Week 1 Introduction to R
For modern evolutionary biologists, handling large amounts of data is a fundamental skill. Familiarity with a programming language, particularly one that makes it straightforward to visualise, explore and filter data is the best way to achieve this ability. There are many different types of programming and scripting languages; the entire concept may seem daunting at first, especially if you have never encountered it before. This is natural, many other biologists applying scripting tools on a daily basis have started from similar first principles. A little patience with the basics of any form of programming and you will soon be able to do much more than you thought possible.
For this section, we will be introducing you to R, a statistical programming language and environment that is widely used in the biological sciences. R is flexible, clear and easy to learn. It also extremely good for producing quick, high quality visualisations of data, which makes it very useful for anyone trying to explore their data. Perhaps the greatest strength of R is its focus on statistics - this makes it an excellent tool for carrying out and learning statistical analysis. R is also used for data analysis beyond evolutionary biology - it forms the basis of data science for companies such as Google, Facebook and Twitter, among many others. If you find yourself wondering why you are learning a programming language, it is worth remembering this point - familiarity with R and scripting will provide you with a very flexible and useful skill.
We believe the best way to get an idea of what R is and what it is capable of is to dive straight in. There really is no better way to learn and understand than to demonstrate the workings of the language to yourself. As a brief overview, we will show the utmost basics here before moving onto more advanced topics in the next chapter. We will also introduce some basic statistical concepts for which R makes visualisation and understanding straightforward. Together these first two chapters will form the foundations for applying R to more evolutionary genetics focused questions.