Data Visualization with R

Code

PSGY3054

School

Psychology

Level

3

Credits

10

Semesters

Spring Malaysia

Summary

Data sets are typically large and unstructured, and making inferences from these data requires understanding the relationships between different data points. This can be difficult to do simply from looking at a table with numbers, especially if there are many dimensions (e.g., several experimental conditions, possibly in crossed or nested designs). Poor visualization can cause confusion or misunderstanding. It is, therefore, important to develop a comfort level with visualizing data in different ways and understand best practices when representing data. This includes choosing visualizations that are appropriate to the data and to the questions researchers want to answer with the data. The aim of this module is to help students to refine their skills in these domains, such that they are ready to tackle any dataset programmatically and reproducibly. The module is aimed at students with no prior knowledge of R but with some minor programming background. As such, we will cover topics such as R programming, standard statistical techniques in R, data structuring, data wrangling, and data visualization.

Target Students

BSc Psychology / BSc Psychology and Cognitive Neuroscience students with basic programming knowledge at UNM.

Co-requisites

Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:

• Scientific Computing with Matlab (PSGY3047)

Classes

• One 2-hour workshop each week for 10 weeks

This module will consist of a series of 10 weekly computer-lab sessions in which students will work through practical exercises aimed at diving into a topic in these areas. Although the techniques taught in the class could be applied in many other computing languages (e.g., Python, MATLAB), this module will be taught using R with RStudio due to its ease of use. Students will achieve additional training in R programming during interactive lab sessions.

Assessment

• 50% Exercises: Complete a brief set of exercises during class sessions.
• 50% Coursework: Demonstrate data visualization skills by completing a final project.

Assessed by end of spring semester

Educational Aims

Learning Outcomes

1. Intellectual skills

• Understanding the capabilities, strengths and limitations of data analyses and visualization methods.
• An appreciation of different data analyses and visualization techniques.
• The ability to understand complex ideas and relate them to specific situations.

2. Professional/practical skills

• The ability to implement selected data analyses and visualization methods for real world applications.
• The ability to evaluate data analyses and visualization techniques and select those appropriate to a given task.

3. Transferable skills

• The ability to address real problems and assess the value of their proposed solutions.
• The ability to retrieve and analyse information from a variety of sources and produced detailed written reports on the result.

Conveners

• Mr Alfred Chee Keong Lim