• Knowledge of basic introductory statistics concepts (hypothesis testing, p-values, confidence intervals, standard statistical tests, such as the t-test.
• Knowledge of regression analysis
• Students should bring a laptop with R and RStudio installed (see below).
R is an open source environment for statistical computing that is gaining in popularity in many research areas, but especially in Bioinformatics. It is used for advanced statistical modelling, genetic data analysis, annotation, handling of large data sets, advanced and flexible plotting, and much more. This course focuses on R as a language for data handling and programming. No concrete novel statistical methods will be taught. Rather, this course teaches the basic skills needed to apply advanced statistical methods, programmed for R, on your own data.
After this course you will be able to
• Do any basic statistical analysis within R
• Read data into R and export data
• Use R scripts and R markdown to make reproducible analyses
• Make advanced and beautiful graphics using ggplot2
• Use dplyr for manipulating large data files
• Write functions in R to standardize and streamline analyses
• Write loops to handle large scale data analyses
• Explore and use packages with advanced statistical methods
Mode of instruction
Lectures intermixed with practice time.
A closing assignment at the end of the last lecture. The assignment is not graded (pass/no pass only).
In preparation, students should bring a laptop with the following programs installed
• R from www.r-project.org
• RStudio from www.rstudio.org
• R Cookbook. Paul Teetor. 2011. O’Reilly Media. ISBN 978-0596809157