Intermediate R: Capacities for Analysis and Visualisation

Learning R inevitably takes time and often the next best step after learning the very basics of this programming language and its most important characteristics (which was covered in the Introduction to R class) is to get a good overview of what and how it is possible to do in R in terms of various analyses. The details and intricacies with regard to each specific topic and methods are almost limitless – and in the end, this is part of what makes R so great – but for getting to know R on the intermediate level these might be of secondary importance. If you have a basic knowledge of R, then the most important next step is to acquaint yourself with the range of options that are available. You do not need to know the details – they will come later – you just need to know what is possible, what to look for and expect, and how to ask the right questions. These will make the steps that follow, the steps that you are most likely to do on your own while working on your analyses, much simpler and smoother. And this is what the purpose of this class is.

The class will cover many topics, which in their substantive breath could fill several week-long classes. But what we will focus on in the class is simply their technical implementation in R. For this class you do not need to know in any meaningful depth what e.g. multilevel models, panel data analysis or any of the other covered methods are, although it would be very good to have at least a basic idea. We will just look at their implementation in R – what packages and functions to use and what data structure is implied – with only a few very basic comments about the nature of the method and the type of data that implies the latter. This is not a class on statistical methods as such, but a class on how they can be used in R. We will cover much technical ground and very little substance.

The class will begin with a more general issue of how to work with R most effectively. We will look at some principles on how to write good, comprehensible and efficient R code and how to structure your files and folders, so that your work would be smooth. Thereafter we will look at two related topics – simulations and bootstrapping – which are applicable across specific methods as means to learn and understand them (simulations) or to get a grasp of the amount of uncertainty in your data and the results you can get from the latter (bootstrapping).

In the next two and a half days we will go over the implementations of some of the most common methods of statistical analysis in R. We will look at how to do factor analysis and structural equation models, how to work with panel data and multilevel data and many related analyses (for a more detailed list, see the day to day schedule). The structure of all of these micro topics will be the same:

• We will devote the very minimum amount of time for refreshing the basic logic of the method – what kinds of questions and what kind of data is it meant for?
• We will look at the main packages in R that provide the functionality to implement this method.
• We look at how to use them – what functions they provide, what arguments the latter require and what is the structure of the output that they give us.
• When the analysis is done, it is important to get an overview of the results. We will look at how to get a good summary of your analysis and how to create the basic visualisations to evaluate your models.

More or less each model comes with its basic options for plotting, but sometimes you want more. Therefore, towards the end of the class we will take a longer look at how to use ggplot2, an R package, which allows you to make almost any kind of data visualisation that you should desire. ggplot2 is like a language within R, and to learn it fully would take more than one week-long class. So we will only be getting our foot through the door to see what is possible with this package and how as far as the main kinds of plots one would want to make are concerned – barcharts, linecharts, scatterplots and the like – as well as how to customise them to your liking and save them in pdf or some other format of your choosing.

Creating visualisations is one way – and perhaps the best way – to get your results out of R, but sometimes you also need good looking tables of model output. In that case, an R package called stargazer can save the day. Copy-pasting numbers from R to a table that you have manually created in your document is time (sometimes countless hours) that could be well spent doing something better. Stargazer gives you the possibility to export your model results from R directly into LaTeX or into html format, and the latter can easily be transferred to Word or any other software you might be using.

When you have covered the topics of this class – when you know how to work in R more effectively, what kinds of analyses it is possible to do and how and the ways in which you can get them out of R quickly and effortlessly – then you are already well on your way to becoming a proficiency user of R.

The class assumes a solid basic knowledge of R on the level comparable to the Introduction to R class offered in the ECPR summer school and is best taken as a continuation course for the latter. Additionally, a basic knowledge of the most common statistical methods used in the social sciences is expected (what are they, what do they do and when should they be used).