dplyr::pull()
Function of the Week: dplyr::pull()
Saffron Evergreen
2022-03-02
Function of the Week
Submit both the .Rmd and the .html files.
Function: Pull()
In this document, I will introduce the pull() function, which is within the tidyverse package. I will show what it’s for, what it does and doesn’t do.
library(tidyverse)
library(palmerpenguins)
data(penguins)
print(head(penguins))## # A tibble: 6 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Adelie Torge~ 39.1 18.7 181 3750 male
## 2 Adelie Torge~ 39.5 17.4 186 3800 fema~
## 3 Adelie Torge~ 40.3 18 195 3250 fema~
## 4 Adelie Torge~ NA NA NA NA <NA>
## 5 Adelie Torge~ 36.7 19.3 193 3450 fema~
## 6 Adelie Torge~ 39.3 20.6 190 3650 male
## # ... with 1 more variable: year <int>What is it for?
What this function does and how to apply it within the dataset ‘penguins’.
This is a function in tidyverse that you can use to extract columns.
The value that is created from using pull() is a vector, which prints in the same length (amount of values) as is in the data frame.
Example 1: calling variable name
pull(data, var name)
pull(penguins, species)## [1] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [8] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [15] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [22] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [29] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [36] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [43] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [50] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [57] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [64] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [71] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [78] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [85] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [92] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [99] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [106] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [113] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [120] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [127] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [134] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [141] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [148] Adelie Adelie Adelie Adelie Adelie Gentoo Gentoo
## [155] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [162] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [169] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [176] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [183] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [190] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [197] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [204] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [211] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [218] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [225] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [232] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [239] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [246] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [253] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [260] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [267] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [274] Gentoo Gentoo Gentoo Chinstrap Chinstrap Chinstrap Chinstrap
## [281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [344] Chinstrap
## Levels: Adelie Chinstrap Gentoo# shows all values, shows the 3 levels of the factored variable at the very endExample 2: calling variable index, positive integer
pull(data, var +#)
pull(penguins, 2)## [1] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## [8] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## [15] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Biscoe
## [22] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [29] Biscoe Biscoe Dream Dream Dream Dream Dream
## [36] Dream Dream Dream Dream Dream Dream Dream
## [43] Dream Dream Dream Dream Dream Dream Dream
## [50] Dream Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [57] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [64] Biscoe Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen
## [71] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## [78] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## [85] Dream Dream Dream Dream Dream Dream Dream
## [92] Dream Dream Dream Dream Dream Dream Dream
## [99] Dream Dream Biscoe Biscoe Biscoe Biscoe Biscoe
## [106] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [113] Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen Torgersen
## [120] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## [127] Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Dream
## [134] Dream Dream Dream Dream Dream Dream Dream
## [141] Dream Dream Dream Dream Dream Dream Dream
## [148] Dream Dream Dream Dream Dream Biscoe Biscoe
## [155] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [162] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [169] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [176] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [183] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [190] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [197] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [204] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [211] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [218] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [225] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [232] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [239] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [246] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [253] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [260] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [267] Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## [274] Biscoe Biscoe Biscoe Dream Dream Dream Dream
## [281] Dream Dream Dream Dream Dream Dream Dream
## [288] Dream Dream Dream Dream Dream Dream Dream
## [295] Dream Dream Dream Dream Dream Dream Dream
## [302] Dream Dream Dream Dream Dream Dream Dream
## [309] Dream Dream Dream Dream Dream Dream Dream
## [316] Dream Dream Dream Dream Dream Dream Dream
## [323] Dream Dream Dream Dream Dream Dream Dream
## [330] Dream Dream Dream Dream Dream Dream Dream
## [337] Dream Dream Dream Dream Dream Dream Dream
## [344] Dream
## Levels: Biscoe Dream Torgersen# shows all values, shows the 3 levels of the factored variable at the very endExample 3: calling variable index, negative integer
pull(data, var -#)
pull(penguins, -2) #2nd column from the right side## [1] male female female <NA> female male female male <NA> <NA>
## [11] <NA> <NA> female male male female female male female male
## [21] female male female male male female male female female male
## [31] female male female male female male male female female male
## [41] female male female male female male male <NA> female male
## [51] female male female male female male female male female male
## [61] female male female male female male female male female male
## [71] female male female male female male female male female male
## [81] female male female male female male male female male female
## [91] female male female male female male female male female male
## [101] female male female male female male female male female male
## [111] female male female male female male female male female male
## [121] female male female male female male female male female male
## [131] female male female male female male female male female male
## [141] female male female male female male male female female male
## [151] female male female male female male male female female male
## [161] female male female male female male female male female male
## [171] female male male female female male female male <NA> male
## [181] female male male female female male female male female male
## [191] female male female male female male male female female male
## [201] female male female male female male female male female male
## [211] female male female male female male female male <NA> male
## [221] female male female male male female female male female male
## [231] female male female male female male female male female male
## [241] female male female male female male female male male female
## [251] female male female male female male <NA> male female male
## [261] female male female male female male female male <NA> male
## [271] female <NA> female male female male female male male female
## [281] male female female male female male female male female male
## [291] female male male female female male female male female male
## [301] female male female male female male female male female male
## [311] male female female male female male male female male female
## [321] female male female male male female female male female male
## [331] female male female male male female male female female male
## [341] female male male female
## Levels: female male# shows all values, shows the 2 levels of the factored variable at the very endExample 4: using a pipe to save a value as a vector
x1<- data %>% pull(column)
body_mass_vector <- penguins %>% pull(body_mass_g)
body_mass_vector # shows all values from the dataframe## [1] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 3300 3700 3200 3800 4400
## [16] 3700 3450 4500 3325 4200 3400 3600 3800 3950 3800 3800 3550 3200 3150 3950
## [31] 3250 3900 3300 3900 3325 4150 3950 3550 3300 4650 3150 3900 3100 4400 3000
## [46] 4600 3425 2975 3450 4150 3500 4300 3450 4050 2900 3700 3550 3800 2850 3750
## [61] 3150 4400 3600 4050 2850 3950 3350 4100 3050 4450 3600 3900 3550 4150 3700
## [76] 4250 3700 3900 3550 4000 3200 4700 3800 4200 3350 3550 3800 3500 3950 3600
## [91] 3550 4300 3400 4450 3300 4300 3700 4350 2900 4100 3725 4725 3075 4250 2925
## [106] 3550 3750 3900 3175 4775 3825 4600 3200 4275 3900 4075 2900 3775 3350 3325
## [121] 3150 3500 3450 3875 3050 4000 3275 4300 3050 4000 3325 3500 3500 4475 3425
## [136] 3900 3175 3975 3400 4250 3400 3475 3050 3725 3000 3650 4250 3475 3450 3750
## [151] 3700 4000 4500 5700 4450 5700 5400 4550 4800 5200 4400 5150 4650 5550 4650
## [166] 5850 4200 5850 4150 6300 4800 5350 5700 5000 4400 5050 5000 5100 4100 5650
## [181] 4600 5550 5250 4700 5050 6050 5150 5400 4950 5250 4350 5350 3950 5700 4300
## [196] 4750 5550 4900 4200 5400 5100 5300 4850 5300 4400 5000 4900 5050 4300 5000
## [211] 4450 5550 4200 5300 4400 5650 4700 5700 4650 5800 4700 5550 4750 5000 5100
## [226] 5200 4700 5800 4600 6000 4750 5950 4625 5450 4725 5350 4750 5600 4600 5300
## [241] 4875 5550 4950 5400 4750 5650 4850 5200 4925 4875 4625 5250 4850 5600 4975
## [256] 5500 4725 5500 4700 5500 4575 5500 5000 5950 4650 5500 4375 5850 4875 6000
## [271] 4925 NA 4850 5750 5200 5400 3500 3900 3650 3525 3725 3950 3250 3750 4150
## [286] 3700 3800 3775 3700 4050 3575 4050 3300 3700 3450 4400 3600 3400 2900 3800
## [301] 3300 4150 3400 3800 3700 4550 3200 4300 3350 4100 3600 3900 3850 4800 2700
## [316] 4500 3950 3650 3550 3500 3675 4450 3400 4300 3250 3675 3325 3950 3600 4050
## [331] 3350 3450 3250 4050 3800 3525 3950 3650 3650 4000 3400 3775 4100 3775Example 5: pull more than 1 column at a time
data %>% pull(column [dbl], column [chr])
penguins %>% pull(body_mass_g, island)## Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## 3750 3800 3250 NA 3450 3650 3625 4675
## Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## 3475 4250 3300 3700 3200 3800 4400 3700
## Torgersen Torgersen Torgersen Torgersen Biscoe Biscoe Biscoe Biscoe
## 3450 4500 3325 4200 3400 3600 3800 3950
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Dream Dream
## 3800 3800 3550 3200 3150 3950 3250 3900
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3300 3900 3325 4150 3950 3550 3300 4650
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3150 3900 3100 4400 3000 4600 3425 2975
## Dream Dream Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 3450 4150 3500 4300 3450 4050 2900 3700
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 3550 3800 2850 3750 3150 4400 3600 4050
## Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen Torgersen Torgersen
## 2850 3950 3350 4100 3050 4450 3600 3900
## Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## 3550 4150 3700 4250 3700 3900 3550 4000
## Torgersen Torgersen Torgersen Torgersen Dream Dream Dream Dream
## 3200 4700 3800 4200 3350 3550 3800 3500
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3950 3600 3550 4300 3400 4450 3300 4300
## Dream Dream Dream Dream Biscoe Biscoe Biscoe Biscoe
## 3700 4350 2900 4100 3725 4725 3075 4250
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 2925 3550 3750 3900 3175 4775 3825 4600
## Biscoe Biscoe Biscoe Biscoe Torgersen Torgersen Torgersen Torgersen
## 3200 4275 3900 4075 2900 3775 3350 3325
## Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen Torgersen
## 3150 3500 3450 3875 3050 4000 3275 4300
## Torgersen Torgersen Torgersen Torgersen Dream Dream Dream Dream
## 3050 4000 3325 3500 3500 4475 3425 3900
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3175 3975 3400 4250 3400 3475 3050 3725
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3000 3650 4250 3475 3450 3750 3700 4000
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4500 5700 4450 5700 5400 4550 4800 5200
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4400 5150 4650 5550 4650 5850 4200 5850
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4150 6300 4800 5350 5700 5000 4400 5050
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 5000 5100 4100 5650 4600 5550 5250 4700
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 5050 6050 5150 5400 4950 5250 4350 5350
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 3950 5700 4300 4750 5550 4900 4200 5400
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 5100 5300 4850 5300 4400 5000 4900 5050
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4300 5000 4450 5550 4200 5300 4400 5650
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4700 5700 4650 5800 4700 5550 4750 5000
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 5100 5200 4700 5800 4600 6000 4750 5950
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4625 5450 4725 5350 4750 5600 4600 5300
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4875 5550 4950 5400 4750 5650 4850 5200
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4925 4875 4625 5250 4850 5600 4975 5500
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4725 5500 4700 5500 4575 5500 5000 5950
## Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe Biscoe
## 4650 5500 4375 5850 4875 6000 4925 NA
## Biscoe Biscoe Biscoe Biscoe Dream Dream Dream Dream
## 4850 5750 5200 5400 3500 3900 3650 3525
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3725 3950 3250 3750 4150 3700 3800 3775
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3700 4050 3575 4050 3300 3700 3450 4400
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3600 3400 2900 3800 3300 4150 3400 3800
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3700 4550 3200 4300 3350 4100 3600 3900
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3850 4800 2700 4500 3950 3650 3550 3500
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3675 4450 3400 4300 3250 3675 3325 3950
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3600 4050 3350 3450 3250 4050 3800 3525
## Dream Dream Dream Dream Dream Dream Dream Dream
## 3950 3650 3650 4000 3400 3775 4100 3775# it has to be in the order of numeric, character and then prints matched observationsIs pull() helpful?
Is this useful for work or is it irrelevant?
Pros:
dplyr::pull() can be worked with or without quotes, however with other similar functions like purrr:pluck(), you need to use quotes around the variables you’re calling.
Easy to read and understand for non-coders or beginners.
Seeing pull(data, var) is nicer on the eyes than other variations like data$var and any other type of variation that uses different types of characters within the code.
Cons:
From digging around on the internet a bit, through forums and articles, it seems like pull() overall isn’t really that functional for more complex wrangling.
Cannot use as many types of “verbs” as select() does.
Select() offers “starts_with()”, “ends_with()”, “contains()”, and “everything()” which can be easier to create tibbles with columns that all contain similar values.
Example in penguins data:
can utilize “ends_with()” to pull columns with the same measurements
bill_length_mm, bill_depth_mm, flipper_length_mm
Comparing to select(), pull() creates a vector, while select() creates a tibble.
penguins %>% pull(species)## [1] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [8] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [15] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [22] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [29] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [36] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [43] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [50] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [57] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [64] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [71] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [78] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [85] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [92] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [99] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [106] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [113] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [120] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [127] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [134] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [141] Adelie Adelie Adelie Adelie Adelie Adelie Adelie
## [148] Adelie Adelie Adelie Adelie Adelie Gentoo Gentoo
## [155] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [162] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [169] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [176] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [183] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [190] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [197] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [204] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [211] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [218] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [225] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [232] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [239] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [246] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [253] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [260] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [267] Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo Gentoo
## [274] Gentoo Gentoo Gentoo Chinstrap Chinstrap Chinstrap Chinstrap
## [281] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [288] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [295] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [302] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [309] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [316] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [323] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [330] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [337] Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap Chinstrap
## [344] Chinstrap
## Levels: Adelie Chinstrap Gentoopenguins %>% select(species)## # A tibble: 344 x 1
## species
## <fct>
## 1 Adelie
## 2 Adelie
## 3 Adelie
## 4 Adelie
## 5 Adelie
## 6 Adelie
## 7 Adelie
## 8 Adelie
## 9 Adelie
## 10 Adelie
## # ... with 334 more rowsSelect() appears to be easier to manipulate and sort through, as well as the verbs that it offers.
Pull() doesn’t offer much but can be a simple way to skim over values or observations from specific columns.
Conclusion
Pull() is good for beginners, it’s simple, it’s readable, it’s a good way to dip your toes into data wrangling but there’s not much you can do with it.
It is often overlooked for being so bland;
This site states;
“Main data manipulation functions”
There are 8 fundamental data verbs/functions which are;
- filter(): Pick rows (observations/samples) based on their values
- distinct(): Remove duplicate rows
- arrange(): Reorder the rows
- select(): Select columns (variables) by their names
- rename(): Rename columns
- mutate() and transmutate(): Add/create new variables
- summarise(): Compute statistical summaries (e.g., computing the mean or the sum)
Pull is not included probably because the results are too uneventful and bland.