Category: R

Steven Sanderson operates over a list of operators over lists:

In this post I will talk about the use of the R functions apply(), lapply(), sapply(), tapply(), and vapply() with examples.

These functions are all designed to help users apply a function to a set of data in R, but they differ in their input and output types, as well as in the way they handle missing values and other complexities. By using the right function for your particular problem, you can make your code more efficient and easier to read.

I do prefer the purrr() syntax because it’s a little easier to remember its function names versus keeping the variants of apply() straight in your mind. Even so, there’s a lot you can do with a judicious use of apply().

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Steven Sanderson has a multi-parter for us. First up is a look at the pmap() function in R’s purrr library:

The pmap() function in R is part of the purrr library, which is a package designed to make it easier to work with functions that operate on vectors, lists, and other types of data structures.

The pmap() function is used to apply a function to a list of arguments, where each element in the list contains the arguments for a single function call. The function is applied in parallel, meaning that each call is executed concurrently, which can help speed up computations when working with large datasets.

Next up is the imap() function:

The imap() function is a powerful tool for iterating over a list or a vector while also keeping track of the index or names of the elements. This function applies a given function to each element of a list, along with the name or index of that element, and returns a new list with the results.

The imap() function takes two main arguments: x and .f. x is the list or vector to iterate over, and .f is the function to apply to each element. The .f function takes two arguments: x and i, where x is the value of the element and i is the index or name of the element.

Both of these sound a little complex and abstract at first, though as you get more familiar with them, you get to see just how powerful they are.

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Amieroh Abrahams explains some of the benefits of R:

The era of data manipulation and analysis using programming languages has arrived. But it can be tough to find the time and the right resources to fully switch over from more manual, time-consuming solutions, such as Excel. In this blog we will show a comparison between Excel and R to get you started!

When choosing between R and Excel, it is important to understand how both solutions can get you the results you need. However, one can make it an easy, reputable, convenient process, whereas the other can make it an extremely frustrating, time-consuming process prone to human errors.

I like this post as a way of showing current Excel users how R can perform a variety of tasks programmatically which they might do manually, though the it probably beats up on Excel too much. There’s a good reason why Excel is the single most important business tool out there and people who are deep into Excel can always break out DAX or M to perform operations.

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