• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Introduction to R

2019/2020
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Bridging course
When:
1 year, 1 module

Instructor

Course Syllabus

Abstract

The course “Introduction to R” is designed to provide students with basic knowledge of in R, free software environment for statistical computing and graphics. The course begins with an introduction to basics of R programming language, data types and importing dataset in different formats. Then students will learn how to explore, clean and prepare data for further analysis. The final part of the course is devoted to techniques of data visualization using R. The course is supported by online platform for education DataCamp (www.datacamp.com). Students are expected to watch online lectures and complete assignments using the platform. Some lectures and final examination are provided by lecturers of National Research University Higher School of Economics.
Learning Objectives

Learning Objectives

  • Know basic syntax of R programming language.
  • Import data, explore and clear it.
  • Have skills of data manipulation and visualization.
Expected Learning Outcomes

Expected Learning Outcomes

  • Know basic data types and R syntax.
  • Is able to explore dataset.
  • Have skills of data cleaning.
  • Know types of data joining.
  • Is able to transform datasets.
  • Have skills of data visualization.
Course Contents

Course Contents

  • Introduction in R
    1. Basics of R, data types and importing. 2. Exploring and cleaning data.
  • Data manipulation and visualization
    3. Data manipulation and joining with dplyr and intermediate operations in R. 4. Visualization with package ggplot2.
Assessment Elements

Assessment Elements

  • non-blocking Self-study work
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.4 * Exam + 0.6 * Self-study work
Bibliography

Bibliography

Recommended Core Bibliography

  • Boehmke, B. C. (2016). Data Wrangling with R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1331500
  • Rahlf, T. (2017). Data Visualisation with R : 100 Examples. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1377904

Recommended Additional Bibliography

  • Spector, P. (2008). Data Manipulation with R. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=229058