This module will provide an introduction into the R statisitical environment, going through the basics of data analysis and graphing for publication quality results. By the end of this module, you should be able to:

- Understand and use the R studio working environment
- Import and manipulate data files
- Undertake linear (ANOVA, regression) and generalised linear (logistic regression) models and associated assumptions/comparisons
- Undertake basic multivariate techniques (PCA, MDS)
- Construct bar plots and scatterplots in ggplot

R is a language and environment for statistical computing and graphics. R is free/open source software and as a result, has a community of dedicated statisticans, coders and developers increasing the capabilities and usability of the platform. R primarily runs as a command-line program.

This is a big entry barrier to many starting to learn R, so most people have turned to “R Studio”.

Since R is free and open source, it is a program and skill that can be carried with you across many institutions and jobs and is for many, the single solution for **statistical analysis, graphing** and even **GIS/spatial analysis**. Programs like JMP, SPSS and ARCGIS cost 100s if not 1000s of dollars and are quickly outdated by new versions.

However, the biggest uses of R come from its sharability and openness. Collaborating and sharing data analysis with R requires only the script and raw data. All data manipulations are done within R, requiring no editing or manipulating of your raw excel data.

R studio “reskins” the standard R environment, giving space for script writing, help, graphics output and tracking of data files. Due to the ease of working in R studio, thats what we will be using. R studio can provide an array of functions from statistical analysis and graphing, GIS/spatial analysis, presentations, document preparation (all of these tutorials are written in R) and even novel functions like interacive graphs and tweeting.

R studio is separated into 4 panels:

The top-left panel (blue) is the editor (or script window) where you can view and write your R script. This is a saveable document of code. Running code is as simple as Ctrl+Enter on a line of code or pressing the run button in the top-right of this window.

The bottom-left (red) is the console. This is the standard R environment where you can run code directly, or view the output of your script as you run it.

The top-right (green) is your workspace. This lists each “object” as you create them through your analyses. Clicking a data-frame object will allow you to view it.

The bottom-left (black) has lists of

*files*and*packages*as well as the*help*window (quickly access by typing ? before any command) and*plots*which shows any graphical output.

Now we have an idea of what R is, it is time to install **R & R Studio** onto your computer.

**Install Instructions**

1. Click Here to visit the R webpage and select one of the Australian mirrors to download (CSIRO, University of Melbourne etc.)

2. Select your version (Windows or Mac), then download the **base** subdirectory

3. Once installed, visit R studio to download R studio desktop.

Once both of those are installed, you can now proceed to open up Rstudio