Introduction to R

3 February 2025

Register here!

Course Overview

R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R. It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers.

This course will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focused on developing practical skills.

Course Outline

This course will cover topics including:

• Basics of interacting with R – calculations, saving variables so you can reuse them, data types and structures, organising R code in scripts
• Tidyverse – a basic introduction to tidy R code
• Data – reading in and organising data (from spreadsheets) with dplyr
• Plotting – make beautiful figures with ggplot

Course Requirements: You will need a computer with administrator access (to install R and RStudio software before attending the course).

Date: Monday 3 February 2025

Duration: 9.30am - 4.30pm

Location: This workshop will be delivered in person ONLY!

You will receive a certificate of completion for the course.

Introductory Statistics for Researchers Using R

5 - 7 February 2025

This is an in person course. Slides are in PDF format, exercises are in R markdown, and both will be downloadable in advance. If HTML slides and alt text are needed we will make every effort to provide these, please let us know well in advance. Lectures will be recorded and available for a week following the workshop.

Important Notes - please read

1. Participants must have basic R skills prior to workshop

This is NOT an introductory workshop in using the statistical software package, R. Basic R coding will not be taught. To do this workshop successfully, you must have basic proficiency in using the R package. All examples and exercises used in this workshop are done using R. We want to ensure everyone is able to follow the material, and no participant is disappointed.

• If you have basic R skills, that's excellent, please complete this quick task HERE. Once you have completed this and emailed your results to stats.central@unsw.edu.au, then you will be given a code to allow you to register.
• If you do not have basic R skills, but want to do the Introductory Statistics for Researchers workshop, you can enrol in our Introduction to R course, February 3, that runs ahead of this workshop. 

2. Own computer

You will need to bring and use your own computer during the workshop with both R and RStudio installed. You will also need administrator rights to install further packages needed throughout the workshop.

This workshop is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. It is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this workshop can serve as a first introduction or a refresher. The theory behind the statistical procedures will, in general, not be discussed.

A range of statistical analyses will be discussed in the workshop, as described in the outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work.

Content

You will be expected to watch this seminar on study design and statistical principles (samples and populations, confounding, statistical inference) ahead of the workshop.

Course outline

Revision

• Descriptive statistics – mean, mode, standard deviation, inter-quartile range, correlation
• Data visualisation - boxplot, histogram, scatterplot, bar graph

Introduction to statistical inference

• Uncertainty, confidence intervals, p-values, significance/evidence
• T-test (comparing two groups)
• Checking model assumptions

Analysis of continuous responses with linear models

• Simple linear regression
• ANOVA
• Multiple regression, ANCOVA

Analysis of categorical responses

• Relative risk, odds ratios
• Chi-square test
• Logistic regression

Presenter and Expertise: Peter Humburg, Senior Statistical Consultant, UNSW Stats Central

Date: Wednesday 5 to Friday 7 February 2025

Duration: 9.30am to 4.00pm - each day

Location: This workshop will be delivered in person ONLY!

You will receive a certificate of completion for the course.

Fundamentals of Regression in R

11 - 13 February 2025

Register here!

Course Overview

This course provides a comprehensive hands-on introduction to regression analysis techniques The course content is designed for researchers with some prior knowledge of basic statistical testing, such as t-tests, p-values, confidence intervals and simple linear regression. The primary focus is on developing a conceptual understanding of regression models through numerous examples. There will be a strong emphasis on practical implementation in R, and interpretation of output. Approximately half the time will be dedicated to practical hands-on sessions.

The core content starts from linear models with more than one variable, enabling research questions like "What is the effect of this treatment/intervention after adjusting for confounding variables?" or "What is the relationship between two variables while controlling for other factors?" We then cover interactions between variables in linear models, enabling research questions like: "How does the effect of the treatment depend on some other variable? Is the treatment effect different between groups?" and "How is the relationship between two variables modified by some other variable?"

Fundamental regression concepts and skills that arise in regression, like multicollinearity, multiple testing, model selection, generalising the linear model to data that is non-normal (e.g., binary response and count data), are all covered in this course. By the end of this course, you will have a foundation in regression modelling techniques with the practical experience in R needed for more advanced regression methods like mixed models, longitudinal data analysis, survival analysis, meta-analysis, generalised additive models, multivariate analysis, ordinal and multinomial regression, spatial regression and other extensions.

Course outline

Day 1: Revision, Multiple Regression Introduction and Extensions

Day 2: Morning/Afternoon: Multiple Comparisons/Model Selection

Day 3: Morning/Afternoon: Generalized Linear Models (GLMs)/Generalized Additive Models (GAMs)

Accessibility

This is an in person course. Slides are in PDF format, exercises are in R markdown, and both will be downloadable in advance. If HTML slides and alt text are needed to assist accessibility, we will make every effort to provide these, please let us know well in advance. Please email Eve (eve.slavich@unsw.edu.au) with any questions or requests.

Prerequisites: We assume knowledge of introductory statistics, including principles of study design, the concept of a p-value, the concept of a confidence interval, one-sample and two-sample t-tests, and the equivalence of a t-test to simple linear regression and simple linear regression (with a single dependent and single independent variable). All of these are covered in our Introduction to Statistics courses.

You will need R and Rstudio installed on your computer. We also assume you have some experience with R. If you are new to R, you should complete our one-day Introduction to R course, February 3 prior to this course. For more details and register HERE.

Also, you will be expected to watch this seminar on Study Design and Statistical Principles below ahead of the course.

Course requirements: You will need to bring and use your own computer during the workshop.

Presenter and Expertise: Eve Slavich, Statistical Consultant, UNSW Stats Central

Date: Tuesday 11 to Thursday 13 February 2025

Duration: 9.30am - 4.00pm, each day

Delivery Mode: In-Person ONLY

You will receive a certificate of completion for the course.