Accessing List Elements in R by Value of Separate Variable

Accessing elements from a list in R can be a bit tricky when the index is not a numeric value but rather a character string. In this article, we’ll explore how to access specific elements of a list using various methods and functions available in R.

Introduction

R provides several ways to manipulate and analyze data, including working with lists. A list in R is an object that can contain any type of R object as its elements. While numeric indexing is straightforward for arrays or vectors, accessing elements from a list by character string index can be more complex. In this article, we’ll delve into how to access specific elements of a list using the map2, rowwise, and base R methods.

The Problem

We have a data frame df with two variables: state (character) and region (numeric), as well as a list acceptable_regions. We want to create a new variable far_away that assigns “0” if the region is included in the set of acceptable codes for each state, and “1” otherwise.

Our initial attempt using ifelse() resulted in an error due to recursive indexing. To avoid using loops, we need to find another way to access the list element based on the value of the variable “state”.

Solution 1: Using map2 (from purrr)

We can use the map2 function from the purrr package to loop through the state and corresponding values of region. This approach allows us to extract the list element of acceptable_region from the value of state, check whether it is %in% region, and coerce the result to binary with as.integer.

library(tidyverse)
df %>% 
  mutate(far_away = map2_int(state, region, ~ as_integer(.y %in% acceptable_regions[[.x]])))

This code will create a new column far_away in the data frame df, which contains the desired “0” or “1” values.

Solution 2: Using rowwise

Alternatively, we can use the rowwise function to access specific elements of the list. This approach allows us to check whether each region is included in the set of acceptable codes for its corresponding state without needing to loop through the data.

df %>% 
  rowwise %>% 
    mutate(far_away = as_integer(region %in% acceptable_regions[[state]]))

This code will also create a new column far_away in the data frame df, containing the desired “0” or “1” values.

Solution 3: Using Base R

We can use the Map function to achieve the same result as before. This approach involves using an anonymous function that takes two arguments (x and y) and returns the boolean value of whether y is in the list element corresponding to x.

df$far_away <- as_integer(unlist(Map(function(x, y) y %in% acceptable_regions[[x]], df$state, df$region)))

This code will create a new column far_away in the data frame df, containing the desired “0” or “1” values.

Comparison and Conclusion

All three methods provide an effective way to access list elements based on the value of a separate variable. The choice between them depends on personal preference, familiarity with R functions, and performance considerations.

• map2 (from purrr) is often preferred for its concise syntax and readability.
• rowwise provides a convenient way to perform operations on rows of a data frame without needing to loop through the data.
• Base R’s Map function offers flexibility but requires more manual effort to set up.

Regardless of the chosen method, these approaches enable efficient access to specific elements of lists in R and can simplify complex operations in data analysis and manipulation.

Last modified on 2025-03-03