20Oct 2020 by Andrew Treadway
Why you should use vapply in R

Why you should use vapply in R

R
In this post we'll cover the vapply function in R. vapply is generally lesser known than the more popular sapply, lapply, and apply functions. However, it is very useful when you know what data type you're expecting to apply a function to as it helps to prevent silent errors. Because of this, it can be more advisable to use vapply rather than sapply or lapply. See more R articles by clicking here Examples Let's take the following example. Here, we have a list of numeric vectors and we want to get the max value of each vector. That's simple enough - we can just use sapply and apply the max function for each vector. [code lang="R"] test <- list(a = c(1, 3, 5), b = c(2,4,6), c = c(9,8,7)) sapply(test,…
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30Dec 2019 by Andrew Treadway
mapply and Map in R

mapply and Map in R

R
An older post on this blog talked about several alternative base apply functions. This post will talk about how to apply a function across multiple vectors or lists with Map and mapply in R. These functions are generalizations of sapply and lapply, which allow you to more easily loop over multiple vectors or lists simultaneously. Map Suppose we have two lists of vectors and we want to divide the nth vector in one list by the nth vector in the second list. Map makes this straightforward to accomplish, while keeping the code clean to read. Map returns a list by default, similar to lapply. Below, we create two sample lists of vectors. [code lang="R"] values1 <- list(a = c(1, 2, 3), b = c(4, 5, 6), c = c(7, 8,…
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13Mar 2019 by Andrew Treadway
Speed Test: Sapply vs. Vectorization

Speed Test: Sapply vs. Vectorization

R
The apply functions in R are awesome (see this post for some lesser known apply functions). However, if you can use pure vectorization, then you'll probably end up making your code run a lot faster than just depending upon functions like sapply and lapply. This is because apply functions like these still rely on looping through elements in a vector or list behind the scenes - one at a time. Vectorization, on the other hand, allows parallel operations under the hood - allowing much faster computation. This posts runs through a couple such examples involving string substitution and fuzzy matching. String substitution For example, let's create a vector that looks like this: test1, test2, test3, test4, ..., test1000000 with one million elements. With sapply, the code to create this would…
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13Nov 2018 by Andrew Treadway
Those “other” apply functions…

Those “other” apply functions…

R
So you know lapply, sapply, and apply...but...what about rapply, vapply, or eapply? These are generally a little less known as far as the apply family of functions in R go, so this post will explore how they work. rapply Let's start with rapply. This function has a couple of different purposes. One is to recursively apply a function to a list. We'll get to that in a moment. The other use of rapply is to a apply a function to only those elements in a list (or columns in a data frame) that belong to a specified class. For example, let's say we have a data frame with a mix of categorical and numeric variables, but we want to evaluate a function only on the numeric variables. Use rapply to…
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