Level: Foundation (F)
This virtual course will run over 4 afternoons. 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 as well as regression modelling in R, including linear and general linear models.
Please note: Bookings will close 4 working days before the course start date or when the course has reached its maximum capacity.
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 as well as regression modelling in R, including linear and general linear models.
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
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 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
- 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
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
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 http://www.stats.bris.ac.uk/R/)
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.
No more places available
09 October 2021
09 October 2021
RSS CStat/Gradstat/Data Analyst
also MIS & FIS