Statistical programming

Statistično programiranje

Course credits:

6.00 ECTS / 60 (30 hours of lectures, 0 hours of exercises, 30 hours of seminars, 0 hours other forms of work)

Course holder:

prof. dr. Aleš Žiberna

Type:

Required

Language:

English

Semester:

First semester

Study degree

2. level

Course execution:

• Postgraduate Programme of Social Informatics

Prerequisits:

Students are allowed to participate in the course provided that they have completed the enrolment procedure. It is assumed that students are familiar with the following topics: Univariate, bivariate and multivariate statistics Any experience with programming is also useful.

Objectives and competences

The goal of the course is to enable students to implement tailor made statistical analysis and prepare templates for automatic report generation. Competences: The ability to use statistical package R, including the ability to write functions and simple statistical programs for data analysis and manipulation The ability to write R scripts for solving specific statistical problems including data analysis and manipulation. The ability to prepare reproducible reports and templates for automatic report generation. The ability to prepare simple statistical web applications (shiny).

Content (Syllabus outline)

R basics: - data types and data structures - data import, export and manipulation in R - introduction to programming in R (loops, conditional execution, writing functions) - overview of univariate and bivariate statistical methods in R Data acquisition and preparation: - text processing - automatics data acquisition Producing reports and web presentations: - overview and basic use of system for reproducible computer supported statistical reports and web presentations - producing simple statistical web applications using Shiny system

Intended learning outcomes:

Knowledge, understanding and practical use of statistical development platform R and reproducible reports generation system. The use of package R for writing shorter statistical programs, that is programs for tailor made data analysis and manipulation Writing R functions.

Learning and teaching methods:

work in online classroom (e-learning): 4 hours (2 lectures and 2 practical work)

Assessment

50 % Homework assignements. 50 % Project work/paper.

Obligatory literature

Additional literature