Introduction to R programming language

Course Description

The goal of this course is primarily enabling students to efficiently use the R programming language with the emphasis on problem solving and practical application. The R programming language is tailored specifically for exploratory, statistical and data mining analysis of data sets. By its nature, it has a lot in common with classical programming languages such as Python, Java or C ++ but also with statistical tools such as SAS or SPSS. With its interactive approach, but also the ability to write complex programming scripts, R has established itself as one of the leading statistical programming languages which together with the accompanying packages offers a very efficient way to perform complex analysis of data sets and create reports accompanied by complex visualizations and calculations. Learning R requires a specific combination of programming skills, knowledge of elementary statistics, but also a certain level of creativity and readiness for challenges.

Learning Outcomes

analyze small and large data sets in a meaningful and organized manner
identify the nature of the data and the nature of its processing
use the interactive programming approach to data analysis
modify the raw data into a form suitable for analysis
prepare complex scripts and software packages in the programming language R
apply machine learning methods in the programming environment
apply the methodology of preparing reports

Forms of Teaching

Lectures

Combination of slide presentations and interactive demonstrations of examples in R language with the option of executing examples on personal computers

Laboratory Work

Writing scripts in R language , programmatic data analysis, applying machine learning methods through the R language, writing reports.

Consultations

Consultations in a predefined weekday time slot or via e-mail correspondence.

Programming Exercises

Dataset analysis and reporting

E-learning

Solving electronic workbook in RMD format using RStudio tool

Grading Method

	Continuous Assessment		Exam
Type	Threshold	Percent of Grade	Threshold	Percent of Grade
Laboratory Exercises	10 %	20 %	0 %	0 %
Homeworks	5 %	10 %	0 %	0 %
Class participation	0 %	5 %	0 %	0 %
Seminar/Project	5 %	20 %	0 %	0 %
Attendance	0 %	5 %	0 %	0 %
Mid Term Exam: Written	5 %	15 %	0 %
Final Exam: Written	10 %	25 %

Week by Week Schedule

Introduction to R; comparison between R and other programming languages; overview of programming tools and IDEs; presentation of the course execution plan
Basic data types and operators; complex data structures: vectores, matrices, data frames and lists;
Introduction to factors; flow control mechanisms; OOP principles in the R programming language
Working with packages, basic functions and environments
User defined functions; the apply family of functions
Pipeline operator; basic principles of tidy data; working with dates and strings
Data munging; introducing the dplyr package
1. midexam
1. midexam
Exploratory analysis and data visualization: grammar of graphics; introducing the ggplot2 package
Exploratory analysis and data visualization: advanced visualization methods; reporting (R Markdown)
Statistical programming; working with distributions; simulations
Descriptive and inferential stastistic in R
Machine learning in R - practical examples: regression (simple linear regression, multiple linear regression, variable selection)
Machine learning in R - practical examples: classification (logistic regression, kNN classification)

Study Programmes

University undergraduate

Computer Engineering (module)

Skills (5. semester)

Computer Science (module)

Skills (5. semester)

Information Processing (module)

Skills (5. semester)

Software Engineering and Information Systems (module)

Skills (5. semester)

Telecommunication and Informatics (module)

Skills (5. semester)

University graduate

Computer Engineering (profile)

Skills (1. semester)

Computer Science (profile)

Skills (1. semester)

Control Engineering and Automation (profile)

Skills (1. semester)

Electrical Engineering Systems and Technologies (profile)

Skills (1. semester)

Electrical Power Engineering (profile)

Skills (1. semester)

Electronic and Computer Engineering (profile)

Skills (1. semester)

Electronics (profile)

Skills (1. semester)

Information Processing (profile)

Skills (1. semester)

Software Engineering and Information Systems (profile)

Skills (1. semester)

Telecommunication and Informatics (profile)

Skills (1. semester)

Wireless Technologies (profile)

Skills (1. semester)

Literature

Hadley Wickham, Garrett Grolemund (2016.), R for Data Science, "O'Reilly Media, Inc."

Jared Lander (2013.), R for Everyone, Addison-Wesley Professional

David Diez, Christopher Barr, Mine Çetinkaya-Rundel (2015.), OpenIntro Statistics,

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013.), An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics), Springer

Jiawei Han, Jian Pei, Micheline Kamber (2011.), Data Mining: Concepts and Techniques, Elsevier