dplyr::between()

Function of the Week

Detect where values fall in a specified range

Author

Sophia Mann

Published

February 11, 2025

1 between( )

In this document, I will introduce the between( ) function and show what it can be used for.

library(tidyverse)

library(palmerpenguins)
data(penguins)

1.1 What is it for?

The between( ) function from the dplyr package makes it easier to find values within a specified range (inclusive of the lower and upper bounds).

1.1.1 Example 1

#1  getting info on penguin body mass 
summary(penguins$body_mass_g)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   2700    3550    4050    4202    4750    6300       2

#2  Getting values that are between the 1st and 3rd quartiles using >= , <= 

penguins_filtered <- penguins %>%
  filter(body_mass_g>= 3550  & body_mass_g <= 4750)

head(penguins_filtered)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           39.3          20.6               190        3650
4 Adelie  Torgersen           38.9          17.8               181        3625
5 Adelie  Torgersen           39.2          19.6               195        4675
6 Adelie  Torgersen           42            20.2               190        4250
# ℹ 2 more variables: sex <fct>, year <int>

# Getting values that are between the 1st and 3rd quartiles using between()

penguins_filtered_btwn<- penguins %>%
  filter(between(body_mass_g, 3550, 4750))

head(penguins_filtered_btwn)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           39.3          20.6               190        3650
4 Adelie  Torgersen           38.9          17.8               181        3625
5 Adelie  Torgersen           39.2          19.6               195        4675
6 Adelie  Torgersen           42            20.2               190        4250
# ℹ 2 more variables: sex <fct>, year <int>

1.1.2 Example 2

#1  getting info on penguin bill length  
summary(penguins$bill_length_mm)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  32.10   39.23   44.45   43.92   48.50   59.60       2

#2 creating dataset of penguins with shirt beaks 

Penguins_short_beaks <- penguins%>% 
  filter(between(bill_length_mm, 32.10, 39.23))

head(Penguins_short_beaks)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           36.7          19.3               193        3450
3 Adelie  Torgersen           38.9          17.8               181        3625
4 Adelie  Torgersen           39.2          19.6               195        4675
5 Adelie  Torgersen           34.1          18.1               193        3475
6 Adelie  Torgersen           37.8          17.1               186        3300
# ℹ 2 more variables: sex <fct>, year <int>

1.2 Is it helpful?

Discuss whether you think this function is useful for you and your work. Is it the best thing since sliced bread, or is it not really relevant to your work?

Yes, it works well with other functions such as mutate or filter, and also makes the readability of your code better. Readability of your code is important because it makes it easier for you to read and find errors, and makes it easier for others to work with your code.