civic 0.58461538
## Toyota Corolla 0.52153846
## Toyota Corona 0.22164948
## Dodge Challenger 0.10333333
## AMC Javelin 0.10133333
## Camaro Z28 0.05428571
## Pontiac Firebird 0.10971429
## Fiat X1-9 0.41363636
## Porsche 914-2 0.28571429
## Lotus Europa 0.26902655
## Ford Pantera L 0.05984848
## Ferrari Dino 0.11257143
## Maserati Bora 0.04477612
## Volvo 142E 0.19633028We can see at the end, we have a new column added to the end.
When using tidyverse, we often will use the pipe, %>%, operator. With this, we can pass our data using the pip instead. Let’s rewrite the example above.
mtcars %>% mutate(mpg_hp = mpg / hp)## 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
## mpg_hp
## Mazda RX4 0.19090909
## Mazda RX4 Wag 0.19090909
## Datsun 710 0.24516129
## Hornet 4 Drive 0.19454545
## Hornet Sportabout 0.10685714
## Valiant 0.17238095
## Duster 360 0.05836735
## Merc 240D 0.39354839
## Merc 230 0.24000000
## Merc 280 0.15609756
## Merc 280C 0.14471545
## Merc 450SE 0.09111111
## Merc 450SL 0.09611111
## Merc 450SLC 0.08444444
## Cadillac Fleetwood 0.05073171
## Lincoln Continental 0.04837209
## Chrysler Imperial 0.06391304
## Fiat 128 0.49090909
## Honda Civic 0.58461538
## Toyota Corolla 0.52153846
## Toyota Corona 0.22164948
## Dodge Challenger 0.10333333
## AMC Javelin 0.10133333
## Camaro Z28 0.05428571
## Pontiac Firebird 0.10971429
## Fiat X1-9 0.41363636
## Porsche 914-2 0.28571429
## Lotus Europa 0.26902655
## Ford Pantera L 0.05984848
## Ferrari Dino 0.11257143
## Maserati Bora 0.04477612
## Volvo 142E 0.19633028More Stat Example
There are many stats we can use when using the mutate function a list of them are provided in the documentation: https://dplyr.tidyverse.org/reference/mutate.html#useful-mutate-functions .
Let’s take a look at a few examples all in one section.
mtcars %>%
mutate(
mpg2 = mpg * 2,
mpg2_squared = mpg * mpg,
mpg_hp = mpg + hp,
mpg_lead = lead(mpg),
mpg_lag = lag(mpg),
mpg_rank = min_rank(mpg)
)## mpg cyl disp hp drat wt qsec vs am gear carb mpg2
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 42.0
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 42.0
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 45.6
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 42.8
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 37.4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 36.2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 28.6
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 48.8
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 45.6
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 38.4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 35.6
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 32.8
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 34.6
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 30.4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 20.8
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 20.8
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 29.4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 64.8
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 60.8
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 67.8
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 43.0
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 31.0
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 30.4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 26.6
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 38.4
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 54.6
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 52.0
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 60.8
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 31.6
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 39.4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 30.0
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 42.8
## mpg2_squared mpg_hp mpg_lead mpg_lag mpg_rank
## Mazda RX4 441.00 131.0 21.0 NA 19
## Mazda RX4 Wag 441.00 131.0 22.8 21.0 19
## Datsun 710 519.84 115.8 21.4 21.0 24
## Hornet 4 Drive 457.96 131.4 18.7 22.8 21
## Hornet Sportabout 349.69 193.7 18.1 21.4 15
## Valiant 327.61 123.1 14.3 18.7 14
## Duster 360 204.49 259.3 24.4 18.1 4
## Merc 240D 595.36 86.4 22.8 14.3 26
## Merc 230 519.84 117.8 19.2 24.4 24
## Merc 280 368.64 142.2 17.8 22.8 16
## Merc 280C 316.84 140.8 16.4 19.2 13
## Merc 450SE 268.96 196.4 17.3 17.8 11
## Merc 450SL 299.29 197.3 15.2 16.4 12
## Merc 450SLC 231.04 195.2 10.4 17.3 7
## Cadillac Fleetwood 108.16 215.4 10.4 15.2 1
## Lincoln Continental 108.16 225.4 14.7 10.4 1
## Chrysler Imperial 216.09 244.7 32.4 10.4 5
## Fiat 128 1049.76 98.4 30.4 14.7 31
## Honda Civic 924.16 82.4 33.9 32.4 29
## Toyota Corolla 1149.21 98.9 21.5 30.4 32
## Toyota Corona 462.25 118.5 15.5 33.9 23
## Dodge Challenger 240.25 165.5 15.2 21.5 9
## AMC Javelin 231.04 165.2 13.3 15.5 7
## Camaro Z28 176.89 258.3 19.2 15.2 3
## Pontiac Firebird 368.64 194.2 27.3 13.3 16
## Fiat X1-9 745.29 93.3 26.0 19.2 28
## Porsche 914-2 676.00 117.0 30.4 27.3 27
## Lotus Europa 924.16 143.4 15.8 26.0 29
## Ford Pantera L 249.64 279.8 19.7 30.4 10
## Ferrari Dino 388.09 194.7 15.0 15.8 18
## Maserati Bora 225.00 350.0 21.4 19.7 6
## Volvo 142E 457.96 130.4 NA 15.0 21Many of these variables don’t tell us much, but we can see the many options we can use during the mutate verb.
Removing or Modifying Variables
We can use the mutate function to remove a variable if we set it to NULL. Also, we can overwrite a variable by passing a parameter with the same name.
mtcars %>%
mutate(
mpg = NULL,
hp = hp * 10
)## cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 6 160.0 1100 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 6 160.0 1100 3.90 2.875 17.02 0 1 4 4
## Datsun 710 4 108.0 930 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 6 258.0 1100 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 8 360.0 1750 3.15 3.440 17.02 0 0 3 2
## Valiant 6 225.0 1050 2.76 3.460 20.22 1 0 3 1
## Duster 360 8 360.0 2450 3.21 3.570 15.84 0 0 3 4
## Merc 240D 4 146.7 620 3.69 3.190 20.00 1 0 4 2
## Merc 230 4 140.8 950 3.92 3.150 22.90 1 0 4 2
## Merc 280 6 167.6 1230 3.92 3.440 18.30 1 0 4 4
## Merc 280C 6 167.6 1230 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 8 275.8 1800 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 8 275.8 1800 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 8 275.8 1800 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 8 472.0 2050 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 8 460.0 2150 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 8 440.0 2300 3.23 5.345 17.42 0 0 3 4
## Fiat 128 4 78.7 660 4.08 2.200 19.47 1 1 4 1
## Honda Civic 4 75.7 520 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 4 71.1 650 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 4 120.1 970 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 8 318.0 1500 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 8 304.0 1500 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 8 350.0 2450 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 8 400.0 1750 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 4 79.0 660 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 4 120.3 910 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 4 95.1 1130 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 8 351.0 2640 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 6 145.0 1750 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 8 301.0 3350 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 4 121.0 1090 4.11 2.780 18.60 1 1 4 2Using Select Helpers
We can use select helpers,
https://dplyr.tidyverse.org/reference/group_cols.html?q=select%20helpers,
and apply functions to each variable with mutate. In this example, we
will apply the as.character transformation to all columns that are not
mpg.
mtcars %>%
mutate(across(!mpg, as.character))## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.9 2.62 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.9 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
## Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.2 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 19.9 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2
## AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2
## Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
## Maserati Bora 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
## Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2Window functions
When grouping data, we can make good use of https://dplyr.tidyverse.org/reference/ranking.html. You can read more about them in the docs, but here is a quick example.
mtcars %>%
select(mpg, cyl) %>%
group_by(cyl) %>%
mutate(rank = min_rank(desc(mpg)))## # A tibble: 32 x 3
## # Groups: cyl [3]
## mpg cyl rank
## <dbl> <dbl> <int>
## 1 21 6 2
## 2 21 6 2
## 3 22.8 4 8
## 4 21.4 6 1
## 5 18.7 8 2
## 6 18.1 6 6
## 7 14.3 8 11
## 8 24.4 4 7
## 9 22.8 4 8
## 10 19.2 6 5
## # ... with 22 more rows