If you have ever found yourself staring at the screen wondering where to start, thinking “how does anyone learn this damn program?”, then this website is for you.

This site is a series of self-guided tutorials on using the R statistical package for data manipulation, analysis and visualisation including the following:

- Setting up your file system and working environment in a reproducable manner
- Viewing and manipulating your data
- Statistical analysis, covering analysis of variance, linear regressions and generalised linear models (more to come!)
- Visualisation of your results using an iterative graphing approach

I am entirely self-taught through online tutorials, textbooks and advice from colleagues. The best advice I can give is to put in the time and effort. Force yourself to work through problems in R, rather than resorting to excel or your known statistical package. Spending the time to learn R and using it frequently will reinforce lessons and how to utilise code, this goes for all coding languages.

The initial focus of this site is data science techniques for biology (mostly ecology) based research, however, this will hopefully improve in scope in the future. Tutorials have initially been developed as part of a teaching module for undergraduate students in the School of Biology at the University of Wollongong but have been expanded beyond that initial development.

Special thanks to Professor Alistair Poore & Associate Professor Will Cornwell for their advice and excellent work on Environmental Computing, which was a huge inspiration and resource during the development of this site.

**Site administrator:** Mitchell Stares

**Disclaimer:** This site has been developed soley by Mitchell Stares, a PhD candidate in forest ecology at the University of Wollongong. Material for this site has been partly developed as material for an undergraduate subject at the University of Wollongong, while under employment as an associate lecturer. Mitchell holds no formal qualifications or certifications in statisitics, mathematics or computer science. Knowledge has stemmed from years of coding in R, self-taught through tutorials, textbooks and colleagues. Many of the resources for this site have been adapted from multiple online websites, and references to each of those websites will be updated shortly.

Last updated: 15th November, 2018