Introduction
a. R framework and available software to use it, installing packages and solving problems (different OS, missing libraries, R versions, StackOverflow)
b. data types, base R functions
c. saving and loading data, Rdata files, work environment
d. R documentation and CRAN, creating project and its structure
e. DataCamp courses
Introduction II
a. R syntax, cycles, conditions, apply family functions, writing functions, „OOP" in R
b. Git, installing packages from GitHub
c. best practices, defensive programming,
Tidyverse package – ggplot2, dplyr, tidyR, readr, purrr, tibble, stringer, forcats
Statistics in R – correlation, regression, t-test, anova, chí-square, probability and distributions
Data visualization in R (ggplot, plotly, lattice, gganim)
Visualization best practices
R Markdown – slides, HTML pages, pdf files, docx documents, LaTeX, bibTeX and CSS basics
R shiny
Psychometrics in R – lavaan, psych, psychometrics, mirt, mirtCAT
Missing data, types of missing data, consequences to parametric statistics, imputation methods, multiple imputation
Text analysis and text mining – quanteda, word2vec; basic steps (); text statistics and summaries, readability indices, word frequency, similarity
Basics of unsupervised and supervised machine learning
Preparation for the exam - selection of suitable methods of analysis and visualization for different types of data, communication of your findings