Working With Strings In Base R

Kevin Feasel



Jozef Hajnala shows us that you don’t need stringr to do cool things with strings in R:

This post is aimed to serve as an overview of functionality provided by base R to work with strings. Note that the term “string” is used somewhat loosely and refers to character vectors and character strings. In R documentation, references to character string, refer to character vectors of length 1.

Also since this is an overview, we will not examine the details of the functions, but rather list examples with simple, intuitive explanations trading off technical precision.

As much as I like the tidyverse for its data platform professional-friendly approach to R, it is good to know the base libraries (and other alternatives) as well.  H/T R-Bloggers

Related Posts

Timing R Function Calls

Colin Gillespie shows off an R package for benchmarking: Of course, it’s more likely that you’ll want to compare more than two things. You can compare as many function calls as you want with mark(), as we’ll demonstrate in the following example. It’s probably more likely that you’ll want to compare these function calls against more […]

Read More

Exploratory Data Analysis with inspectdf

Laura Ellis continues a dive into Exploratory Data Analysis, this time using the inspectdf package: I like this package because it’s got a lot of functionality and it’s incredibly straightforward to use. In short, it allows you to understand and visualize column types, sizes, values, value imbalance & distributions as well as correlations. Better yet, […]

Read More


November 2018
« Oct Dec »