Introduction to R programming language

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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

[FER3-HR] Computing - study

Skills (3. semester) (5. semester)

[FER3-HR] Electrical Engineering and Information Technology - study

Skills (3. semester) (5. semester)

[FER2-HR] Computer Engineering - module

Skills (5. semester)

[FER2-HR] Computer Science - module

Skills (5. semester)

[FER2-HR] Computing - study

Skills (3. semester)

[FER2-HR] Control Engineering and Automation - module

Skills (5. semester)

[FER2-HR] Electrical Engineering and Information Technology - study

Skills (3. semester)

[FER2-HR] Electrical Power Engineering - module

Skills (5. semester)

[FER2-HR] Electronic and Computer Engineering - module

Skills (5. semester)

[FER2-HR] Electronics - module

Skills (5. semester)

[FER2-HR] Information Processing - module

Skills (5. semester)

[FER2-HR] Software Engineering and Information Systems - module

Skills (5. semester)

[FER2-HR] Telecommunication and Informatics - module

Skills (5. semester)

[FER2-HR] Wireless Technologies - module

Skills (5. semester)

University graduate

[FER3-HR] Computing - study

Skills (1. semester)

[FER3-HR] Electrical Engineering and Information Technology - study

Skills (1. semester)

[FER3-HR] Information and Communication Technology - study

Skills (1. semester)

[FER2-HR] Computer Engineering - profile

Skills (1. semester)

[FER2-HR] Computer Science - profile

Skills (1. semester)

[FER2-HR] Control Engineering and Automation - profile

Skills (1. semester)

[FER2-HR] Electrical Engineering Systems and Technologies - profile

Skills (1. semester)

[FER2-HR] Electrical Power Engineering - profile

Skills (1. semester)

[FER2-HR] Electronic and Computer Engineering - profile

Skills (1. semester)

[FER2-HR] Electronics - profile

Skills (1. semester)

[FER2-HR] Information Processing - profile

Skills (1. semester)

[FER2-HR] Software Engineering and Information Systems - profile

Skills (1. semester)

[FER2-HR] Telecommunication and Informatics - profile

Skills (1. semester)

[FER2-HR] 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