R: intro / data cleaning and imputation R / basics of visualisation 2400-DS1R

The main aim of the course is to make students familiar with advanced statistical software which requires coding skills. In the course we will focus on:
i) preparation of an efficient work environment
ii) preparing and tidying different types of data sets
iii) visualization.
Students will obtain the necessary knowledge of the language of the R program to use the R program packages, as well as independently develop applications based on available packages. In addition to IT skills, students will be shown examples of the empirical application of the data analysis program to encourage them to use the program in their own areas of interest.

The topics include:
• R and R-Studio basics – graphical interface/editor, projects, package installation
• Data import
• Basics of commands, types of objects and data structures
• Basic data management – operations on data-frames, handling missing values, factors, combining data, sorting, subsetting
• Basic plots – bar plots, pie charts, histograms, box plots, scatter plots, mosaic plots or correlograms
• R as the analytical solution – descriptive statistics, frequency and contingency tables, correlations, example parametric and nonparametric tests
• Basics of user-written functions, control flow
• RMarkdown report preparation
• Introduction to %>% pipe flow in R – basics of tidyverse
The list may be extended, depending on the initial skills and the interest of the group.