R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.
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.
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
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