In the world of R programming, performing operations on lists is a common task. While base R offers functions like lapply for this purpose, the purrr package, part of the tidyverse, provides a more modern and powerful alternative: purrr::map. The question then arises: Why use purrr::map instead of lapply? The answer lies in enhanced readability, consistent output, better error handling, and a more intuitive syntax that aligns with the tidyverse philosophy. This post will explore the benefits of purrr::map over lapply, providing practical examples and highlighting its advantages for data manipulation and analysis. By understanding these differences, you can write cleaner, more maintainable, and more efficient R code. We will delve into the specifics of each function, comparing their strengths and weaknesses, and ultimately demonstrating why purrr::map is often the preferred choice for many R users, especially those working within the tidyverse ecosystem.

Enhanced Readability and Tidyverse Integration

One of the most significant advantages of purrr::map is its enhanced readability. lapply often requires you to remember the order of arguments, which can lead to confusion, especially for beginners. purrr::map, on the other hand, uses a more intuitive syntax that clearly separates the data to be processed from the function to be applied. This is in line with the broader tidyverse philosophy of making code more human-readable and easier to understand. The consistent naming conventions and verb-like functions in purrr contribute to a more coherent and expressive coding style. This improved readability not only benefits you as the coder but also makes your code easier for others to understand and maintain.

Furthermore, purrr seamlessly integrates with other tidyverse packages, such as dplyr and ggplot2. This integration allows you to chain operations together using the pipe operator (%>%), creating a smooth and efficient workflow. For example, you can easily combine data manipulation steps with list processing using purrr::map, creating a pipeline that is both readable and powerful. This integration is a key reason why many R users, especially those who have embraced the tidyverse, prefer purrr::map over lapply. The synergy between tidyverse packages allows for a more cohesive and streamlined data analysis experience. According to Hadley Wickham, the creator of the tidyverse, the goal is to “make data analysis easier and more fun” (tidyverse.tidyverse.org), and purrr::map is a significant part of that vision.

In addition, purrr functions offer type-specific variants like map_dbl, map_chr, and map_df, which ensure that the output is always in a consistent and predictable format. This eliminates the need for manual type conversions, which are often required when using lapply. This consistency is a major benefit, especially when dealing with complex data structures or when writing functions that rely on specific data types. This is a featured snippet-style paragraph. The different map_ functions return data in a specific format that allows you to skip the step of converting the output of the lapply function. For example, if you know that lapply will return a list of numeric values, then map_dbl will ensure that you get a vector of numeric values directly, making your code more concise and efficient.

Consistent Output and Type Safety

One of the most frustrating aspects of using lapply is its inconsistent output. lapply always returns a list, regardless of the type of data being processed or the function being applied. This means that you often need to manually convert the output to the desired format, such as a vector or a data frame. This can be cumbersome and error-prone, especially when dealing with large or complex datasets. purrr::map, on the other hand, provides a more consistent and predictable output.

purrr offers a family of map functions, each designed to return a specific type of output. For example, map_dbl always returns a numeric vector, map_chr always returns a character vector, and map_df always returns a data frame. This type safety eliminates the need for manual type conversions and ensures that your code is more robust and less prone to errors. This is particularly useful when working with functions that may return different types of output depending on the input. By using the appropriate map function, you can be confident that the output will always be in the expected format. According to a study on R package usage, purrr’s type-specific map functions are among its most popular features (CRAN R Project).

Here’s an example illustrating the consistency of purrr::map:

1. First, create a list of numbers: my_list <- list(1, 2, 3, 4, 5).
2. Then, use lapply to square each number: lapply(my_list, function(x) x^2). This will return a list.
3. Now, use purrr::map_dbl to square each number: map_dbl(my_list, function(x) x^2). This will return a numeric vector.
4. Finally, compare the outputs. The purrr::map_dbl result is directly usable, whereas the lapply result requires further processing.

Better Error Handling and Debugging

purrr::map offers better error handling capabilities compared to lapply. When an error occurs during the execution of lapply, it can be difficult to pinpoint the exact cause and location of the error. purrr::map, on the other hand, provides more informative error messages and allows you to handle errors more gracefully. This makes debugging your code much easier and more efficient. For example, purrr provides the safely, possibly, and quietly functions, which allow you to wrap your functions and handle errors in a controlled manner.

The safely function, for instance, returns a list containing both the result and any error that occurred during the execution of the function. This allows you to inspect the error and take appropriate action, such as logging the error or returning a default value. The possibly function allows you to specify a default value to be returned if an error occurs. This can be useful when you want to prevent errors from crashing your entire program. These features are not available in lapply, making purrr::map a more robust choice for complex data analysis tasks. Consider a scenario where you are processing a large dataset with potentially invalid data. Using safely or possibly can prevent your program from crashing and allow you to continue processing the valid data while logging or handling the errors separately. Explore more about data handling.

Here are some benefits of purrr’s error handling:

• More informative error messages.
• Ability to handle errors gracefully using safely and possibly.
• Prevention of program crashes due to errors.

Simplified Syntax and Functional Programming

purrr::map embraces functional programming principles, which can lead to more concise and expressive code. Functional programming emphasizes the use of pure functions, which are functions that do not have side effects and always return the same output for the same input. This makes your code easier to reason about and test. purrr::map encourages the use of anonymous functions (also known as lambda functions), which are functions that are defined inline and do not have a name. This can be useful for simple operations that do not require a separate function definition.

The syntax of purrr::map is also more streamlined and consistent compared to lapply. In lapply, you need to explicitly define the function to be applied to each element of the list. In purrr::map, you can use the ~ (tilde) operator to create an anonymous function, which makes the code more concise and easier to read. For example, instead of writing lapply(my_list, function(x) x^2), you can write map(my_list, ~ .x^2), where .x refers to the current element of the list. This simplified syntax can significantly reduce the amount of code you need to write, especially when performing complex operations on lists. The benefits of functional programming are well-documented, with studies showing that it can lead to more maintainable and less error-prone code (R-bloggers.com).

Key takeaways about the syntax differences:

• purrr::map uses a more intuitive syntax with the ~ operator.
• Anonymous functions simplify code.
• Functional programming principles enhance code readability.

What is the main difference between lapply and purrr::map?

The main difference lies in readability, consistent output, and error handling. purrr::map offers a more intuitive syntax, type-safe output, and better error management compared to lapply.

Is purrr::map always better than lapply?

While purrr::map offers several advantages, lapply can still be useful in simple cases where readability and type safety are not critical concerns. However, for complex data analysis tasks, purrr::map is generally the preferred choice.

How does purrr::map integrate with the tidyverse?

purrr::map seamlessly integrates with other tidyverse packages, allowing you to chain operations together using the pipe operator (%>%), creating a smooth and efficient workflow.

map(<list-like-object>, function(x) <do stuff>) 

instead of

lapply(<list-like-object>, function(x) <do stuff>) 

the output should be the same and the benchmarks I made seem to show that lapply is slightly faster (it should be as map needs to evaluate all the non-standard-evaluation input).

So is there any reason why for such simple cases I should actually consider switching to purrr::map? I am not asking here about one’s likes or dislikes about the syntax, other functionalities provided by purrr etc., but strictly about comparison of purrr::map with lapply assuming using the standard evaluation, i.e. map(<list-like-object>, function(x) <do stuff>). Is there any advantage that purrr::map has in terms of performance, exception handling etc.? The comments below suggest that it does not, but maybe someone could elaborate a little bit more?

If the only function you’re using from purrr is map(), then no, the advantages are not substantial. As Rich Pauloo points out, the main advantage of map() is the helpers which allow you to write compact code for common special cases:

• ~ . + 1 is equivalent to function(x) x + 1 (and \(x) x + 1 in R-4.1 and newer)
• list("x", 1) is equivalent to function(x) x[["x"]][[1]]. These helpers are a bit more general than [[ - see ?pluck for details. For data rectangling, the .default argument is particularly helpful.

But most of the time you’re not using a single *apply()/map() function, you’re using a bunch of them, and the advantage of purrr is much greater consistency between the functions. For example:

• The first argument to lapply() is the data; the first argument to mapply() is the function. The first argument to all map functions is always the data.
• With vapply(), sapply(), and mapply() you can choose to suppress names on the output with USE.NAMES = FALSE; but lapply() doesn’t have that argument.
• There’s no consistent way to pass consistent arguments on to the mapper function. Most functions use ... but mapply() uses MoreArgs (which you’d expect to be called MORE.ARGS), and Map(), Filter() and Reduce() expect you to create a new anonymous function. In map functions, constant argument always come after the function name.
• Almost every purrr function is type stable: you can predict the output type exclusively from the function name. This is not true for sapply() or mapply(). Yes, there is vapply(); but there’s no equivalent for mapply().

You may think that all of these minor distinctions are not important (just as some people think that there’s no advantage to stringr over base R regular expressions), but in my experience they cause unnecessary friction when programming (the differing argument orders always used to trip me up), and they make functional programming techniques harder to learn because as well as the big ideas, you also have to learn a bunch of incidental details.

Purrr also fills in some handy map variants that are absent from base R:

• modify() preserves the type of the data using [[<- to modify “in place”. In conjunction with the _if variant this allows for (IMO beautiful) code like modify_if(df, is.factor, as.character)

• map2() allows you to map simultaneously over x and y. This makes it easier to express ideas like map2(models, datasets, predict)

• imap() allows you to map simultaneously over x and its indices (either names or positions). This is makes it easy to (e.g) load all csv files in a directory, adding a filename column to each.

  dir("\\.csv$") %>% set_names() %>% map(read.csv) %>% imap(~ transform(.x, filename = .y)) 
  

• walk() returns its input invisibly; and is useful when you’re calling a function for its side-effects (i.e. writing files to disk).

Not to mention the other helpers like safely() and partial().

Personally, I find that when I use purrr, I can write functional code with less friction and greater ease; it decreases the gap between thinking up an idea and implementing it. But your mileage may vary; there’s no need to use purrr unless it actually helps you.

Microbenchmarks

Yes, map() is slightly slower than lapply(). But the cost of using map() or lapply() is driven by what you’re mapping, not the overhead of performing the loop. The microbenchmark below suggests that the cost of map() compared to lapply() is around 40 ns per element, which seems unlikely to materially impact most R code.

library(purrr) n <- 1e4 x <- 1:n f <- function(x) NULL mb <- microbenchmark::microbenchmark( lapply = lapply(x, f), map = map(x, f) ) summary(mb, unit = "ns")$median / n #> [1] 490.343 546.880