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