PSY30460 Data Visualization - creating graphs in R - an introduction to programming

Academic Year 2019/2020

Data visualization is the graphical representation of information and data. By using visual elements like graphs and charts data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
In this module students will be introduced to the R environment before moving on to practical based workshops that focus on a variety of core visualization approaches using R, an open source programming language. No previous experience with programming is required.
This course introduces students to data ‘from the ground’ up by giving a series of practical workshops on how to communicate research findings through the visualization package ggplot in R. Here, students will learn, in a hands on environment, how to visualize data sets in R.

Workshops take place on Feb 3rd, Feb 10th, Feb 24th and March 2nd
9 am -12

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Curricular information is subject to change

Learning Outcomes:

Students will learn, in a hands on environment, how to visualize data sets in R.
Learn to produce meaningful and beautiful data visualizations
Visualization best practice
Students will acquire the skills to select and create the appropriate visual element given the type of data and the information that needs to be conveyed.
Students will create an online portfolio of the coding scripts and associated graphs they have created.

Indicative Module Content:

This module involves four, three hour workshops.
Students are required to arrive at the first workshop with R and R studio downloaded onto their laptops.
Content includes -
Introduction to R & ggplot2
Data frame manipulation
Scatterplots, Bar charts, histograms, line graphs, density plots, violin plots.

Student Effort Hours: 
Student Effort Type Hours
Seminar (or Webinar)

12
Specified Learning Activities

30
Autonomous Student Learning

83
Total

125
Approaches to Teaching and Learning:
Active-task based learning.
 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Pre-requisite:
PSY30350 - Research Methods & Stats III


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Attendance: Students participation at each workshop contributes 30% to the final grade.
 Throughout the Trimester n/a Standard conversion grade scale 40% No

30
Continuous Assessment: Completion of online take home coding tasks Varies over the Trimester n/a Standard conversion grade scale 40% Yes

30
Portfolio: Each student will create an online portfolio of the plots, graphs and code from the workshops.
Submission of this portfolio accounts for 40% of grade.		 Varies over the Trimester n/a Standard conversion grade scale 40% Yes

40

Carry forward of passed components
No
 
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Online automated feedback

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