Aim of the course
The course is designed to provide a quick and efficient introduction to R environment and develop participants' practical R programming skills. The course is best suited for:
- Software developers, who want to dive into the data science
- Statisticians and data analytics, who want to get acquainted with R
Prerequisites
- Computer proficiency
- Programming experience (any language) is highly recommended
- Background in statistics is beneficial, but not required
Learning outcomes
- Obtain practical skills of R programming
- Apply core techniques of data processing
- Use R for data visualization
- Run classical models and present the results
Session 1. Basics of R programming
- How it works (introduction to R console and RStudio)
- Coding basics and variable types in R
- Installing third-party packages
- Vectors and lists
- Basic programming structures (conditions, loops, functions)
Session 2. Handling data
- Data frames
- Importing data
- Data pipes (tidyverse library)
- Managing data (arranging, filtering, summarizing)
- Descriptive statistics and grouped summaries
Session 3. R visualization tools
- Basic plots (charts, scatters, histograms)
- ggplot2 library
- Aesthetic mappings
- 2D and 3D plots
- Plotting spatial data
Session 4. Basic data analytics in R
- Model basics
- Model building
- Predictions
- Running and understanding basic models
- Visualizing models and their results
