Module Code - Title:
PS6211
-
DATA PROCESSING AND VISUALISATION WITH R
Year Last Offered:
N/A
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module provides an introduction to working with R for analysing social research data. It assumes no prior knowledge of R, but provides an intensive learning experience in order to achieve fluency in complex data processing tasks and in producing compelling visualisations that are appropriate to the data and research question. The module covers material focusing on the practicalities of coding and data analysis as well as relevant guiding principles of data visualisation and exploratory data analysis.
Syllabus:
- Introduction to R and coding for data analysis
- Data visualisation principles
- Visualising relationships between variables
- Uncertainty and statistical models
- Data processing and file management in R
- Text data visualisation
- Time series data
- Maps and geographical data
- Connections and networks
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
- Explain the core concepts of exploratory data analysis
- Evaluate the value and effectiveness of a wide range of visualisation possibilities
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
- Appreciate the flexibility provided by analysing data using code
- Implement with confidence the appropriate analyses and visualisations for multiple types of data
Psychomotor (Physical Skills)
On successful completion of this module, students will be able to:
- Conduct exploratory data analysis and visualisations with R
- Process data in preparation for further analysis
How the Module will be Taught and what will be the Learning Experiences of the Students:
The module consists of weekly lectures, labs with hands-on training in R, and literature seminars based on student-led presentations and critical discussions of recent examples of applied research. Students will complete multiple individual projects applying the analysis and visualisation techniques. While the modules does promote the development of all UL graduate attributes, agility and innovation stand out as particularly relevant given that students will engage with methods they are likely less familiar with, and in the context of building fluency in a constantly evolving coding environment.
Prime Texts:
Healy, K. (2019)
Data Visualization: A Practical Introduction.
, Princeton University Press
Yau, N. (2013)
Data Points: Visualization That Means Something.
, Wiley
Other Relevant Texts:
Lander, J. (2017)
R for Everyone: Advanced Analytics and Graphics.
, Addison-Wesley Professional
Wickham, H., Cetinkaya-Rundel, M., & Grolemund, G. (2023)
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data.
, O’Reilly Media
Programme(s) in which this Module is Offered:
MASTER OF SCIENCE IN APPLIED SOCIAL DATA ANALYTICS
Semester(s) Module is Offered:
Autumn
Module Leader:
Pablo.DeTezanos-Pint@ul.ie