Statistical analyses

  • Descriptive stats
  • Modeling
  • Inferential stats
  • Manage and organize data
  • Large data (large scale / big data / EEG / fMRT)
  • Small data (experimental data / questionnaire data / single-case data)

Present data

  • Visualize data and results
  • Keep a notebook of your research and analyses
  • Write reports, including stats
  • Write presentations
  • Write books
  • Build interactive websites
  • Connect with various other software and internet tools

Schedule

# Topic
1 Installation R, R Studio, and other software
2 Basics of R: Logic and syntax
3 Basics of R: Subsetting
4 Markdown
5 Data manipulation with tidyverse
6 Basic statistics
7 Data visualizazion with ggplot2
8 Regression analyses with R

Installing R

R homepage: https://www.r-project.org/

Installing R-Studio

https://posit.co/download/rstudio-desktop/

Installing R tools (windows only)

  • Windows user have to install additional programs that allow for compiling R packages.
  • This is necessary when you want to install certain extensions to R (packages).
  • You can install it from here: https://cran.r-project.org/bin/windows/Rtools/
  • Choose the version that corresponds to the version of R that you installed previously.
  • Linux or MacOs users have this software already installed.

R for Data science

R for Data Science is a terrific book and completely free!

Before You Start

  • Create a new R file: Click on
  • Save the file: Click on
  • Write your code in this new file.
  • To run the code: Highlight the text you want to execute and click , or use the shortcut Ctrl + Enter (Mac: Command + Enter).
  • Don’t forget to save your file at the end of the session (and occasionally in between)!

Create a separate folder, e.g., r-course, and name your file exercise-1.R.