Course Resources

ISSS608 Visual Analytics and Applications

About

Weekly Session

DataViz Makeover

Assignment

Visual Analytics Project

Resources

Data Visualisation Desinger

In this course, students will be exposed to and gain hands-on experience on several generic visual analytics toolkit and specialised data visualisation applications. Below are a list of the core software tools for this course.

Desktop Data Visualisation Designer

Tableau

• Tableau home page [1]
• Training and Tutorials [2]
• Visual Gallery [3]
• Blogs that inspired
  • The Information Lab [4]
  • DataRemixed [5]

JMP Pro

• JMP home page [6]
• Discovering JMP [7]
• JMP Learning Library [8]
• JMP® for Students 1: Navigation and Use [9]

QlikView and/or Qlik Sense (Optional)

• Qlik home page [10]
• QlikView home page [11]
• Qlik Sense home page [12]

Power BI (Optional)

• Power BI homepage [13]
• Guided Learning [14]
• Power BI Documentation [15]

Online Data Visualisation Designer

Flourish

• Features
• Examples
• Blog

Specialised Data Visualisation Tools

Interactive Exploratory Data Analysis

• Mondrian [16]
• GGobi [17]

High-dimensional Data Visualisation

• Treemaps [18]
• Hierarchical Clustering Explorer [19]

Time-series Data Visualisation

• Time Searcher [20]

Graph Visualisation

Gephi

• Gephi [21]
• Webpage [22]
• Download [23]
• Tutorial: Quick Start [24], Visualization [25], and Layouts [26]
• Forum [27]
• Gephi Tutorial
• GEPHI – Introduction to Network Analysis and Visualization

Cytoscape

• Cytoscape [28]

Getting Started with R

• R for Data Science by Garrett Grolemund and Hadley Wickham.
• Modern R with the tidyverse by Bruno Rodrigues. Chapter 2 provides a detail discussion on R data objects.

R Packages for Data Visualisation

ggplot2

ggplot2 Core

• ggplot2 [29]
• ggplot2 – The R graph Gallery [30]
• Introduction to R Graphics with ggplot2 [31]
• ggplot2 - A Short Tutorial [32]
• ggplot2 介紹

ggplots Extension

• ggVis
• ggmap
• ggtern, an extension to ggplot2 specifically for the plotting of ternary diagrams [33]
• ggExtra, a collection of functions and layers to enhance ggplot2. The main function is ggMarginal, which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots. [34]
• ggthemes, some extra themes, geoms, and scales for 'ggplot2'. Provides 'ggplot2' themes and scales that replicate the look of plots by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata', 'Excel', and 'The Wall Street Journal', among others. Provides 'geoms' for Tufte's box plot and range frame. [35]
• ggigraph lets R users to make ggplot interactive. [36]
• GGally extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks. [37]
• sjPlot-package, Data Visualization for Statistics in Social Science [38]
• ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich plots.

Interactive Data Visualisation with R

plotly R

• plotly: Create Interactive Web Graphics via 'plotly.js'
• Interactive web-based data visualization with R, plotly, and shiny
• Plotly R Open Source Graphing Library
• Getting Started with Plotly and ggplot2

Other R graphics packages

• corrplot [39]. A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.
• corrgram [40] calculates correlation of variables and displays the results graphically. Included panel functions can display points, shading, ellipses, and correlation values with confidence intervals. [41]
• vcd, Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. [42]
• tmap [43] offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.

Web-based Visual Analytics Development tool in R

Getting Started

• Hadley Wickham (2020) Mastering Shiny. Everything you need to know about Shiny can be found here. It is not an easy to read book but worth investing time and effort to read.
• Shiny from R Studio
• Learn Shiny
• Function reference
• The Shiny Cheat sheet
• Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard (2020) Engineering Production-Grade Shiny Apps
• Shiny 入門

Shiny Applications

• Gallery
• Shiny Contest Winners 2019 - Full List
• Fifteen New Zealand government Shiny web apps
• ordpress.com/2018/10/15/introducing-the-new-zealand-trade-intelligence-dashboard/ Introducing the New Zealand Trade Intelligence Dashboard

github

• Happy Git and GitHub for the useR. Highly recommended to beginners.
• GitHub and RStudio
• Getting starting with git and GitHub using RStudio
• Using Git within RStudio
• github doc
• Transform a folder as git project synchronized on Github or Gitlab

R Markdown

• R Markdown: The Definitive Guide. Highly recommended to beginners.
• R Markdown from R Studio
• R Markdown Cookbook
• RMarkdown for Scientists
• Ten awesome R Markdown tricks

blogdown

• blogdown: Creating Websites with R Markdown. Highly recommended to beginners.
• blogdown
• 3.1 Netlify
• How to build a website with Blogdown in R
• Setting up our blog with RStudio and blogdown I: Creating the blog
• Setting up your blog with RStudio and blogdown II: Workflow
• Setting up your blog with RStudio and blogdown III: modify your theme