Session 01: Introduction to R I

The cost motive is obvious, but the benefits that come from being fully open-source, with a critical mass of users are many.

Firstly, there is a burgeoning array of online forums, tutorials and code examples through which to learn R. StackOverflow is a particularly useful resource for answering more individual questions.

Second, with such a large community, there are numerous expert R users who themselves contribute by developing libraries or packages that extend its use.

In recent years there has been much introspection around how science works — around how statistical claims are made from reasoning over evidence. This came on the back of, amongst other things, a high profile paper published in Science, which found that of 100 recent peer-reviewed psychology experiments, the findings of only 39 could be replicated. The upshot is that researchers must now make every possible effort to make their work transparent. In this setting, traditional data analysis software that support point-and-click interaction is unhelpful; it would be tedious to make notes describing all interactions with, for example, SPSS. As a declarative programming language, however, it is very easy to provide such a provenance trail for your workflows in R since this necessarily exists in your analysis scripts.

Concerns around the reproducibility crisis are not simply a function of transparency in methodology and research design. Rather, they relate to a culture and incentive structure whereby scientific claims are conferred with authority when reported within the (slightly backwards) logic of Null Hypothesis Significance Testing (NHST) and p-values. This isn’t a statistics session, but for an accessible read on the phenomenon of p-hacking (with interactive graphic) see this article from the excellent FiveThirtyEight website. Again, the upshot of all this introspection is a rethinking of the way in which Statistics is taught in schools and universities, with greater emphasis on understanding through computational approaches rather than traditional equations, formulas and probability tables. Where does R fit within this? Simply put: R is far better placed than traditional software tools and point-and-click paradigms for supporting computational approaches to statistics — with a set of methods and libraries for performing simulations and permutation-based tests.

You should see a set of windows roughly similar to those in the figure above. The top left pane is the Code Editor. This is where you’ll write, organise and comment R code for execution. Code snippets can be executed using Run at the top of the RStudio pane or typing cmd R (Mac) ctr R (Windows). Below this, in the bottom left pane is the R Console, in which you write and execute commands directly. To the top right is a pane with the tabs Environment and History. The purpose of these will soon be clear. In the bottom right is a pane for navigating through project directories (Files), displaying Plots, details of installed and loaded Packages and documentation on the functions and packages you’ll use (Help).

You’ll initially use R as a calculator by typing commands directly into the Console. You’ll create a variable (x) and assign it a value using the assignment operator (←), then perform some simple statistical calculations using functions that are held within the (base) package.

In these introductory sessions, you’ll mainly be using a collection of packages that form part of the so-called tidyverse. Do read some of the documentation provided from the project website to learn more around its philosophy. The packages together provide a very intuitive means of interacting with R and support analysis tasks that form most Data Science workflows.

There are two steps to making packages available in your working environment. install.packages(<package-name>) downloads the named package from a repository, library(<package-name>) makes the package available in your current session.

The sessions will cover a somewhat depressing, though very current, topic: the UK’s referendum vote on membership of the EU. You’ll start by exploring the results data published at Local Authority level and made available in .csv form by The Elecctoral Commission. You’ll do so using the readr package (part of the tidyverse).

Notice that the results data now appears under the Data field of the Environment pane. It is stored as a data frame — a spreadsheet-like representation where rows correspond to individual observations and columns act as variables. You can inspect a data frame as you would a spreadsheet by typing View(<dataframe-name>) or by pointing and clicking on the named data frame in the Environment pane. You can also get a quick view on a data frame’s contents by typing glimpse(<dataframe-name>).

Data wrangling type operations are supported by functions that form the dplyr package. Again, dplyr is within the family of packages that comprise the tidyverse. Its functions have been named with verbs that neatly describe their purpose — filter(), select(), arrange(), group_by(), summarise() and more. The pipe (%>%) is a particularly handy operator that allows calls to these functions to be chained together.

ggplot2 is a powerful and widely used package for producing statistical graphics — and again a package that is core to the tidyverse. It has a very strong theoretical underpinning, based on a framework for data visualization known as The Grammar of Graphics (Wilkinson 2005). The general approach is of treating graphical elements separately and building features in a series of layers.

In this section, you will analyse variation in EU referendum voting behaviour by Local Authority by generating various Choropleth maps. You will do so using the tmap package: a new, user-friendly library that adopts a syntax very similar to that of ggplot2.

The general form of a tmap specification:

Content by Roger Beecham | 2018 | Licensed under Creative Commons BY 4.0.