From R to Statistics

Usable R fundamentals:

• Introduction and history of R
• IDEs, RStudio, packages and good practices
• Anatomy of a command, standard output/error, batch & interactive mode
• R data structures, basic operators, objects, classes
• Reading data into R objects
• The Tidy data
• Important functions in R for Tidy Data

The grammar of graphics with ggplot & data visualization:

• Introductory rules of data visualisation
• The generic R graphic devices, plot command, colours
• Colours in R
• The grammar of graphics
• The ggplot2 levels and hierarchy
• Different types of geoms for different types of data (histograms, bar plots, pie charts, density plots, scatter plots, box plots)
• Piping summarized data into ggplot
• Special purpose graphics: volcano, heatmap, bubble plot

Fundamentals of descriptive statistics:

• Different kind of data, how to distinguish them
• Basic descriptive indices
• How to evaluate data position, variability and symmetry.
• How to interpret data position, variability and symmetry.
• Correlation and covariance

Bases of statistical inferences:

• Research hypotheses and tests
• Definition of null and alternative hypothesis
• The p-value
• One and two-sided tests
• (optional) Type I and II errors and power of a test
• Evaluating performances: Sensitivity and Precision
• Differences between parametric and non-parametric tests
• Choosing the most fitting test to answer a question
• Differences between independent and paired tests
• How to choose a test