Education and Outreach

I am excited about getting people involved in science.  I have two main avenues by which I accomplish this: teaching early career researchers how to use the R statistical manipulation platform, and second through designing websites for scientific laboratories and researchers.  Examples of both are provided below:



R courses


Before I begin, I have a confession.  I am an R addict, a code monkey.  I love it, the thrill (dare I say Rgasm?) of compiling a piece of complicated code, the frustration (AaaaRgh) of things not working as expected…like I said, I’m an addict.  I also have a passion for sharing my addiction.  I present a range of R courses aimed primarily at postgraduate students who are just begining their data analysis careers, but also accomodating senior professors that want to (need to) make the jump to the best all-round statistical and graphical platform available.  I have designed and presented a variety of R-courses for users of different levels from undergrad field course ready 4hr (2 x 2hr sessions) courses that cover some experimental design, two sample tests and making barplots, to specialist workshops.  The most popular course is the R-bootcamp that I co-present with colleagues Justin Touchon  & Andy Jones, below is an extract from the course syllabus:

For full details of courses, including availability and pricing, feel free to email me

R-Bootcamp Course Outline


The objective of the 3-Day (6 session) R-Bootcamp is to provide students with a solid foundation in the use and capabilites of the R platform and to get over the initial steep learning curve.  R is notoriously “expert friendly” and while many new researchers attempt to learn it, many return to alternative programs quickly as they lack the basic skills necessary to master it.  This bootcamp is specifically designed to equip students with the tools to quickly build competency in R analyses, and a framework for continued learning and capacity building.


“Really well organized. The lectures progressed at a really nice pace and built on from previous lectures nicely. The content suited me perfectly – it included enough revision of basic stats that I could clearly follow what was going on, but we also covered the things I was hoping to learn how to do.”

2011 Course participant

 “The R-Bootcamp is the best way to learn R in my opinion. I learned more from this short course than I did in weeks of self-teaching. A great opportunity and great instructors, I highly recommend this course for everybody who plans to use R in any capacity.”

2012 Course participant

Course Syllabus

Day 1

Objectives:  By the end of the first day students will be able to enter data into R either directly or by uploading a file.  Through creating and manipulating objects in the R environment, students will become comfortable with the R prompt and gain familiarity with the types of objects that can be created, manipulated and summarized in R


AM session

Objectives:  Launch & navigate R.  Format data and upload to R


Lecture: Introduction to R

Launching and customizing R  |  Using text editors  |  Finding your way around in R

Getting Help  |  Formatting data for R  |  Missing data  |  Loading data into R


Workshop:  The instructors will assist all students individually to ensure that data can be loaded into the R workspace


PM session

Objectives:  Learn how to create various objects in R (i.e. different types of data), and combine and manipulate those objects into a model dataset that we will use for the exercises to follow.

Students will become comfortable with entering data directly from the command line prompt, learn how to subset and summarize data within R (instead of creating sub-datasets in an external program), and export (save) data from the R environment.


Lecture: Creating objects in R

Vectors | Data frames | Matrices | Lists

Subsetting data | Summarizing data | Exporting data from R


Day 2.

Objectives:  Learn how to conduct basic statistical tests and linear models using R


AM session:  Two-sample tests and probability theory

Lecture: Basic statistics in R

Selecting the right test | Normality | t-tests (one-, two-, and paired-samples)

Wilcoxon sum rank | Chi-square | Fishers exact | Correlations


PM session: Linear models

Lecture: Introduction to linear models

Linear models in R | Assumptions of normality | ANOVA | ANCOVA | Multiple regression

Interpreting output | Summary | anova vs Anova | What to report



Day 3.

Objectives: Students will learn how to perform generalized linear models and construct publication ready plots using their own data


AM session:  Generalized linear models

PM session:  Plotting data in R
Web design