Real-world problem often happen at the intersection of many areas. For example, maybe you want to build a web app for easier data ingestion. As an R user that’s no problem. You can easily dabble into the world of web development using the fantastic Shiny package.

But if you’re in that world, you will soon reach the boundaries of where using only R code can get you out of trouble. You will soon find that any meaningful Shiny app will have to sooner rather than later deal with problems that happen at the intersection of R and traditional webdev languages like HTML, CSS & JS. That’s where it helps to know a bit about these languages.

Similarly, you may want to incorporate a cool machine learning model into your app that’s only available in Python. In that case, knowing how to handle Python can be beneficial. Even if you do most of your scripting in R.

Or if some part of your R analysis is slow, it can be helpful to refactor some of your code in a compiled programming language like C++. That way, some of your bottle-neck functions can become blazingly fast all of a sudden.

And there are lots more examples where knowing your way around other programming languages can be really helpful. No one is saying that you need to give up R and become an expert in the other languages, though. You can make great progress by just knowing the basics of a language and combining that with your programming knowledge that you have already acquired through R programming.

In that spirit, this collection of resources is supposed to help you ease your way into other programming languages. The goal here is to highlight resources that will give you a gentle nudge towards these other languages when coming from R. Some of the resources I have created, some of them are things I’ve collected from the wider R community. If I forgot one of your favorite resources, don’t hesitate to put them into the comments.