3 Why switch to R

If you’ve gotten this far, I’m assuming that you’re already profeceint in STATA. Maybe you’re a seasoned researcher with scores of publications on you CV. Maybe you’re a grad student, recently emerged from the gauntlet of stats class after stats class, having learned STATA along the way. Perhaps you’re asking yourself, Why would I re-learn how to do something I already know?

The answer for many of you is: You shouldn’t. Some people don’t need to learn R, especially considering that they’ve already learned how to do everything they need to do in STATA. For everyone else, here are some good reasons to make the switch (or at least learn some R).

3.1 R advantages

  1. R is free

R is an open source programing language available for Windows, Mac, and Linux operating systems. This means that anyone can download it, use it, publish results, develop packages, and other fun stuff – without spending any money. Seriously, it’s 100% free. 100%. All versions, updates, extra packages, even some books, free. No need for temporary lisences, shelling out for perpetual liscences, buyng new versions, getting your institution to buy it for you, or borrowing that sketchy thumb drive that one person has (not that any reader ever pirated anything). It’s all free.

  1. R is free

Seriously, though. It’s free. Even if this doesn’t matter to you, if you teach, it likely matters to your students. College is expensive, especially if you go for a really long time, like getting a Ph.D. Even for students with scholarships and funding, the financial burden can be tough. Not having to buy software, on top of buying an overpriced statistics textbook (the latest edition only!), can make a big difference.

  1. R is the preferred language of statisticians and methodologists in many fields

If you want to be at the forefront of statistics or your chosen field, there’s a good chance that the latest developments are going to come in the form of new R packages before they spread to other languages or software. Even before R packages are published, people often post their work on GitHub to be downloaded and used as you will.

  1. R is a programing language

Getting comfortable in R means learning some fundanamentals of how to write basic code. While this can be extended to developing entire programs in R, learning a bit about functions and loops may mean suffering through a bit of a learning curve, but it will make you life easier down the road. Especially when it come to tedious repetitive tasks, learing a bit about coding can save you lots of time and energy. Learning R means getting comfortable with some of the more basic coding principals.

This also means that learning other prograing languages will be easier. All languages employ the same basic logic, with some variation. Understading how one languages works, means it will be a lot easier to learn another. If you want to employ the latest machine learning algorithm, for example, you’ll probably need to learn Python. If you’re comfortable with the basics, learing a new language is mostly just changing a bit of syntax.

  1. Graphics

R’s libraies for visualization – ggplot2 in particular – can produce everything from publication ready graphs, to maps, to animated 3D simulations. The possibilites are vast with other programing languages building libraries to imitate R’s. While a web developer creating visulizations may prefer something such as D3.js, for researchers, it’s hard to beat R when it comes to visualizing you data and results.

  1. Boredom

Sometimes we just want to do things a bit different. Tired of using STATA all the time, why not use R?

  1. To be condescending to your collegues

- Oh, you use STATA? That’s cute. I use R, like a real statistician.

3.2 In conclusion…

As you can see, some of these reasons are better than others. Maybe they all fit your situation, maybe none do. For those that are commited, lets write some R code.