'Data Analysis And Visualization With R' Course

 

Registration Now Open

Registration is now open for two advanced, one-day workshops in R. Space is limited, so please register at your earliest convenience.

  • 30 April - Advanced topics in R: Community Ecology and Multivariate Statistics - Register Here
  • 2 May - Advanced topics in R: An introduction to linear mixed-effects models in R - Register Here

Costs for each:

  • WSU HDR students - $20
  • WSU staff - $30
  • External academic (HDR students) - $100
  • External academic (non-HDR) - $150
  • Corporate - $300

For those of you interested in the introductory, week-long workshop, we anticipate that this will be offered the week of 24 September 2018. Registrations will open closer to the date, but please express your interest here.

Advanced topics in R: Community Ecology and Multivariate Statistics

Monday 30 April, 9:30 – 16:30 (lunch break 12:30 – 13:30)

For this workshop it is mandatory that you have a working knowledge of the R computing environment – this is not an introduction to R.

Many studies involve data collection for multiple variables on the same experimental unit, but analysis of these data require special multivariate approaches. There are several well-developed and well-maintained R resources for analysis of multivariate data, eliminating the need to pay to license proprietary software. This one-day session will include topics covering ordination (principal component analysis, correspondence analysis, metric and non-metric multidimensional scaling), two-table analysis (redundancy analysis, canonical correspondence analysis, constrained analysis of principal coordinates), indicator species analysis, variation partitioning, and statistical hypothesis-testing (stepwise model building, [permutational] multivariate analysis of variance, multivariate generalised linear models). Examples using both quantitative and qualitative explanatory variables will be included. The session will also cover the preparation of publication-quality figures for inclusion in manuscripts.

For both the morning and afternoon sessions, we combine short lectures with hands-on practice time. Computers are provided with an installation of R and RStudio, but you can bring your own laptop if preferred (campus Wifi available).


Advanced topics in R: An introduction to linear mixed-effects models in R

Wednesday 2 May, 9:30 – 16:30 (lunch break 12:30 – 13:30)

For this workshop it is mandatory that you have a working knowledge of the R computing environment – this is not an introduction to R.

Linear mixed-effects (LME) models are a popular technique in the analysis of longitudinal data (repeated measures), and hierarchical data (nested designs).  We use LME to not only estimate ‘fixed effects’ (treatments, continuous predictors, etc.), but also effects associated with individuals sampled at random from the population of interest. These are 'random' effects and convey information about the degree that individuals in a population differ but not how or why they differ.

This workshop gives an introduction to linear mixed-effects (LME) modelling with the lme4 package in R. Rather than to explain the theory and technical background of LME, we focus on a hand-on approach with many practical examples.  Key concepts will be explained and demonstrated with the use of example datasets.

For both the morning and afternoon sessions, we combine short lectures with hands-on practice time. Computers are provided with an installation of R and RStudio, but you can bring your own laptop if preferred (campus Wifi available).


Do you want to take your data analysis skills to the next level?

The R statistical computing environment has become a standard for scientific data analysis, visualization and reproducible research.

At the HIE, we offer an introductory course to help you climb the steep learning curve.

This five-day course is aimed at postgraduate students and staff, and is for newcomers to R, as well as intermediate users.

The course covers

  • Basics of R (data entry, manipulation, tabulation, special data types)
  • Visualisation (basic and advanced plotting of data)
  • Statistical analyses (basic statistics, linear models)
  • Project management (workflow and organization, principles of reproducible research).

The course will consist of short lectures with ample time for hands-on exercises.

The exercises are set up with different levels: more advanced students can work on more difficult problems.

We assume that you have completed some introductory statistics course at university level, but no previous programming experience is required.

Instructor

  • Assoc Prof Jeff Powell

Class Resources:

The following resources are available for this course:

Event Contact

For all inquiries please contact Assoc Prof Jeff Powell on jeff.powell@westernsydney.edu.au.

Event Details

Venue

Hawkesbury Institute for the Environment
Western Sydney University
College Drive
Richmond NSW 2754


Times:

9.30am - 4.30pm


Accommodation Options:

We recommend these local accommodation options:

Crowne Plaza Hawkesbury (opens in a new window) - approx. 10 minutes by car to campus or 20 minutes by train (station is around 10 minutes' walk from hotel)

New Inn Motel (opens in a new window) - approx. 10 minutes' drive or 25 minutes' walk to campus. The University Shuttle Bus routes are nearby - track the shuttle bus locations online. (opens in a new window)

Stay On Campus (opens in a new window) - Short Stays may be available through Western Sydney University Village.

Course Presenters

Eucalyptus Leaf