Exploring Statistics with R – Course Outline

Syllabus

This course consists of 14 sessions in which students learn how to use R as a tool to explore statistics. The sessions are scheduled to last 90 minutes each and consist of presentations and applied parts.

The focus is on fixing problems and learn how to help yourself to understand and illustrate statistical methods by illustrating them in R.

We aim at undergraduates but also have a trick or two for intermediate practitioners. The course intends to supplement basic stats textbook courses by applied illustration but does not explain basic concepts from scratch.

Prerequisites

• A basic stats course maybe be an advantage but is not required.
• Students are asked to use their own notebooks to run the free open source software packages R and R Studio on their own computers during the course. If you cannot install these package or do not have an own notebook please inform us on time before the course.
  

Session 1 : The R Ecosystem

on Rstudio, knitr, Rmarkdown, CRAN,packages , Task Views and more

applied part: read data from different sources, find information, use the help, install packages

Session 2 : Moving Around in R

on indices, vectors, matrices, data.frames, lists, characters, factors, integers and NAs

applied part: learn how explore data types, run basic operations in R, learn how to subset

Session 3: Generating Sample Data

on build-in datasets, distributions, parameters, randomness, seeds and samples

applied part: run tests on sample data, visualize sample data, set up illustrative examples on your own.

Session 4: Functions in R

on helpful functions, custom functions and the apply family

applied part: learn how to automate tasks

Session 5: Anova, linear regressions and t.tests

on the relationship between popular statistical methods

applied part run linear regressions models, t.tests and anova and find out how they relate to each other by applying them.

Session 6: Advanced visualization in R

on ggplot2 and other packages to create more than just scatterplots

applied part learn how to choose meaningful visualizations for your data.

Session 7: Multivariate Methods in R

on factor analysis and principal components in R

applied part Interpret results, use PCA and factor analysis on your own data