Welcome to the Research Computing Services R/RStudio repository. R is a free, open source and cross-platform software for manipulating datasets and producing statistical plots and analyses. R is commonly used in many scientific disciplines for statistical analysis.
Here you will find online learning resources which are intended to complement our in-person workshops.
Are you wondering why you should use R? Have a look at this video to find out why:
The goal of this workshop is to teach novice programmers to write code in R for data analysis. R is a free, open source and cross-platform software for manipulating datasets and producing statistical plots and analyses. R is commonly used in many scientific disciplines for statistical analysis.
The emphasis of this workshop is to give attendees a strong foundation in the fundamentals of R, and to teach them how to use the packages from R to manipulate, analyse and visualise data. This workshop is also intended as an opportunity for attendees to meet and network with the community that is actively using R for research.
Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. If you would like help with your statistical analysis, you can contact the Statistical Consulting Centre for one-on-one consultations statistical training courses.
A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.
You don't need to have any previous knowledge of the tools that will be presented at the workshop. However, if you have no previous programming experience, we recommend that you consult the course materials for introduction to programming concepts prior to attending the current workshop:
[course material online](TODO: JEREMY LINK)
A course explaining how to make your R-Code run faster.
We explore how to profile (where are the bottlenecks) and optimise your code. We also explore how to make the best of your computer’s power with parallelisation. The final part of the course explores what other options there are @unimelb for running your code in the cloud or at a high performance computer.
The Material is divided into two sections
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Part 1: Presentation on how to make your code run faster in your computer.
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Part 2: Cloud and High performance computing at Melbourne University.
TODO: Beautify Webpage for e-learning self-containment
- Control Flow (TODO)
- Functions (TODO)
TODO: Add presentation
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Intro to R Cheatsheet
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Useful Commands for data analysis TODO Centralise this
TODO: Add Magazine tips
Here we can add all the other workshops and communities at unimelb
Maybe send an email to people running R-related stuff to send us relevant events…
Add links to David's guides!
TODO: Centralise Nikki's book
R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.
Windows
Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE.
Mac OS X
Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.
Linux
You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install the RStudio IDE.
Create your analyses using RStudio directly from your browser. There is no software to install and nothing to configure on your computer.
RStudio Cloud is currently free to use and you can access it by creating an account here.
The Melbourne Research Cloud provides Infrastructure-as-a-Service cloud computing to the University of Melbourne researchers, providing access to a robust set of on-demand virtualized computing resources (such as servers and storage). The service makes it easy for researchers to quickly access scalable computational power as their research grows, without the overhead of spending precious time and money setting up their own compute environment.
Using the Melbourne Research Cloud you can install RStudio on a cloud computer and access it through the web. You can find instructions on how to do this here.