R is a free software programming language and software environment for statistical computing and graphics. R is a powerful business tool for developing statistical software and data analysis.
Why choose this course?
Complete this intensive four-day course and you will progress from an introduction to R through to advanced use cases. The course will give you an understanding of R's basic concepts and an insight into the R environment. You will explore R graphics in depth and acquire skills and knowledge for visualising data and producing professional graphics in R. Once you are familiar with these basic concepts and components, you will begin to customise R's core analytical capabilities and perform advanced statistical analyses.
If you wish, you may choose to attend any of the single-day workshops that make up the intensive course, or any combination of workshops, according to your experience and preference. Access to computers is provided throughout the course, but you may also wish to bring your own laptop so that you can use the software in a familiar context.
Who will teach me?
The course is led by Dr Golnaz Shahtahmassebi, a lecturer in statistics and an applied statistician. She uses R extensively in her research in statistical and computational methods related to life sciences including medicine, sport and physics.
Dr Ben Dickins lectures in the School of Science and Technology with teaching responsibilities in molecular biology, biochemistry and evolution and also uses R extensively in his research.
Get in touch
If you have any questions about this course, please email us.
What you'll study
Day One: Start using R for statistical analysis
On day one you will explore the R environment and acquire basic skills in R, including data objects, working with data and generating standard graphics. You will discover how to produce summary statistics, carry out analysis of variance or regression analysis and calculate sample size and the power of a statistical test.
Day one topics
- R's basic concepts
- components including the R environment
- data objects
- working with data
- generating standard graphics.
Day Two: Explore R graphics in depth
By the end of day two you will have acquired key skills and knowledge for visualising data and producing professional graphics. During this workshop you will be introduced to the ggplot2 package which facilitates custom designs, and you will explore the advanced capabilities associated with the lattice and googleVis packages.
Day two topics
- R's core graphical capabilities
- Customising R base graphics
- ggplot2 graphics modelling
- Lattice and googleVis packages.
Day three: Advanced R skills
Having mastered the basics, we will start to develop skills in customising R's core analytical capabilities for your own purposes. Topics include: looping, conditional expressions and writing your own functions. You will learn how to perform advanced statistical analyses such as logistic regression, survival analysis and Bayesian analyses in R using packages such as MCMCpack and coda.
Day three topics
- Looping, conditional expressions
- Write your own functions
- Advanced statistical analysis, including logistic regression, survival analysis and Bayesian analysis.
Day four: Visualising Genomics Data with R
This workshop will suit those wishing to visualise sequencing or related kinds of data. An awareness of typical data formats associated with Next Generation Sequencing (NGS) projects is desirable. By the end of day four you will be able to integrate R into your NGS workflow. During this session we will also introduce you to some helpful packages for handling sequence data and introduce you to the bioconductor repository.
Day four topics
- Visualise and summarise data generated by Next Generation Sequencing (NGS) projects
- Sequence data handling
- An introduction to bioconductor repository.
If you wish, you may choose to attend any of the single day workshops that make up this four-day course, or any combination of workshops, according to your experience and preference.
Careers and employability
There is currently no careers information available for this course.This course does not currently offer placements.
There are no formal entry requirements for this course. You do not need any prior knowledge of R or programming, but a basic understanding of statistics would be helpful.
If you prefer, you can choose to attend any of the single-day workshops that make up the intensive course, or any combination of workshops according to your experience and preference.
How to apply
Applications for this course will be opening soon. To register your interest, please email us.
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