Jacek Matulewski
R language course

Lecture for cognitive science students

Installation:
R: https://cran.r-project.org/bin/windows/base/
RStudio: https://rstudio.com/products/rstudio/download/#download
Creating a notebook in RStudio for the first time (including the installation of necessary packages)

Schedule:

1. The basics
   • Videos (English subtitles)
     • Data types and variables
     • Vectors
     • Defining functions
     • Operators, part 1
     • Operators, part 2
     • Common functions
     • Statistical parameters
     • Lists
     • Control statements
     • More on defining functions
     • Attributes
   • Pliki
     • Notebook
2. Multidimensional data types
   • Videos (English subtitles)
     • Matrices and arrays
     • Matrix operations
     • Factors
     • Data frames
     • CSV files
     • Data processing example
     • R script
     • Files
   • Pliki
     • Notebook
     • File 1.csv used in video Data processing example
   • Further readings
     • Loading data from John Hopkins University on the COVID-19 epidemic
3. Essential functions for statistics in R
   Disclaimer: I assume students are familiar with the basics of statistics and its concepts.
   The videos below do not explain it but only show how to calculate the basic statistical
   parameters using R and its packages.
   • Videos (English subtitles)
     • Visual data analysis
     • Linear regression
     • Ascombe's quarter
     • Correlation and other statistical parameters of data sets
     • Statistical hypothesis testing
     • Supplement: Effect size
     • Example: analysis of data from an experiment in the TGPP paradigm
     • Analysis of variance (ANOVA) and its non-parametric counterpart (new)
     • Further examples (see Exercises on real data below)
     • Additional material: Benford's Law (no english subtitles)
   • Files
     • Notebook
     • Data files for an example of data analysis from an experiment in the TGPP schema
     • Notebook for the TGPP example
     • Statistical hypothesis testing: slide and poster
   • Further readings
     • Chi-squared test
     • Materials from the book Discovering Statistics Using R
     • MAGIC criteria
4. Packages
   • Videos (English subtitles)
     • Installing and loading packages
     • Package dplyr - filtering, sorting and data processing
     • Package tidyr - a wide and long data formats
     • Package ggplot2 - charts
     • Package stringi - string managing
     • Package caret - see machine learning
     • Package shiny - creating interactive web applications:
       • part 1 - shiny application structure
       • part 2 - input parameters control widgets
       • part 3 - storing application state
       • part 4 - files transfer; tabs and grid layout
   • Files
     • Notebook
   • Further readings
     • Gallery of charts and diagrams with code examples
     • Package maps - data visualisation using maps: tutorial
     • Maps using ggplot2 - tutorial: the basics, layers, layout
     • Package knitr - preparing documentation: tutorial, tutorial
     • More about the shiny package
5. Machine learning
   • Videos (English subtitles)
     • Machine learning I - caret package
     • Machine learning II - Supervised learning - classification
     • Machine learning III - Unsupervised learning - Clustering
   • Files
     • Notebook
     • Calculating kappa
6. Creating and publishing packages to CRAN
   • Making your first R package (Fong Chun Chan's Blog)
   • Guide from R-bloggers
   • R Studio help pages
7. Exercises
8. Exercises on real data
   • SUS questionnaire
     
     • Step-by-step description
     • Data (scans of questionnaires filled-in)
     • Notebook
   • A set of exercises using data from the anxiety survey during the first wave of the COVID-19 epidemic (prepared by Wojciech Sobczuk)
     • Exercises
     • Solutions (don't look if you don't have to)

YouTube playlist

This class is supported by DataCamp

Personal homepage
Grupa (6 miesięcy)

Online materials:

1. Oscar Baruffa Big Book of R
2. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton Modern Data Science with R (2nd edition)
3. Brian S. Everitt, Torsten Hothorn A handbook of statistical analyses using R, z-lib.org (e-book)