General Information

Introduction to R for non-programmers.

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

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 or see their flyer for details of 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. These materials are a modified version of those used by Software Carpentry for their "R for reproducible scientific analysis" workshop: Greg Wilson: "Software Carpentry: Lessons Learned". F1000Research, 2016, 3:62 (doi: 10.12688/f1000research.3-62.v2).

Who: The course is aimed at graduate students and other researchers. 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 material for introduction to programming concepts workshop prior to attending the current workshop.

Where: Day One (2017-10-03) - COLAB (Room 329), Level 3, ERC, Building 171, University of Melbourne, Parkville;

Day Two (2017-10-06) - COLAB (Room 329), Level 3, ERC, Building 171, University of Melbourne, Parkville. Get directions with Google Maps.

Requirements: **Participants must bring a laptop and charger** with a Mac, Linux, or Windows operating sytem (not a tablet, Chromebook, etc.) that they have administrative privileges on.

Participants are also asked to install R, and the RStudio IDE prior to the workshop (See instructions ).

Contact: Please email pablo.franco.dn@gmail.com or nicole.unimelb@gmail.com for more information.

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Schedule

Surveys

Please be sure to complete these surveys at the end of each day of the workshop.

End of first day survey

End of second day survey

Topics

1. Introduction to R and RStudio
2. Seeking help
3. Data structures
4. Data frames and reading in data
5. Subsetting data
6. Creating functions
7. Creating publication quality graphics
8. Vectorisation
9. Control flow
10. Writing data
11. Dataframe manipulation with dplyr
12. Wrapping up

Other Resources

• Reference
• Instructor’s Guide

We will use this google doc for taking notes, and sharing URLs and bits of code.

Setup

Requirements: **Participants will be required to bring their own laptop and charger**

Wifi: If you're bringing a wifi device to the workshop, access to the wifi network will depend on whether you're affiliated with the Univerity of Melbourne. University of Melbourne staff/students can connect to the UniWireless network; instructions on how to do this and where to get assistance can be found here.