Using R for Advanced Statistical Analysis and Analytics

This course is intended for those who have basic knowledge and experience with R, and would like to further advance or develop their experiences in R and advanced statistical methods. The course would also be suitable for people familiar with these methods in other packages, but wanting to learn R for the first time.

Level 3 - runs over 5 days

Dr Mark Griffin is the Director of Insight Research Services Associated (, and holds Adjunct appointments within the School of Public Health, University of Queensland and the Sydney Medical School, University of Sydney. Mark serves on the Executive Committee for the Statistical Society of Australia, and is Chair of their Section for Business Analytics. Mark also serves as the Asia-Pacific Regional Director for the International Institute of Business Analysis, is Chair of their Business Analytics Special Interest Group, and is an IIBA Endorsed Education Provider. He is currently doing research with the Queensland Ambulance Service analyzing their incident reports, where the QAS visits approximately 700,000 incidents per year. To date he has presented over 80 two-day and 10 five-day workshops in statistics around Australia.

Course dates: Monday 11 February 2019 - Friday 15 February 2019
Week 3
About this course: 

R is a free software environment for scientific and statistical computing and graphics that runs on all common computing platforms. An active and highly skilled developer community works on development and improvement. It has become an environment of choice for the implementation of new methodology. It is at the same time attracting wide attention from statistical application area specialists. The powerful and innovative graphics abilities available in R include the provision of well-designed publication-quality plots.


Course syllabus: 

Day 1

  • Study design and reporting (including Setting goals and objectives, Inclusion and exclusion criteria for participant selection, Data Management and data linkage, Reporting styles including the CONSORT statement, Ethics including privacy and confidentiality)
  • Linear, logistic and Poisson regression - Including odds ratios, incidence rate ratios, and regression diagnostics


Day 2

  • Analysis of Variance
  • Factor analysis – including factor rotations, uniqueness and commonality


Day 3

  • Longitudinal data analysis - mixed effects models and generalized estimating equations


Day 4

  • Clustering techniques – k means clustering, cluster linkage, and dendrograms
  • Missing data and multiple imputation


Day 5

  • Data visualization – including radial plots, clustered and stacked bar charts, and donut plots. This will focus on ggplot and Rshiny.


Course format: 

This course may run in a computer lab, or you may be advised to bring your own laptop with specified software.

We will let you know in advance.


Approximately half of the time in this workshop will be spent in PowerPoint seminars, and the other half will be spent in computer demonstrations and self-paced computer exercises (using datasets publicly available within R).

Recommended Background: 

This course is intended for three different demographics

  • Participants who have a basic knowledge and experience with R, and would like to further develop this expertise
  • Participants who have completed a basic course on R with ACSPRI, and would like to take their skills to the next level
  • Participants who have some familiarity with these statistical methods in other software (eg SPSS, SAS or Stata), and who wish to learn the R system (potentially as new R users).


A basic knowledge of statistics is assumed. No prior knowledge of the statistical methods taught in this course or any prior experience with R is assumed, though students with prior knowledge will be better suited to tackle the more advanced topics in this workshop.


Participants must be comfortable with typing commands at the command line.


Recommended Texts: 

Maindonald, J.H. and Braun, W.J. (2010). Data Analysis and Graphics Using R. An Example-Based Approach. 3rd edn, Cambridge University Press.


Course Fees:
All courses at a given program have the same fee structure, but fees vary depending on whether your organisation is an ACSPRI Member and whether there are Early Bird Discounts available at the time. The prices for this program are available on the program page.

Q: Do I have to have any prerequisites to do this course?

A: Yes see recommended background section.


Q: Is R really free and publicly available?

A: For sure. R has been / is being developed by an online community of statisticians and programmers around the world that have made all of their work available for the benefit of users.


Q: Can I download and install R prior to the workshop?

A: Yes, please visit It is assumed that most if not all participants will not have installed R prior to the workshop, though there may be a couple of eager participants who want to make a head start.


Participant feedback: 

Made it easy to understand (Summer 2018)


I’m going to utilize the skill I learnt here in my every day job. (Summer 2018)


Mark provided a reasonable balanced of self-guided work with interactive lectures- it was very well paced! (Winter 2017)


Got a good introduction to some important techniques in R and how they can be used in real world settings. (Winter 2017)

Summer Program 2019

The instructor's bound, book length course notes will serve as the course texts.