How to use dplyr pull in R
06.05.2021
Intro
The pull method from dplyr allows use to simplify accessing single values from data. We can see this by comparing the base r way with the dplyr way. In this article, we will learn how to use the dplyr pull function 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 %>% pull(mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4Loading 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,~The Basic dplyr Pull
To use the pull method, we can pass the data set we would like to extract from and the name of the column we would like to extract.
pull(mtcars, mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4However, it is more common to use the pipe, %>%, operator when working
with the tidyverse. To use this, we can pipe our data to the pull
method. Here is a rewrite of the example above.
mtcars %>% pull(mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4Comparing the Pull Method to Others
Let’s look at how we would accomplish the same in base R. First, we have
two options. We can use the [[]] double bracket operator or the $
operator. Both of these will extract a vector from data. We compare this
again to the pull method below.
mtcars[['mpg']]## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4mtcars$mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4mtcars %>% pull(mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4So, the main benefit here is the consistent language (the verbs, i.e. pull) you get when using tidyverse, but let’s continue on to see some other benefits of pull.
Quickly, let’s compare pull to select. If you know how to use select,
you would normally use the single bracket [] operator in R. This will
usually return a data frame instead of a vector.
mtcars['mpg']## mpg
## Mazda RX4 21.0
## Mazda RX4 Wag 21.0
## Datsun 710 22.8
## Hornet 4 Drive 21.4
## Hornet Sportabout 18.7
## Valiant 18.1
## Duster 360 14.3
## Merc 240D 24.4
## Merc 230 22.8
## Merc 280 19.2
## Merc 280C 17.8
## Merc 450SE 16.4
## Merc 450SL 17.3
## Merc 450SLC 15.2
## Cadillac Fleetwood 10.4
## Lincoln Continental 10.4
## Chrysler Imperial 14.7
## Fiat 128 32.4
## Honda Civic 30.4
## Toyota Corolla 33.9
## Toyota Corona 21.5
## Dodge Challenger 15.5
## AMC Javelin 15.2
## Camaro Z28 13.3
## Pontiac Firebird 19.2
## Fiat X1-9 27.3
## Porsche 914-2 26.0
## Lotus Europa 30.4
## Ford Pantera L 15.8
## Ferrari Dino 19.7
## Maserati Bora 15.0
## Volvo 142E 21.4mtcars %>% select(mpg)## mpg
## Mazda RX4 21.0
## Mazda RX4 Wag 21.0
## Datsun 710 22.8
## Hornet 4 Drive 21.4
## Hornet Sportabout 18.7
## Valiant 18.1
## Duster 360 14.3
## Merc 240D 24.4
## Merc 230 22.8
## Merc 280 19.2
## Merc 280C 17.8
## Merc 450SE 16.4
## Merc 450SL 17.3
## Merc 450SLC 15.2
## Cadillac Fleetwood 10.4
## Lincoln Continental 10.4
## Chrysler Imperial 14.7
## Fiat 128 32.4
## Honda Civic 30.4
## Toyota Corolla 33.9
## Toyota Corona 21.5
## Dodge Challenger 15.5
## AMC Javelin 15.2
## Camaro Z28 13.3
## Pontiac Firebird 19.2
## Fiat X1-9 27.3
## Porsche 914-2 26.0
## Lotus Europa 30.4
## Ford Pantera L 15.8
## Ferrari Dino 19.7
## Maserati Bora 15.0
## Volvo 142E 21.4Again, compare this with pull.
mtcars %>% pull(mpg)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4Accessing From the Left
With the pull method, we can also supply a numeric offset for which column we would like to access. Here are two examples of accessing the first and third columns.
mtcars %>% pull(1)## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4mtcars %>% pull(3)## [1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8
## [13] 275.8 275.8 472.0 460.0 440.0 78.7 75.7 71.1 120.1 318.0 304.0 350.0
## [25] 400.0 79.0 120.3 95.1 351.0 145.0 301.0 121.0Accessing From the Right
Similar to the above, we can access from the right. Instead of passing a positive number, we will pass a negative number. Here is an example of accessing the last and the third from the last columns.
mtcars %>% pull(-1)## [1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2mtcars %>% pull(-3)## [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1Multipe Columns
Pull does support multiple columns, but returns a different result than using select. See here for example of pull height and name from the starwars data set.
starwars %>% pull(height, name)## Luke Skywalker C-3PO R2-D2
## 172 167 96
## Darth Vader Leia Organa Owen Lars
## 202 150 178
## Beru Whitesun lars R5-D4 Biggs Darklighter
## 165 97 183
## Obi-Wan Kenobi Anakin Skywalker Wilhuff Tarkin
## 182 188 180
## Chewbacca Han Solo Greedo
## 228 180 173
## Jabba Desilijic Tiure Wedge Antilles Jek Tono Porkins
## 175 170 180
## Yoda Palpatine Boba Fett
## 66 170 183
## IG-88 Bossk Lando Calrissian
## 200 190 177
## Lobot Ackbar Mon Mothma
## 175 180 150
## Arvel Crynyd Wicket Systri Warrick Nien Nunb
## NA 88 160
## Qui-Gon Jinn Nute Gunray Finis Valorum
## 193 191 170
## Jar Jar Binks Roos Tarpals Rugor Nass
## 196 224 206
## Ric Olié Watto Sebulba
## 183 137 112
## Quarsh Panaka Shmi Skywalker Darth Maul
## 183 163 175
## Bib Fortuna Ayla Secura Dud Bolt
## 180 178 94
## Gasgano Ben Quadinaros Mace Windu
## 122 163 188
## Ki-Adi-Mundi Kit Fisto Eeth Koth
## 198 196 171
## Adi Gallia Saesee Tiin Yarael Poof
## 184 188 264
## Plo Koon Mas Amedda Gregar Typho
## 188 196 185
## Cordé Cliegg Lars Poggle the Lesser
## 157 183 183
## Luminara Unduli Barriss Offee Dormé
## 170 166 165
## Dooku Bail Prestor Organa Jango Fett
## 193 191 183
## Zam Wesell Dexter Jettster Lama Su
## 168 198 229
## Taun We Jocasta Nu Ratts Tyerell
## 213 167 79
## R4-P17 Wat Tambor San Hill
## 96 193 191
## Shaak Ti Grievous Tarfful
## 178 216 234
## Raymus Antilles Sly Moore Tion Medon
## 188 178 206
## Finn Rey Poe Dameron
## NA NA NA
## BB8 Captain Phasma Padmé Amidala
## NA NA 165