In this post in the R:case4base series we will examine sorting (ordering) data in base R. We will learn to sort our data based on one or multiple columns, with ascending or descending order and as always look at alternatives to base R, namely the tidyverse’s dplyr and data.table to show how we can achieve the same results.
It is recommended to first have a look at the post on subsetting to understand the concepts underlying the sorting process in more detail.
In this post in the R:case4base series we will look at string manipulation with base R, and provide an overview of a wide range of functions for our string working needs.
We will use simple examples to learn to perform basic string operations, concatenate strings, work with substrings, switch cases, quote, find and replace within strings and more. Some interesting bonuses will also be included.
As always, some popular alternatives to base R will also be suggested and many useful references provided for further reading.
In this post we will look at yet another productivity increasing feature of the RStudio IDE - Code Snippets. Code Snippets let us easily insert and potentially execute predefined pieces of code and work not just for R code, but many other languages as well.
In this post we will cover 4 different ways to increase productivity using Code Snippets and provide 11 real-life examples of their use that you can take advantage of instantly.
In this post in the R:case4base series we will look at one of the most common operations on multiple data frames - merge, also known as JOIN in SQL terms.
We will learn how to do the 4 basic types of join - inner, left, right and full join with base R and show how to perform the same with tidyverse’s dplyr and data.table’s methods. A quick benchmark will also be included.
Inspired by a recent post on how to import a directory of csv files at once using purrr and readr by Garrick, in this post we will try achieving the same using base R with no extra packages, and with data·table, another very popular package and as an added bonus, we will play a bit with benchmarking to see which of the methods is the fastest, including the tidyverse approach in the benchmark.
RStudio version 1.1 introduced the Terminal functionality, which does not seem to be getting enough deserved attention and love even though it is very well integrated with the rest of the IDE and can be extremely useful for several daily use-cases.
In this post we will try to cover 4 very common scenarios where the Terminal can be very useful and productive, and how to get the most of it.
We all know that feeling. We have this great idea about a new project, feature, function, piece of code.
What do we want? Write that amazing new code! When do we want it? Right NOW!
The aim of this post is to try and give you at 3 good reasons to resist that urge and consider other options, be it in your business projects or your private projects.
Calling functions in R usually involves typing brackets. And since many of our actions in R involve calling a function, we will have to type a lot of brackets working with R. Often it would make our life a lot easier if we could omit the need to type brackets where convenient. We will do exactly that today.
Work in R faster with custom bracketless commands A good starting example is, well, quitting R altogether.
In this summertime post in the case4base series, we will look at useful tools in base R, which let us profile our code without any extra packages needed to be installed. We will cover simple and easy to use speed profiling, more complex profiling of performance and memory and, as always, look at alternatives to base R as well, with a special shout out to profiling integration in RStudio.
Profiling our code is a very useful tool to determine how well the code performs on different metrics.
The addin we will create in this article will let us use a keyboard shortcut to run profiling on R code selected in RStudio without blocking the session or requiring any external packages. Specifically for very simple overview use, it may be beneficial to look at the time needed for a set of expressions to compute, e.