# Data analysis with program R

2021/2022
Programme:
Financial mathematics, First Cycle
Year:
2 year
Semester:
first
Kind:
mandatory
ECTS:
5
Language:
slovenian
Course director:
Lecturer (contact person):
Hours per week – 1. semester:
Lectures
2
Seminar
0
Tutorial
0
Lab
2
Prerequisites

Completed course Introduction to programming.

Content (Syllabus outline)

Introduction. R as a calculator.
Spreadsheets, units, variables. Measurement scales. Data preparation. NA. Excel, CSV. Reading and storing.
Numerical data. Vectors. Summary. Histogram, boxplot, distribution density.
Ordinal and nominal data. Representation in R. Summary. Bar chart and pie chart.
Data presentation on maps. Colors.
Clustering. Measures of similarity. Agglomerative method and leaders algorithm.
Associations between variables. pairs, QQplot. Least squares method. Regression line.
Non-linear associations. Models. Smoothing and fitting.
Working with texts. Unicode. Regular expressions. Zipf's law.
Data from internet. Reading from web pages. XML. Crawling web pages.
Basic operations on time series.
Visualizations using the ggplot2 library
Basics of Monte Carlo method

M.J. Crawley: The R Book. Wiley, 2007.
J. Maindonald, J. Braun: Data Analysis and Graphics Using R, Cambridge Univ. Press, Cambridge, 2003.
J.M. Chambers: Programming with R Software for Data Analysis. Springer, 2008.
P. Murrell: R Graphics, Chapman &, Hall/CRC, Boca Raton, 2005.
C.P. Robert, G. Casella: Introducing Monte Carlo Methods with R. Springer 2010.
spletna stran http://www.r-project.org

Objectives and competences

Students learn programming language R with the corresponding environment. Using the language they learn basics of statistical data analysis and visualization.

Intended learning outcomes

Knowledge and understanding: Student learns programming package R designed primarily for statistical data analysis and visualization. Student upgrades her/his knowledge of basic programming techniques and learns some special features of language R.
Application: Builiding of user's libraries, preparation od charts, simple data analysis.
Reflection: The importance of modern information technology in analysis of large amounts of data, the importance of visualization in data exploration and presentation of results.
Transferable skills: Working with a computer, algorithmic way of thinking.

Learning and teaching methods

Lectures, exercises, homework, consultations

Assessment

Homeworks, final project
Theoretical exam
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Lecturer's references

Andrej Bauer:
BAUER, Andrej, STONE, Christopher A. RZ: a tool for bringing constructive and computable mathematics closer to programming practice. Journal of logic and computation, ISSN 0955-792X, 2009, vol. 19, no. 1, str. 17-43. [COBISS-SI-ID 15325785]
BAUER, Andrej, TAYLOR, Paul. The Dedekind reals in abstract Stone duality. Mathematical structures in computer science, ISSN 0960-1295, 2009, vol. 19, iss. 4, str. 757-838. [COBISS-SI-ID 15322201]
BAUER, Andrej, BIRKEDAL, Lars. Continuous functionals of dependent types and equilogical spaces. V: CLOTE, Peter G. (ur.). Computer science logic : 14th international workshop, CSL 2000, annual conference of the EACSL, Fischbachau, Germany, August 21-26, 2000 : proceedings, (Lecture notes in computer science, ISSN 0302-9743, 1862). Berlin [etc.]: Springer, 2000, vol. 1862, str. 202-216. [COBISS-SI-ID 10606681]