Delivered at:: Department of Innovation and Business in Information Technologies

Course type:: Elective course

When:: 1 year, 2-4 module

Abstract

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. This is blended course.

Learning Objectives

To equip students with knowledge how to program in R and how to use R for effective data analysis.

Expected Learning Outcomes

Install and configure the software required for the statistical programming environment
Configure statistical programming software
Understanding how to use R for efficient data analysis;
Understand critical programming language concepts
Should make use of R loop functions and debugging tools
Collect detailed information using R profiler

Course Contents

Background, Getting Started, and Nuts & Bolts
This unit covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story
Programming with R
Welcome to Unit 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
Loop Functions and Debugging
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice
Simulation & Profiling
This unit covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R III

Assessment Elements

Экзамен в формате теста
It is organized online via Coursera platform (final test for the course) or via LMS systems with questions related to the content of the course. Students get information about online platform not later than 3 days before exam.
Online course attendance

Interim Assessment

Interim assessment (4 module)
0.4 * Online course attendance + 0.6 * Экзамен в формате теста

Bibliography

Recommended Core Bibliography

Medeiros, K. (2018). R Programming Fundamentals : Deal with Data Using Various Modeling Techniques. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1904978

Recommended Additional Bibliography

Ren, K. (2016). Learning R Programming. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1409189
Trejo, O., & C. Figliozzi, P. (2017). R Programming By Example : Practical, Hands-on Projects to Help You Get Started with R. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1682395

Master’s Programme 'Electronic Business and Digital Innovations'

Contacts

Programming in R

Course Syllabus