The purpose of this tutor led virtual 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. 

Dr Rhian Davies

Dr Rhian Davies has a PhD in Statistics specialising in clustering. As a data scientist, she has worked closely with varied domain experts including physicists, psychologists, game designers and engineers.

Rhian has been using R for over ten years and is an active member of R Ladies. She's also a statistical ambassador for the RSS and enjoys helping people understand statistical principles.