General Information

Introduction to R for non-programmers.

The goal of this workshop is to teach novice programmers to write code in 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 them how to use the packages from Tidyverse to manipulate, analyse and visualise data.

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.

Who: 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.

Where: COLAB (Room 329), Level 3, ERC, Building 171, University of Melbourne, Parkville. Get directions with Google Maps.

Requirements: **Participants must bring a laptop** 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 (details below).

Contact: Please email pablo.franco.dn@gmail.com or research.bazaar@gmail.com for more information.

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Schedule

Surveys

Please be sure to complete this survey at the end of the workshop.

End of day survey (NPS)

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

Google Docs: https://docs.google.com/document/d/19w_GjSnLFWxSlmYdzHMsqOUKE1LziilaR6A3-NkxrsQ/edit?usp=sharing.
We will use this Google Docs for chatting, 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.

For visitors who are not from a tertiary institution, the visitor login details for the "DataCarpentry" network are:

• password: hackerwithin