PSYb2320 R101: A practical guide to using R as your everyday statistical tool

Faculty of Social Studies
Autumn 2022
Extent and Intensity
1/1/0. 4 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Karel Rečka (lecturer)
Mgr. Hynek Cígler, Ph.D. (lecturer)
doc. Mgr. Stanislav Ježek, Ph.D. (lecturer)
Guaranteed by
doc. Mgr. Stanislav Ježek, Ph.D.
Department of Psychology – Faculty of Social Studies
Contact Person: Mgr. Karel Rečka
Supplier department: Department of Psychology – Faculty of Social Studies
Timetable
Tue 18:00–19:40 U53
Prerequisites
Any introductory statistics course.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 1/20, only registered: 0/20
fields of study / plans the course is directly associated with
there are 18 fields of study the course is directly associated with, display
Course objectives
The course has three main goals. The first is to weaken the dependence on paid statistical software that can be unavailable in many future workplaces of our students. The second is to spring interest in a programming language with vast analytical possibilities and a vital global community. The third goal is to refresh statistical foundations from previous courses students may have taken and expand it.
Learning outcomes
Student knows basic principles of the R language and classes of objects it manipulates. Student can work with data, filter it and transform. Student can perform basic statistical analyses and create graphical representations of data and statistics. Models include linear regression, logistic regression, factor analysis and confirmatory factor analysis.
Syllabus
  • R programming language, working with RStudio; creating and manipulating data objects; data import and cleaning; data description and exploration; data transformation; creating custom functions; iteration to reduce of duplicate code; dealing with missing data; power analysis; selected statistical models (e.g., correlations, linear regressions, hierarchical models); structural models (e.g., EFA, CFA, path analysis); reporting results.
Literature
    required literature
  • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
    recommended literature
  • GROLEMUND, Garrett. Hands-on programming with R : write your own functions and simulations. Edited by Hadley Wickham. 1st edition. Sebastopol: O'Reilly Media, 2014, xiii, 232. ISBN 9781449359010. info
  • CHANG, Winston. R graphics cookbook : practical recipes for visualizing data. Second edition. Beijing: O'Reilly, 2019, xiii, 425. ISBN 9781491978603. info
  • XIE, Yihui, J. J. ALLAIRE and Garrett GROLEMUND. R Markdown : the definitive guide. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2019, xxxiv, 303. ISBN 9781138359420. info
  • BUUREN, S. van. Flexible imputation of missing data. Boca Raton, FL: CRC Press, 2012, xxv, 316. ISBN 9781439868249. info
  • LITTLE, Todd D. Longitudinal structural equation modeling. Edited by Noel A. Card. London: Guilford Press, 2013, xxii, 386. ISBN 9781462510160. info
Teaching methods
lecture, seminar, online exercises, and discussion
Assessment methods
Credit awarded for individually evaluated online DataCamp exercises and presentation of an R package using R Markdown.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
Teacher's information
https://www.datacamp.com/
This class is supported by DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 350+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 6 million learners around the world and close your skills gap.
The course is also listed under the following terms Autumn 2019, Autumn 2021, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2022, recent)
  • Permalink: https://is.muni.cz/course/fss/autumn2022/PSYb2320