This assignment attempts to solve the 2021 IEEE Visual Analytics Science and Technology (VAST) Challenge: Mini-Challenge 2 by applying different visual analytics concepts, methods, and techniques with relevant R data visualisation and data analysis packages.
This assignment attempts to solve the 2021 IEEE Visual Analytics Science and Technology (VAST) Challenge: Mini-Challenge 2 by applying different visual analytics concepts, methods, and techniques with relevant R data visualisation and data analysis packages.
This assignment attempts to solve the 2021 IEEE Visual Analytics Science and Technology (VAST) Challenge: Mini-Challenge 2 by applying different visual analytics concepts, methods, and techniques with relevant R data visualisation and data analysis packages.
This assignment attempts to solve the 2021 IEEE Visual Analytics Science and Technology (VAST) Challenge: Mini-Challenge 2 by applying different visual analytics concepts, methods, and techniques with relevant R data visualisation and data analysis packages.
This assignment attempts to solve the 2021 IEEE Visual Analytics Science and Technology (VAST) Challenge: Mini-Challenge 2 by applying different visual analytics concepts, methods, and techniques with relevant R data visualisation and data analysis packages.
This in-class exercise introduces Shiny as an example of web-enabled visual analytics application.
This in-class exercise explores the concepts and methods of Text Visualisation. It visualises and analyses the text data from a collection of 20 newsgroups. It introduces the tidytext framework for processing, wrangling, analysing and visualising text data using tidytext, tidyverse, widyr, wordcloud, ggwordcloud, textplot, DT, lubridate, and hms packages.
This in-class exercise explores Network Data Visualisation and Analysis. It visualises the GASTech emails from VAST Challenge 2021: [Mini-Challenge 1](https://vast-challenge.github.io/2021/MC1.html) using dplyr, lubridate, tidygraph, ggraph, and VisNetwork packages. Additionally, the cc_data.csv from VAST Challenge 2021: [Mini-Challenge 2](https://vast-challenge.github.io/2021/MC2.html) was used to create Bipartitle Network.
This in-class exercise explores Analysis of Spatial Data. Specifically, it visualises the movement data from VAST Challenge 2021: [Mini-Challenge 2](https://vast-challenge.github.io/2021/MC2.html) using sf, raster, readr, clock and tmap packages.
This in-class exercise explores programming data visualisation in R. It introduces the "Grammar of Graphics", ggplot2 for static graphics, ggiraph and plotly for interactive graphics, and tidyverse for the data science workflow.
The task for this makeover is to design and implement **interactive visualisation** for the infographics by Department of Statistics, Singapore (DOS). The details can be found [here](https://isss608.netlify.app/dataviz/dataviz2). The proposed data visualisation is available on Tableau Public through this [link](https://public.tableau.com/app/profile/adolit/viz/DataViz_Makeover02/SGTradeDashboard).
The task for this makeover is to design and implement **static visualisation** for the Merchandise Trade by Region/Market data provided by Department of Statistics, Singapore (DOS). The details can be found [here](https://isss608.netlify.app/dataviz/dataviz1). The proposed data visualisation is available on Tableau Public through this [link](https://public.tableau.com/app/profile/adolit/viz/DataViz_Makeover01/Dashboard).