Introduction to R & Statistical Modelling in R - Virtual Classroom

Date: Tuesday 11 May 2021, 9.00AM
Location: Online
CPD: 12.0 hours
RSS Training

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Level: Foundation (F)

This virtual course is scheduled to run over 4 mornings. The purpose of this course is to introduce participants to the R environment for statistical computing.  The course focuses on entering, working with and visualising data in R, regression modelling in R, including linear and general linear models 
Level: Foundation (F)

The purpose of this course is to introduce participants to the R environment for statistical computing. The course focuses on entering, working with and visualising data in R, regression modelling in R, including linear and general linear models.

Learning Outcomes

By the end of the course, participants will be able to use R to:

  • Direct themselves around the R interface in an efficient way
  • Import and export their own data from spreadsheets and a number of other data storages to R
  • Summarise the data with R's built-in summary statistic functions
  • Plot data in interesting ways
  • manipulate data in ways such that they can efficiently analyse data
  • Have a thorough understanding of popular statistical techniques
  • Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in R
  • Be able to fit regression models in R between a response variable
  • Understand how to apply said techniques to their own data using R's common interface to statistical functions
  • Be able to cluster data using standard clustering techniques

Topics Covered

  • Introduction to R: A brief overview of the background and features of the R statistical programming system
  • Data entry: A description of how to import and export data from R
  • Data types: A summary of R's data types
  • R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results
  • R graphics: Creating, editing and storing graphics in R
  • Summary statistics: Measures of location and spread
  • Manipulating data in R: Describing how data can be manipulated in R using logical operators
  • Vector operations: Details of R's vectors operations
  • Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test,teo-sample t-test for paired samples. Wilcoxon signed-rank test
  • ANOVA tables: One-way and two-way tables
  • Simple and multiple linear regression: Including model diagnostics
  • Clustering: Hierarchical clustering, k-means
  • Principle components analysis: Plotting and scaling data

Target Audience

This course is ideally suited to anyone who:

  • Is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using R
  • Has used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as R
  • Is already familiar with basic statistical methods in R and would like to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variables
  • Is familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses

Assumed Knowledge

The course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing. 

Each participant will need to bring their own laptop installed with the R software (which can be downloaded free for Linux, MacOS X or windows from


The course tutor will be one of the following:

Dr Colin Gillespie

Dr Colin Gillespieis a statistics lecturer at Newcastle University and is often employed as an R consultant by Jumping Rivers. He has been using R since 1999 and teaching R programming for the last eight years. Colin has authored a number of R packages and regularly answers R questions and is a top contributor on stackoverflow.

Dr Jamie Owen

Dr Jamie Owen has a PhD in Statistics, with a focus on scalable Bayesian inference. Since leaving academia, he has applied these skills in a number of commercial settings. His focus is using complex data sets, to give meaningful insights. 

Additionally, he has delivered R training courses ranging from introductory to advanced programming and big data analytics at a wide range of companies and academic institutions in both the UK and Europe, including the Francis Crick Institute and the MoD.




Registration before
 20 April 2021


Registration on/after
 20 April 2021


Non Member 

RSS Fellow 

RSS CStat/Gradstat also MIS & FIS