It is Christmas time! And what better time than this to write about the great tools that are available to all who like R and would like to publish their R work or even blog about it. This post is meant as a praise to the tools that are helping me to write this blog and make it a very nice experience, allowing me to focus on the content.
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.
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 part of the primer we discuss creating and using custom .jar archives within our R scripts and packages, handling of Java exceptions from R and a quick look at performance comparison between the low and high-level interfaces provided by rJava. In the first part we talked about using the rJava package to create objects, call methods and work with arrays, we examined the various ways to call Java methods and calling Java code from R directly via execution of shell commands.
This primer shall consist of two parts and its goal is to provide a walk-through of using resources developed in Java from R. It is structured as more of a “note-to-future-self” rather than a proper educational article, I however hope that some readers may still find it useful. It will also list a set of references that I found very helpful, for which I thank the respective authors.