Bi8190 Visualization of biological data

Faculty of Science
Spring 2014
Extent and Intensity
0/2. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: z (credit).
Teacher(s)
Mgr. David Zelený, Ph.D. (seminar tutor)
Guaranteed by
Mgr. David Zelený, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: Mgr. David Zelený, Ph.D.
Supplier department: Department of Botany and Zoology – Biology Section – Faculty of Science
Timetable
Fri 8:00–11:50 B09/316
Prerequisites
The class has two main goals: 1) introduce students to basics and modern trends in vizualization of scientific data (how a figure in good scientific publication should look like, what are the possibilities for visualization different data types, which file formats are the most suitable etc.), and 2) teach them how to produce both simple and more advanced graphs in R software package. The class is rather technically oriented and takes place mostly in front of the computer with running R program, combined with theoretical half-hours. Recommended prerequisite is Bi7560 Introduction to R, teaching the basics of R (Vizualization class contains also brief introduction to R program). Vizualization of biological data is complementary to Bi7920 Analysis of biological data and Bi7550 Practical Analysis of Biological Data – Seminar, which are also focused on work in R (but while Bi7920 Analysis of biological data is devoted to analysis of univariate data and Bi7550 Practical Analysis of Biological Data – Seminar to analysis of multivariate data, Vizualization focuses on graphical presentation of data). The class will build on the knowledge learned at Bi5040 Biostatistics - basic course, so student who has already passed this course will be advantaged.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This class has two goals: to teach students the basics of graphical presentation of biologica data (including creation of effective and well-arranged graphs for publication), and to improve their skill in using R program for other than statistical purposes.
Syllabus
  • 1) Basics of R.
  • 2) Basic rules for data visualization - different types of data require different types of graphs. What should not be missing in figures and what is redundant? How to create simple graphs (boxplots, histograms, scatter plots) in R program?
  • 3) Colors - how to combine, how to choose colors for printing and for people who suffer from color vision deficiency, how to mix colors in R and when to use/not to use colors?
  • 4) Image formats - is it better to use jpg, bmp, tiff, pdf or even eps? What are the differences between individual formats and how to create them in R? Which formats fits to which purpose?
  • 5) Advanced methods of data visualization - trellis diagrams and 3D graphics.
  • 6) How to create figures from scratch?
Literature
  • MURRELL, Paul. R graphics. Boca Raton, Fla.: Chapman & Hall/CRC. xix, 301. ISBN 158488486X. 2006. info
  • URL: http://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html
Teaching methods
The class will be held in computer room right in front of the computer - students can directly apply what they learn. During the class there will be 3 homeworks followed by discussion in next class (you need to complete 2 of them as one of the conditions for receiving the credits). Active attending of the class is voluntary, but highly recommended - learning curve of R language is steep and who will miss the train will hardly catch up again. In the end of the class students will individually prepare a small project - graphical visualization of their own or training data. This project will be presented at the last lesson (this is the second condition for receiving the credits).
Assessment methods
The last lesson will be held in form of informal "workshop", where students will shortly present results of their graphical work. Students will gain their credit based on their final presentation and completed homeworks (at least three from four homeworks done).
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught once in two years.
Information on the per-term frequency of the course: v jarním semestru sudých let (2008, 2010, ...).
Teacher's information
http://www.davidzeleny.net/wiki/doku.php/vizualizace:start
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2007, Spring 2008, Spring 2010, Spring 2012, spring 2012 - acreditation, Autumn 2016, Autumn 2018.
  • Enrolment Statistics (Spring 2014, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2014/Bi8190