Introduction to R & Statistical Modelling in R

Organised by

Royal Statistical Society


Dr Colin Gillespie or Dr Jamie Owen


13/05/2021 - 14/05/2021


12 Errol Street


View in Google Maps  (EC1Y 8LX)



The purpose of this course is to introduce participants to the R environment for statistical computing. Day 1 of the course focuses on entering, working with and visualising data in R. Day 2 focuses on regression modelling in R, including linear and general linear models 

Learning Outcomes

By the end of Day 1, 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

 By the end of Day 2, participants will be able to:

  • 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

Topics covered in Day 1 include: 

  • 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

Topics covered in Day 2 include: 

  • 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


Entry (no or almost no prior knowledge)


£588 - £816 (inc VAT)

Website and registration


Greater London


Qualitative Data Handling and Data Analysis, Python, R environment , Visualising data , Regression Modelling , Linear models

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