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 if you are unable to do so). An unexplained absence could result in all your future QFAB registrations becoming Waitlisted.

R is a programming language and free software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. No matter what data analysis you are doing, there is probably an existing R “package”. The important advantages of using a programming language for conducting data analysis are the ability to reproduce, share, and automate analysis.

Recommended Participants

Researchers looking for analysis capabilities beyond those provided by Excel. No prior knowledge of R is required. The workshop is relevant for all disciplines, although examples and exercises will focus on biological datasets.


●The Rstudio integrated development environment

●The format of the R language - variables, data structures and functions

●R language and how to read and understand their documentation

●The import and processing of data within R

●The knitR package for writing reports

Learning Objectives

●Upload and process data in R to generate plots, figures and tables

●Install and use packages from the Bioconductor R repository

●Create and run functions in R

●Create reports interactively in R

●Write your own R script for data processing

While waiting, get started with R and RStudio:

NOTE: The examples used in this course will be from bioinformatics. You may also want to consider attending the R sessions in the Centre for Digital Scholarship. See the Library's training link for more information on the latest sessions.

About Research & analysis

Your research degree requires the ability to apply …'analytical and critical thinking skills to generate new knowledge, investigate problems and develop inventive solutions.' (UQ HDR Graduate Attributes, 2017) This starts with the way you design your project, your review of existing literature, the methodologies you use and the analysis you apply. Building these skills during your research degree will equip you to engage these same skills in your future career and apply them in a wide variety of contexts. 

Library workshops

The library offers a range of workshops under the headings below. 

R and Python Research data management Data visualisation

Useful links

  • The Library runs courses in data analysis, text analysis, data visualisation, geographical information systems, and 3D modelling. Popular courses include RStudio, NVivo, Python. Visit the Library Training page and look for Software or search in Student Hub for Events. (Only some sessions have been highligted in the CDF calendar below).
  • ISSR Training Courses and Services ($$)
  • Data Management at UQ (UQRDM) 
  • Sage Research Methods 
  • Join Sage’s Methodspace 
  • Publons Academy: A free online course for peer reviewers
  • Hacky Hour (supported by RCC, QCIF, IMB and wonderful volunteer helpers!): RCC runs consultations on the first and last Tuesdays of the month 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