This is similar to What is the equivalent of Stata function inlist() in R?, except about inrange().
In Stata, I can select observations of (numeric) variable var that fall within a certain [x,y] range with inrange(var, x, y). For instance:
<code>keep if inrange(var, 50, 100)
</code>
<code>keep if inrange(var, 50, 100)
</code>
keep if inrange(var, 50, 100)
What is the equivalent in R, preferably within the tidyverse?
I know that I can do the following, but is there a faster way?
<code>data %>% filter(var>=50 & var<=100)
</code>
<code>data %>% filter(var>=50 & var<=100)
</code>
data %>% filter(var>=50 & var<=100)
7
To kind of summarize the comments. between in dplyr or data.table is probably the closest thing to Stata’s inrange function. Fortunately the syntax of the function also looks really similar!
<code>library(palmerpenguins)
library(dplyr)
penguins |>
filter(between(body_mass_g, left = 2000, right = 3000)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37 16.9 185 3000
#> 2 Adelie Dream 37.5 18.9 179 2975
#> 3 Adelie Biscoe 34.5 18.1 187 2900
#> 4 Adelie Biscoe 36.5 16.6 181 2850
#> 5 Adelie Biscoe 36.4 17.1 184 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
## this can generalise to other popular ways to manipulate data in r
penguins[between(penguins$body_mass_g, left = 2000, right = 3000),] |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 <NA> <NA> NA NA NA NA
#> 2 Adelie Dream 37 16.9 185 3000
#> 3 Adelie Dream 37.5 18.9 179 2975
#> 4 Adelie Biscoe 34.5 18.1 187 2900
#> 5 Adelie Biscoe 36.5 16.6 181 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
</code>
<code>library(palmerpenguins)
library(dplyr)
penguins |>
filter(between(body_mass_g, left = 2000, right = 3000)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37 16.9 185 3000
#> 2 Adelie Dream 37.5 18.9 179 2975
#> 3 Adelie Biscoe 34.5 18.1 187 2900
#> 4 Adelie Biscoe 36.5 16.6 181 2850
#> 5 Adelie Biscoe 36.4 17.1 184 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
## this can generalise to other popular ways to manipulate data in r
penguins[between(penguins$body_mass_g, left = 2000, right = 3000),] |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 <NA> <NA> NA NA NA NA
#> 2 Adelie Dream 37 16.9 185 3000
#> 3 Adelie Dream 37.5 18.9 179 2975
#> 4 Adelie Biscoe 34.5 18.1 187 2900
#> 5 Adelie Biscoe 36.5 16.6 181 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
</code>
library(palmerpenguins)
library(dplyr)
penguins |>
filter(between(body_mass_g, left = 2000, right = 3000)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37 16.9 185 3000
#> 2 Adelie Dream 37.5 18.9 179 2975
#> 3 Adelie Biscoe 34.5 18.1 187 2900
#> 4 Adelie Biscoe 36.5 16.6 181 2850
#> 5 Adelie Biscoe 36.4 17.1 184 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
## this can generalise to other popular ways to manipulate data in r
penguins[between(penguins$body_mass_g, left = 2000, right = 3000),] |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 <NA> <NA> NA NA NA NA
#> 2 Adelie Dream 37 16.9 185 3000
#> 3 Adelie Dream 37.5 18.9 179 2975
#> 4 Adelie Biscoe 34.5 18.1 187 2900
#> 5 Adelie Biscoe 36.5 16.6 181 2850
#> # ℹ 2 more variables: sex <fct>, year <int>
The data.table version differs a little because it gives you the option to do < and > statements
<code>library(palmerpenguins)
library(dplyr)
penguins |>
filter(data.table::between(body_mass_g, lower = 2000, upper = 3000, incbounds = FALSE)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37.5 18.9 179 2975
#> 2 Adelie Biscoe 34.5 18.1 187 2900
#> 3 Adelie Biscoe 36.5 16.6 181 2850
#> 4 Adelie Biscoe 36.4 17.1 184 2850
#> 5 Adelie Dream 33.1 16.1 178 2900
#> # ℹ 2 more variables: sex <fct>, year <int>
</code>
<code>library(palmerpenguins)
library(dplyr)
penguins |>
filter(data.table::between(body_mass_g, lower = 2000, upper = 3000, incbounds = FALSE)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37.5 18.9 179 2975
#> 2 Adelie Biscoe 34.5 18.1 187 2900
#> 3 Adelie Biscoe 36.5 16.6 181 2850
#> 4 Adelie Biscoe 36.4 17.1 184 2850
#> 5 Adelie Dream 33.1 16.1 178 2900
#> # ℹ 2 more variables: sex <fct>, year <int>
</code>
library(palmerpenguins)
library(dplyr)
penguins |>
filter(data.table::between(body_mass_g, lower = 2000, upper = 3000, incbounds = FALSE)) |>
head(n = 5)
#> # A tibble: 5 × 8
#> species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
#> <fct> <fct> <dbl> <dbl> <int> <int>
#> 1 Adelie Dream 37.5 18.9 179 2975
#> 2 Adelie Biscoe 34.5 18.1 187 2900
#> 3 Adelie Biscoe 36.5 16.6 181 2850
#> 4 Adelie Biscoe 36.4 17.1 184 2850
#> 5 Adelie Dream 33.1 16.1 178 2900
#> # ℹ 2 more variables: sex <fct>, year <int>
Created on 2024-09-09 with reprex v2.1.1