Welcome to ISSS608 Visual Analytics and Applications!

Welcome to ISSS 608 Visual Analytics and Applications. In this website, you will find my coursework prepared for this course which is taught by the magnificent Prof. Kam.

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Armed Conflicts in Myanmar - Prototype

The following page will run through the various steps on how to prepare the dataset to conduct Confirmatory Data Analysis (CDA).

Armed Conflicts in Myanmar - Prototype

The following page will run through the various steps on how to prepare the dataset to conduct Confirmatory Data Analysis (CDA).

Hands On Exercise 4.1

Firstly, we need to load the necessary packages below:

Hands On Exercise 6

By the end of this hands-on exercise you will be able create the followings data visualisation by using R packages:

Hands On Exercise 9

By the end of this hands-on exercise, you will be able to:

Hands on Exercise 7.1

Choropleth mapping involves the symbolisation of enumeration units, such as countries, provinces, states, counties or census units, using area patterns or graduated…

Hands on Exercise 7.2

Proportional symbol maps (also known as graduate symbol maps) are a class of maps that use the visual variable of size to represent differences in the magnitude of a…

Hands on Exercise 7.3

In this in-class exercise, you will gain hands-on experience on using appropriate R methods to plot analytical maps.

Hands on Exercise 8

In this hands-on exercise, you will learn how to model, analyse and visualise network data using R.

Hands-on Exercise 1

The code chunk below uses p_load() of pacman package to check if tidyverse packages are installed in the computer. If they are, then they will be launched into R.

Hands-on Exercise 2

In this chapter, we will be using several ggplot2 extensions to create more elegant and effective statistical graphics.

Hands-on Exercise 3.1

In this hands-on exercise, we will be exploring how to create interactive data visualisation by using functions in ggiraph and plotlyr packages.

Hands-on Exercise 3.2

When telling a visually-driven data story, animated graphics tends to…

Hands-on Exercise 4.2

In this hands-on exercise, you will gain hands-on experience on using:

Hands-on Exercise 4.3

Visualising uncertainty is relatively new in…

Hands-on Exercise 4.4

Funnel plot is a specially…

Hands-on Exercise 5.1

Ternary plots are a way of displaying the distribution and variability of three-part compositional data. (For example, the proportion of aged, economy active and young…

Hands-on Exercise 5.2

Correlation coefficient is a popular statistic that use to measure the type and strength of the relationship between two variables. The values of a correlation coefficient…

Hands-on Exercise 5.3

Heatmaps visualise data through variations in colouring. When applied to a tabular format, heatmaps are useful for cross-examining multivariate data, through placing…

Hands-on Exercise 5.4

Parallel coordinates plot is a data visualisation specially designed for visualising and analysing multivariate, numerical data. It is ideal for…

Hands-on Exercise 5.5

In this hands-on exercise, you will gain hands-on experiences on designing treemap using appropriate R packages.…

Hands-on_Exercise 5.2.1

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmark…

In Class Exercise 7

In-Class Exercise 6

A horizon graph is an analytical graphical…

In-class Exercise 1

In this hands-on exercise, two R packages will be used. They are:

Take Home Ex 4.1 - Project Charts

Take Home Exercise 1

OECD education director Andreas Schleicher shared in a BBC article that “Singapore managed to achieve excellence without wide differences between children from wealthy and…

Take Home Exercise 2

In this take-home exercise, we will be selecting an classmates take-home exercise 1 and do the following:

Take Home Exercise 3

In this take home exercise, the requirements are as of below:

Take Home Exercise 4

Prototyping first may help keep you from investing far too much time for marginal gains.