Module Details
Module Code: |
VISU |
Module Title:
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Data Visualisation
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Title:
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Data Visualisation
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Agnes Maciocha
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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.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Visualisation as a phase within the data science workflow
Data Science Workflow (Grolemund & Wickham); Visualisation - concepts, definitions, current trends ect.
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Introduction to R & RStudio (IDE) environments
RStudio: scripts, workflow, packages: ggplot,plotly, tidyverse (dplyr,readr, purrr,forcats,stringr), plots tab: Graphs export, 3D graphs
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The Grammar of Graphics
The layered grammar of graphic by Hadley Wickham; concepts, definitions, components and layers
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Producing the basic visualisations
The key packages: ggplot(), plot_ly (), plotly.js(), ggplotly(), functions and arguments
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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
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3D charts
3D charts: Markers, Paths, Lines, Axes, Surfaes
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Publishing views
Saving and embedding HTML; Exporting static images,Editing views for publishing; Combining multiple views, Linking multiple views,
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Creating simple dashboard
flexdashboard library; layout, components (htmlwidgets), Sizing, Storyboards,
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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
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Creating interactive dashboard
Introducing Shiny package, and shiny components to enable reactivity;Input Sidebar, Shiny Modules
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Creating first Shiny app
Basic UI, Basic reactivity, Workfow, Layout, themes, HTML,
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Shiny in action
User feedback, Uploads and downloads, Dynamic UI, Bookmarking, Tidy evaluation
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Mastering reactivity
why reactivity, The reactive graph, Reactive building blocks, Escaping the graph
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Best practices
General guidelines, Functions, Shiny modules, Packages, Testing, Security, Performance
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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 100.00% |
AssessmentsFull Time
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.
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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.
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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 |
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Contact |
No Description |
12 Weeks per Stage |
3.00 |
36 |
Independent Learning |
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Non Contact |
No Description |
15 Weeks per Stage |
5.93 |
89 |
Total Weekly Contact Hours |
3.00 |
Module Resources
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Recommended Book Resources |
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Carson Sievert. (2020), Interactive Web-based Data Visualization with R, Plotly, and Shiny, Chapman & Hall/CRC: The R Series, p.470, [ISBN: 1138331457].
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Hadley Wickham. (2020), Mastering Shiny: Build Interactive Apps, Reports, and Dashboards, [ISBN: 1492047384].
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Garrett Grolemund,Hadley Wickham. (2016), R for Data Science, [ISBN: 1491910399].
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Thomas Rahlf. (2019), Data Visualisation with R, Springer, p.451, [ISBN: 3030284433].
| Recommended Article/Paper Resources |
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Hadley Wickham. (2010), A layered grammar of graphics, ournal of Computational and Graphical
Statistics, vol. 19, no. 1, p.3–28,
| Other Resources |
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R Markdown Studio,
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R markdown /Dashboard,
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