Module Details

Module Code: VISU
Module Title: Data Visualisation
Title: Data Visualisation
Module Level:: 8
Credits:: 5
Module Coordinator: Nigel Whyte
Module Author:: Agnes Maciocha
Domains:  
Module Description: The aim of this module is to enable students to gain insight and practical skills for creating interactive web visualisations, Apps and dashboard powered by R. Additionally, students will be familiarised with the current trends and practices in data visualisation.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply and critically evaluate current trends and practices in data visualisation to produce informative, engaging and repeatable interctive web application
LO2 Apply selected and adequate open source methods and tools/ packages to produce interactive web application /graphic for data analysis
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Visualisation as a phase within the data science workflow
Data Science Workflow (Grolemund & Wickham); Visualisation - concepts, definitions, current trends ect.
Introduction to R & RStudio (IDE) environments
RStudio: scripts, workflow, packages: ggplot,plotly, tidyverse (dplyr,readr, purrr,forcats,stringr), plots tab: Graphs export, 3D graphs
The Grammar of Graphics
The layered grammar of graphic by Hadley Wickham; concepts, definitions, components and layers
Producing the basic visualisations
The key packages: ggplot(), plot_ly (), plotly.js(), ggplotly(), functions and arguments
Working with colours
RColorBrewer (Colorbrewer palettes),viridis (viridis color scales), wesanderson (Wes Anderson color palettes), ggsci (scientific journal color palettes); ggplot2 (grey color palettes), R base color palettes: rainbow, heat.colors, cm.colors
3D charts
3D charts: Markers, Paths, Lines, Axes, Surfaes
Publishing views
Saving and embedding HTML; Exporting static images,Editing views for publishing; Combining multiple views, Linking multiple views,
Creating simple dashboard
flexdashboard library; layout, components (htmlwidgets), Sizing, Storyboards,
Key HTML Widgets
rbookeh - an interface to Bookeh a framework for creating web-based plots; Leaflet library to create dynamic maps, dygraphs for charting time-series; Highcharter - rich R interface to the Highcharts JavaScript graphic library, visNetwork - an interface to the network visualisation capabilities of the vis.js library
Creating interactive dashboard
Introducing Shiny package, and shiny components to enable reactivity;Input Sidebar, Shiny Modules
Creating first Shiny app
Basic UI, Basic reactivity, Workfow, Layout, themes, HTML,
Shiny in action
User feedback, Uploads and downloads, Dynamic UI, Bookmarking, Tidy evaluation
Mastering reactivity
why reactivity, The reactive graph, Reactive building blocks, Escaping the graph
Best practices
General guidelines, Functions, Shiny modules, Packages, Testing, Security, Performance
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment100.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Project % of Total Mark 100
Timing Week 11 Learning Outcomes 1,2
Non-marked No
Assessment Description
Students are asked to apply the theory and the practical skills acquired throughout the class as well as explore any other neccessary materials to create interactive visualisation of their choice. Additionally, students will be asked to prepare presentation related to the produced visualisation.
No Project
No Practical
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Decided by module academic in conjunction with programme board. Repeat of coursework and/or written examination or other repeat mechanism as appropriate dependent on students performance and module engagement.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description 12 Weeks per Stage 3.00 36
Independent Learning Non Contact No Description 15 Weeks per Stage 5.93 89
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Carson Sievert. (2020), Interactive Web-based Data Visualization with R, Plotly, and Shiny, Chapman & Hall/CRC: The R Series, p.470, [ISBN: 1138331457].
  • Hadley Wickham. (2020), Mastering Shiny: Build Interactive Apps, Reports, and Dashboards, [ISBN: 1492047384].
  • Garrett Grolemund,Hadley Wickham. (2016), R for Data Science, [ISBN: 1491910399].
  • Thomas Rahlf. (2019), Data Visualisation with R, Springer, p.451, [ISBN: 3030284433].
Recommended Article/Paper Resources
Other Resources
Discussion Note: