2021/2022
Programming in R
Type:
Optional course (faculty)
Delivered by:
Department of Sociology
When:
2 module
Open to:
students of one campus
Instructors:
Ksenia Tenisheva
Language:
English
ECTS credits:
3
Contact hours:
2
Course Syllabus
Abstract
R is a programming language specifically designed for doing advanced statistical analysis. It is highly popular among data scientists, since it allows to solve essentially all types of tasks that analysts may encounter in real-life industrial or scientific applications: from data preparation and cleaning to estimation of complicated statistical models to presentation of results in an effective and human-friendly way. This course introduces fundamentals of R programming and reviews most widely used base R commands for exploratory data analysis and visualization. You will also learn how to prepare you data for analysis and then explore it using tidyverse and ggplot2 libraries, as well as how to communicate your results using Rmarkdown tools. It is expected that you understand some basic statistical concepts, such as variable, distribution, mean, variance, and correlation, but if not, this should not be a big issue, since you will have an opportunity to learn them when playing around real-world examples of applied data analysis with R and also exercises throughout the course.
Learning Objectives
- The key objective of this class is to help students to master basic skills of using R for data manipulation and exploratory data analysis.
Expected Learning Outcomes
- be able to install R and Rstudio on your PC/laptop
- be able to perform simple and complicated mathematical and logical operations using R
- be able to understand basic principles of programming in R and recognize key R data types and data classes
- be able to import external data sets into R
- be able to clean, recode, transform, subset, and merge your data using base R tools and tidyverse
- be able to perform exploratory data analysis in R: frequencies, shares, means, variances, correlations, etc.
- be able to create effective data visualizations using base R and ggplot2
- be able to summarize outputs of your analysis in tabular forms
- be able to write your own simple R functions, profile and debug your code.
- be able to prepare html and pdf reports on your analyses using Rmarkdown
Course Contents
- Getting started with R
- Data cleaning and data manipulation
- Data visualization
- Tidyverse
- Markdown
- Functions
- Shiny
- Interactive data visualization
- Object-oriented programming
