This is a QFAB course and places are highly limited as Graduate School is paying the fee. If you are unable to attend, please de-register (or email graduateschool@uq.edu.au if you are unable to do so). An unexplained absence could result in all your future QCIF registrations becoming Waitlisted.

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. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. 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. Please do not sign up for this workshop if you are unwilling or unable to attend both days. If you are unsure if R is the correct tool for you, you might want to attend the Library's R workshops first.

About Research software and programming

At UQ, there are many research tools available at your disposal. Preview the available research software and programming languages and learn the pros and cons of each. Investigate how to use them to effectively analyse, manipulate, and visualise your research data.
 

Library workshops

The library offers a range of workshops under the headings below including:

R with RStudio  Excel for research data  NVivo Pro

Useful links

  • Library's software training resources
  • Software carpentry: The open-source movement to teach basic lab skills for research computing.
  • REDCap consortium: A secure web application for building and managing online surveys and databases. UQ is a member of the consortium
  • RStudio cheat sheets
  • Hacky Hour (supported by RCC, QCIF, IMB and wonderful volunteer helpers!): RCC runs consultations every Tuesday at Cafe Nano. Come and ask IT questions such as: getting started with Python and R, stats, machine learning, bio-image, software tools, research data management (RDM), how to access high performance computing, cloud data storage and tools for data cleaning and data visualisation. Bring your project's IT problems or come along as a helper! Very occasionally Hacky Hour may not run; follow us on Twitter (@HackyHourStLuc​) to get the latest announcements. Aside from the usual IT research questions, 
    • First Tuesday of the monthBioinformatics Hacky Hour, ask bioinformatics analysis, software and pipeline questions
    • Last Tuesday of the monthBioLab Hacky Hour, ask about experimental design and technical sample processing questions to get started in the lab