In this tutorial, we will explore R as a tool to analyse and visualise data. R is a statistical programming language that has rapidly gained popularity in many scientific fields. The main difference between R and other statistical software like SPSS is that R has no graphical user interface. There are no buttons to click. R is run entirely by typing commands into a text interface. This may seem daunting, but hopefully by the end of this tutorial you will see how R can help you to do better statistical analysis.

So why are we using R and not one of the many other statistical packages like SPSS, SAS, or Microsoft Excel? Some of the most important reasons:

• Unlike other software, R is free and open source, and always will be!
• R is a programming language rather than a graphical interface like SPSS. It carries out analyses or visualizations by running a few lines of code. These lines of code can be saved as scripts for future repetition of the analyses or visualizations. It also makes it very easy to share your work with others, who can then learn from it or correct it if it contains a mistake.
• R has a very active and helpful online community. When you run into a problem, very often a quick Google search is all it takes to find a crowd-sourced solution.
• All leading marketing research companies indicate that they are experimenting with R and that R is the software of the future.

This tutorial focuses on statistical analyses relevant for marketing students. If you want a more extensive but very accessible introduction to R, check out the excellent and free book ‘R for Data Science’. This introductory chapter and the next are based on the ‘Intro to R 1’ found at Coding Club Tutorials, which has many other great R tutorials as well.

This tutorial has been written in RMarkdown, with the help of the amazing bookdown package.

A special thanks to Samuel Franssens for developing this tutorial