R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. Learning R has a relatively steep learning curve, completely dependent on self-motivation and interest but that can be very significantly speed up by providing with concrete useful examples and explanations. This course aims to bring anybody up to speed with R basics from zero. However please note a beginners course is not neccesarily an easy course! if you join us you will gain a lot of valuable skills but you'll need to put in quite a bit of work. Are you ready? :D
This course covers:
- Day 1: Foundations of R: get familiar with R, R studio, operators, variables, functions, directories and the script editor
- Day 2: Visualisation in basic R (boxplots, scatterplots, line graphs and histograms) and ggplot2 (same types of plots and also manipulation of data for use within this package as well as simple linear regression)
- Day 3: Introduction to statistical analyses in R: univariate statistics (choosing the right test, checking data assumptions, calculating and extracting the values to report in publications) and Principal Component Analysis (calculation and visualisation with ggplot2)
- Extra materials: Introduction to the Tidyverse, Introduction to for loops
All materials have been built using relevant life sciences/clinical examples. The course has been designed to introduce R from the very basics. Therefore applicants do not need any prior experience to attend, just the desire to learn R. Real life examples with bioinformatic applications are included. All delegates can follow the materials at their own pace with a group of teachers and demonstrators available for 1-1 live support throughout the course. Note that this means lectures are pre-recorded, but teaching team available for live questions. There will also be opportunities to test your knowledge and progress with anonymous live quizzes at the end of each day. Further asynchronous support is provided for 8 weeks post course completion.
Fees are: £300 for all academic delegates; £500 for all delegates from public institutions (not in higher education). Industry delegates will be quoted upon request.
If you are a delegate from the University of Liverpool you can access our student bursaries. Please fill in your application in the link accessible from the registration form. Note the delegate bursaries applications closes well before registration. You must submit both registration and bursary forms simultaneouly if applying for one.
Feedback from previous attendees:
Very practical focused which is great, knowledgeable trainers who are willing to help. Very good curve in the complexity of the content of the course. Handbooks are also explained clearly so I can easily understand each term in R code line. Best programming course I have ever attended, even better than my undergraduate programming modules.
NERC fundeed PhD student, University of Manchester
The exercises are well paced with a gradual learning curve so they build on previous knowledge. There is a nice flow through the workbooks. Exercises at the end are a good level of hard that you have to think and apply what you have learnt without copy/pasting but not too hard that they are intimidating.
PhD student, University of Liverpool
The stats lecture is perfect, told me exactly what I needed to know! (e.g. key statistical terms, which test to use, when, how to check error/normality). No excessive maths included which is good. Always aligned well to the practical sessions.
Senior research staff, University of Liverpool
Fab beginners course! Have signed up to the intermediate and shared amongst the CIMA MRes cohort. Ms Emily Clarke, MRes Student, University of Liverpool
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