17May 2019 by Andrew Treadway
Four ways to reverse a string in R

Four ways to reverse a string in R

R
R offers several ways to reverse a string, include some base R options. We go through a few of those in this post. We'll also compare the computational time for each method. Reversing a string can be especially useful in bioinformatics (e.g. finding the reverse compliment of a DNA strand). To get started, let's generate a random string of 10 million DNA bases (we can do this with the stringi package as well, but for our purposes here, let's just use base R functions). 1) Base R with strsplit and paste One way to reverse a string is to use strsplit with paste. This is the slowest method that will be shown, but it does get the job done without needing any packages. In this example, we use strsplit to…
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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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