Key Features

Data Manipulation, R
Author

Arav Patil, Prajwal Patil & Abhinav Thakur

Published

February 25, 2025

library(tidyverse)

#dplyr is a part of the Tidyverse collection of packages. We can use dplyr by loading this package as well.
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data Set

mtcars
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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

Basic Tools

filter()

# Filter rows where mpg is greater than 20
mtcars %>% filter(mpg > 20)
A data.frame: 14 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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
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
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
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
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Multiple conditions
mtcars %>% filter(mpg > 20, cyl == 6)
A data.frame: 3 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
# Using %in% operator
mtcars %>% filter(cyl %in% c(4, 6)) #Basically selects cars that have only 4 or 6 cylinders in their engine
A data.frame: 18 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
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
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
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
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

select()

# Select specific columns
mtcars %>% select(mpg, cyl, hp)
A data.frame: 32 × 3
mpg cyl hp
<dbl> <dbl> <dbl>
Mazda RX4 21.0 6 110
Mazda RX4 Wag 21.0 6 110
Datsun 710 22.8 4 93
Hornet 4 Drive 21.4 6 110
Hornet Sportabout 18.7 8 175
Valiant 18.1 6 105
Duster 360 14.3 8 245
Merc 240D 24.4 4 62
Merc 230 22.8 4 95
Merc 280 19.2 6 123
Merc 280C 17.8 6 123
Merc 450SE 16.4 8 180
Merc 450SL 17.3 8 180
Merc 450SLC 15.2 8 180
Cadillac Fleetwood 10.4 8 205
Lincoln Continental 10.4 8 215
Chrysler Imperial 14.7 8 230
Fiat 128 32.4 4 66
Honda Civic 30.4 4 52
Toyota Corolla 33.9 4 65
Toyota Corona 21.5 4 97
Dodge Challenger 15.5 8 150
AMC Javelin 15.2 8 150
Camaro Z28 13.3 8 245
Pontiac Firebird 19.2 8 175
Fiat X1-9 27.3 4 66
Porsche 914-2 26.0 4 91
Lotus Europa 30.4 4 113
Ford Pantera L 15.8 8 264
Ferrari Dino 19.7 6 175
Maserati Bora 15.0 8 335
Volvo 142E 21.4 4 109
# Select columns by range
mtcars %>% select(mpg:hp)
A data.frame: 32 × 4
mpg cyl disp hp
<dbl> <dbl> <dbl> <dbl>
Mazda RX4 21.0 6 160.0 110
Mazda RX4 Wag 21.0 6 160.0 110
Datsun 710 22.8 4 108.0 93
Hornet 4 Drive 21.4 6 258.0 110
Hornet Sportabout 18.7 8 360.0 175
Valiant 18.1 6 225.0 105
Duster 360 14.3 8 360.0 245
Merc 240D 24.4 4 146.7 62
Merc 230 22.8 4 140.8 95
Merc 280 19.2 6 167.6 123
Merc 280C 17.8 6 167.6 123
Merc 450SE 16.4 8 275.8 180
Merc 450SL 17.3 8 275.8 180
Merc 450SLC 15.2 8 275.8 180
Cadillac Fleetwood 10.4 8 472.0 205
Lincoln Continental 10.4 8 460.0 215
Chrysler Imperial 14.7 8 440.0 230
Fiat 128 32.4 4 78.7 66
Honda Civic 30.4 4 75.7 52
Toyota Corolla 33.9 4 71.1 65
Toyota Corona 21.5 4 120.1 97
Dodge Challenger 15.5 8 318.0 150
AMC Javelin 15.2 8 304.0 150
Camaro Z28 13.3 8 350.0 245
Pontiac Firebird 19.2 8 400.0 175
Fiat X1-9 27.3 4 79.0 66
Porsche 914-2 26.0 4 120.3 91
Lotus Europa 30.4 4 95.1 113
Ford Pantera L 15.8 8 351.0 264
Ferrari Dino 19.7 6 145.0 175
Maserati Bora 15.0 8 301.0 335
Volvo 142E 21.4 4 121.0 109
# Exclude columns
mtcars %>% select(-cyl, -hp)
A data.frame: 32 × 9
mpg disp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 21.0 160.0 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 160.0 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 108.0 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 258.0 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 360.0 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 225.0 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 360.0 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 146.7 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 140.8 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 167.6 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 167.6 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 275.8 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 275.8 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 275.8 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 472.0 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 460.0 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 440.0 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 78.7 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 75.7 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 71.1 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 120.1 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 318.0 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 304.0 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 350.0 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 400.0 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 79.0 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 120.3 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 95.1 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 351.0 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 145.0 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 301.0 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 121.0 4.11 2.780 18.60 1 1 4 2
# Select columns starting with 'd'
mtcars %>% select(starts_with("d"))
A data.frame: 32 × 2
disp drat
<dbl> <dbl>
Mazda RX4 160.0 3.90
Mazda RX4 Wag 160.0 3.90
Datsun 710 108.0 3.85
Hornet 4 Drive 258.0 3.08
Hornet Sportabout 360.0 3.15
Valiant 225.0 2.76
Duster 360 360.0 3.21
Merc 240D 146.7 3.69
Merc 230 140.8 3.92
Merc 280 167.6 3.92
Merc 280C 167.6 3.92
Merc 450SE 275.8 3.07
Merc 450SL 275.8 3.07
Merc 450SLC 275.8 3.07
Cadillac Fleetwood 472.0 2.93
Lincoln Continental 460.0 3.00
Chrysler Imperial 440.0 3.23
Fiat 128 78.7 4.08
Honda Civic 75.7 4.93
Toyota Corolla 71.1 4.22
Toyota Corona 120.1 3.70
Dodge Challenger 318.0 2.76
AMC Javelin 304.0 3.15
Camaro Z28 350.0 3.73
Pontiac Firebird 400.0 3.08
Fiat X1-9 79.0 4.08
Porsche 914-2 120.3 4.43
Lotus Europa 95.1 3.77
Ford Pantera L 351.0 4.22
Ferrari Dino 145.0 3.62
Maserati Bora 301.0 3.54
Volvo 142E 121.0 4.11

mutate()

# Create a new column
mtcars %>% mutate(kpl = mpg * 0.425)
A data.frame: 32 × 12
mpg cyl disp hp drat wt qsec vs am gear carb kpl
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 8.9250
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 8.9250
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 9.6900
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 9.0950
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 7.9475
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7.6925
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 6.0775
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 10.3700
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 9.6900
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 8.1600
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7.5650
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 6.9700
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 7.3525
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 6.4600
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 4.4200
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 4.4200
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 6.2475
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 13.7700
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 12.9200
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 14.4075
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 9.1375
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 6.5875
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 6.4600
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 5.6525
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 8.1600
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11.6025
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11.0500
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 12.9200
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 6.7150
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 8.3725
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 6.3750
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 9.0950
# Modify existing column
mtcars %>% mutate(mpg = mpg * 2)
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 42.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 42.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 45.6 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 42.8 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 37.4 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 36.2 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 28.6 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 48.8 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 45.6 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 38.4 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 35.6 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 32.8 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 34.6 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 30.4 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 20.8 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 20.8 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 29.4 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 64.8 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 60.8 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 67.8 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 43.0 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 31.0 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 30.4 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 26.6 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 38.4 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 54.6 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 52.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 60.8 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 31.6 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 39.4 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 30.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 42.8 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Create multiple columns
mtcars %>% mutate(
  kpl = mpg * 0.425,
  hp_per_ton = hp / (wt * 0.45359237)
)
A data.frame: 32 × 13
mpg cyl disp hp drat wt qsec vs am gear carb kpl hp_per_ton
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 8.9250 92.56049
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 8.9250 84.35078
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 9.6900 88.37496
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 9.0950 75.43032
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 7.9475 112.15377
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 7.6925 66.90329
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 6.0775 151.29763
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 10.3700 42.84846
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 9.6900 66.48862
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 8.1600 78.82808
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7.5650 78.82808
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 6.9700 97.50174
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 7.3525 106.38930
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 6.4600 104.98203
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 4.4200 86.08526
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 4.4200 87.38825
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 6.2475 94.86683
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 13.7700 66.13868
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 12.9200 70.98475
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 14.4075 78.09290
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 9.1375 86.75391
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 6.5875 93.94699
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 6.4600 96.27173
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 5.6525 140.65952
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 8.1600 100.34043
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 11.6025 75.19643
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 11.0500 93.74797
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 12.9200 164.65456
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 6.7150 183.60264
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 8.3725 139.28121
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 6.3750 206.87635
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 9.0950 86.44024

arrange()

# Sort by mpg in ascending order
mtcars %>% arrange(mpg)
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 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 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
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
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
# Sort by mpg in descending order
mtcars %>% arrange(desc(mpg))
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
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
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
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
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
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
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
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
# Sort by multiple columns
mtcars %>% arrange(cyl, desc(mpg))
# It first sorts the data by the 'cyl' (cylinder) column in ascending order.
# Within each cylinder group, it then sorts by 'mpg' (miles per gallon) in descending order.
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
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
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
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
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
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

summarize()

# Calculate mean mpg
mtcars %>% summarize(avg_mpg = mean(mpg))
A data.frame: 1 × 1
avg_mpg
<dbl>
20.09062
# Multiple summary statistics
mtcars %>% summarize(
  avg_mpg = mean(mpg),
  max_hp = max(hp),
  min_wt = min(wt)
)
A data.frame: 1 × 3
avg_mpg max_hp min_wt
<dbl> <dbl> <dbl>
20.09062 335 1.513

Advanced dplyr Features

group_by() with summarize()

# Group by cylinder and calculate mean mpg
mtcars %>%
  group_by(cyl) %>%
  summarize(avg_mpg = mean(mpg))
A tibble: 3 × 2
cyl avg_mpg
<dbl> <dbl>
4 26.66364
6 19.74286
8 15.10000
# Multiple summary statistics by group
mtcars %>%
  group_by(cyl) %>%
  summarize(
    avg_mpg = mean(mpg),
    max_hp = max(hp),
    count = n()
  )
A tibble: 3 × 4
cyl avg_mpg max_hp count
<dbl> <dbl> <dbl> <int>
4 26.66364 113 11
6 19.74286 175 7
8 15.10000 335 14

Joining Operations

# Sample data frames
df1 <- data.frame(id = 1:3, value = c("a", "b", "c"))
df2 <- data.frame(id = 2:4, score = c(80, 90, 100))
# Inner join
inner_join(df1, df2, by = "id")
A data.frame: 2 × 3
id value score
<int> <chr> <dbl>
2 b 80
3 c 90
# Left join
left_join(df1, df2, by = "id")
A data.frame: 3 × 3
id value score
<int> <chr> <dbl>
1 a NA
2 b 80
3 c 90
# Full join
full_join(df1, df2, by = "id")
A data.frame: 4 × 3
id value score
<int> <chr> <dbl>
1 a NA
2 b 80
3 c 90
4 NA 100

Window Functions

# Rank cars by mpg within each cylinder group
mtcars %>%
  group_by(cyl) %>%
  mutate(mpg_rank = rank(desc(mpg)))
A grouped_df: 32 × 12
mpg cyl disp hp drat wt qsec vs am gear carb mpg_rank
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2.5
21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2.5
22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 8.5
21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1.0
18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2.0
18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 6.0
14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 11.0
24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 7.0
22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 8.5
19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 5.0
17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 7.0
16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 4.0
17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 3.0
15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 7.5
10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 13.5
10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 13.5
14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 10.0
32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2.0
30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 3.5
33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1.0
21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 10.0
15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 6.0
15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 7.5
13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 12.0
19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1.0
27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 5.0
26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 6.0
30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 3.5
15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 5.0
19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 4.0
15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 9.0
21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 11.0
# Calculate cumulative sum of hp
mtcars %>%
  arrange(hp) %>%
  mutate(cumulative_hp = cumsum(hp))
A data.frame: 32 × 12
mpg cyl disp hp drat wt qsec vs am gear carb cumulative_hp
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 52
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 114
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 179
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 245
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 311
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 402
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 495
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 590
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 687
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 792
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 901
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1011
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1121
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1231
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1344
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1467
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1590
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1740
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1890
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2065
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 2240
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 2415
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 2595
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 2775
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 2955
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 3160
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 3375
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 3605
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 3850
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 4095
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 4359
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 4694

across() Function

# Apply the same operation to multiple columns
mtcars %>%
  mutate(across(c(mpg, disp, hp), round))
A data.frame: 32 × 11
mpg cyl disp hp drat wt qsec vs am gear carb
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Mazda RX4 21 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 23 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 19 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18 6 225 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14 8 360 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24 4 147 62 3.69 3.190 20.00 1 0 4 2
Merc 230 23 4 141 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19 6 168 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 18 6 168 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16 8 276 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17 8 276 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15 8 276 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10 8 472 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10 8 460 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 15 8 440 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32 4 79 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30 4 76 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 34 4 71 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 22 4 120 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 16 8 318 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15 8 304 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13 8 350 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19 8 400 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27 4 79 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26 4 120 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30 4 95 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 16 8 351 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 20 6 145 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15 8 301 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21 4 121 109 4.11 2.780 18.60 1 1 4 2
# Summarize multiple columns
mtcars %>%
  group_by(cyl) %>%
  summarize(across(c(mpg, disp, hp), list(mean = mean, sd = sd)))
A tibble: 3 × 7
cyl mpg_mean mpg_sd disp_mean disp_sd hp_mean hp_sd
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
4 26.66364 4.509828 105.1364 26.87159 82.63636 20.93453
6 19.74286 1.453567 183.3143 41.56246 122.28571 24.26049
8 15.10000 2.560048 353.1000 67.77132 209.21429 50.97689

case_when() for Complex Conditions

mtcars %>%
  mutate(size = case_when(
    hp < 100 ~ "small",
    hp >= 100 & hp < 200 ~ "medium",
    hp >= 200 ~ "large"
  ))
#When you need to evaluate multiple complex conditions you can use mutate
A data.frame: 32 × 12
mpg cyl disp hp drat wt qsec vs am gear carb size
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 medium
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 medium
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 small
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 medium
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 medium
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 medium
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 large
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 small
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 small
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 medium
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 medium
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 medium
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 medium
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 medium
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 large
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 large
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 large
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 small
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 small
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 small
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 small
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 medium
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 medium
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 large
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 medium
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 small
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 small
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 medium
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 large
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 medium
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 large
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 medium

Handling Missing Values

# Replace specific values with NA
df <- data.frame(x = c(1, 2, -99, 4, 5))
df %>% mutate(x = na_if(x, -99))
A data.frame: 5 × 1
x
<dbl>
1
2
NA
4
5
# Remove rows with any NA
df %>% filter(!if_any(everything(), is.na))
A data.frame: 5 × 1
x
<dbl>
1
2
-99
4
5