Using Purrr or Furrr to Filter, Map and Pass Character Vectors into Additional Functions

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In this article, we will explore how the popular R package purrr (or its sister package furrr) can be used to simplify and speed up data manipulation tasks. Specifically, we will focus on using purrr::map to filter datasets, pass filtered datasets into additional functions, and then use Reduce to combine the results.

Introduction

The R community has long been aware of the importance of efficient data manipulation when working with large datasets. Over the years, various packages have emerged that provide solutions for simplifying these tasks. One such package is purrr, which provides a set of functions and techniques designed to make data manipulation easier and more readable.

In this article, we will explore how purrr::map can be used to filter datasets, pass filtered datasets into additional functions, and then use Reduce to combine the results. We will also touch on some common pitfalls and best practices when working with purrr.

The Problem

Let’s start by examining the problem at hand. Suppose we have a dataset that we want to filter based on certain criteria, such as country or region. Once we have filtered the data, we need to pass it into several additional functions, each of which performs its own specific task.

Here is an example code snippet:

# Load the necessary libraries
library(gapminder)

# Create a sample dataset
data <- gapminder_unfiltered

# Filter the data by country
group_1_data <- gapminder_unfiltered %>%
  filter(country %in% c("Algeria", "Benin"))

group_2_data <- gapminder_unfiltered %>%
  filter(country == "United States")

group_3_data <- gapminder_unfiltered %>%
  filter(country %in% c("Italy", "France"))

# Define a function to perform feature selection
select_features <- function(d) {
  features <- c()
  if (sample(c(0,1), 1) == 0) {
    features <- c("pop", "gdpPercap")
  } else {
    features <- c("pop", "gdpPercap", "lifeExp")
  }
  return(list(d = d, features))
}

# Define a function to perform validation
validate_data <- function(feat_list) {
  d <- feat_list[[1]]
  ret <- mutate(d, prediction = rnorm(nrow(d)), linear_weight = runif(nrow(d)))
  return(ret)
}

# Define a function to perform application
applicate_data <- function(feat_list) {
  d <- feat_list[[1]]
  ret <- mutate(d, prediction = rnorm(nrow(d)), linear_weight = runif(nrow(d)))
  return(ret)
}

As we can see, the code snippet above is quite lengthy and repetitive. We need to filter three different datasets using a for loop and then perform several additional tasks on each dataset.

The Solution

Now let’s introduce the solution to this problem using purrr.

# Load the necessary libraries
library(dplyr)

# Create a list of datasets to be filtered
groups_data <- list(group_1_data, group_2_data, group_3_data)

# Define a function to filter and select features from each dataset in the list
select_features_list <- purrr::map(groups_data, select_features)

# Use Reduce to combine the results of filtering and feature selection into one list
val_list <- Reduce(function(x, y) {
  return(rbind(x, select(y, prediction, linear_weight)))
}, select_features_list[2:3], init = select(select_features_list[[1]], prediction, linear_weight))

# Define a function to filter and apply features from each dataset in the list
appl_list <- purrr::map(groups_data, applicate_data)

# Use Reduce to combine the results of filtering and application into one list
total_applications <- Reduce(function(x, y) {
  return(rbind(x, select(y, prediction, linear_weight)))
}, appl_list[2:3], init = select(appl_list[[1]], prediction, linear_weight))

As we can see from this code snippet above, using purrr::map significantly simplifies the data manipulation task compared to the original code. We now use a list comprehension to define two separate functions that filter and apply features, each of which performs its own specific task.

Conclusion

In conclusion, using purrr::map to combine filtering, mapping, and applying can greatly simplify and speed up data manipulation tasks when working with large datasets. By utilizing this package, we can reduce the amount of repetition in our code, improve readability, and make it easier for others to understand what we are doing.

Best Practices

Always use purrr::map or other function composition techniques whenever possible.
Use a consistent naming convention for your functions (e.g., lowercase with underscores).
Avoid using for loops; instead, use iterative functions like purrr::map.
When working with lists of datasets, always initialize the list to prevent unexpected behavior.

By following these best practices and understanding how purrr::map works, you can simplify your code, improve readability, and make it easier for others to understand what you are doing.

Last modified on 2024-10-22