Topic 3 Introduction to R and R Studio

3.1 By the end of this section, you should be able to:

Workflow is different
- Organise files and data differently
- Workspace can contain data and outputs
- Can manage multiple datasets within a workspace
Learning curve can be steep initially
- e.g. Variables and coding, scripts
Need to know what you want
- e.g. building your regression model / ANOVA error terms

Using scripts means analysis is easy to follow and reproduce
R scripts are small, online collaboration, no SPSS “older version” problems
Data can be organised and reorganised however you need it (tidyr)
Packages are available for “cutting edge” analysis: e.g. Big Data & Machine Learning
A robust language for precise plots and graphics (ggplot)
R analysis code can be embdeded into documents and presentations (R Markdown)

Click on these links to download:

R Studio IDE

The interface for R Studio looks daunting at first. However, there are 4 main sections, 2 on the left and 2 on the right.

MAIN TOP: R Script files or R Document Files
- Where we usually type our code as a script before we run it. Script files are usually saved so we can work on them and rerun the code again later (.R files).
MAIN BOTTOM: Console
- Shows the output of our R code. We can type R code directly into the console and the answer will ouput immediately. However, it is more convenient to use script files.
RIGHT TOP: Environment
- Contains all of the objects (e.g. data, analysis, equations, plots) that are currently stored in memory. We can save all of this to a file and load it later (.RData files).
RIGHT BOTTOM: File Browser
- The folder that R is working from is called ‘the working directory’ and it will automatically look for files there if we try to import something (e.g. a data file). Using the more button on the file browser allows you to set your desired working directory.