SiHui Learns Visual Analytics!

Welcome to SiHui Learns Visual Analytics! This website documents my learning journey for ISSS608 Visual Analytics and Applications course. Hopefully I can surf through this course smoothly like this penguin!

Hands-On Exercises

Hands-on Exercise 1: A Layered Grammar of Graphics: ggplot2 methods

[Updated charts with annotations and references on 16 Jan 2024]

Hands-On Exercise 2: Beyond ggplot2 Fundamentals

In this exercise, we will be introduced to the following ggplot2 extensions to create more elegant and effective statistical graphics:

Hands-On Exercise 3a: Programming Interactive Data Visualisation with R

[Updated with in-class notes on 3 Feb 2024]

Hands-On Exercise 3b: Programming Animated Statistical Graphics with R

In this exercise, we will learn how to create animated data visualisation by using gganimate and plotly r packages. At the same time, we will also learn how to (i) reshape…

Hands-on Exercise 4a - Visualising Distribution

[Updated with In-class notes on 3 Feb 2024 and an interactive raincloud plot!]

Hands-on Exercise 4b - Visual Statistical Analysis

In this hands-on exercise, we will learn how to:

Hands-on Exercise 4C - Visualising Uncertainty

In this hands-on exercise, we will learn how to:

Hands-on Exercise 4d - Funnel Plots for Fair Comparisons

In this exercise, we will learn how to plot the following:

Hands-on Exercise 5a - Creating Ternary Plot with R

In this exercise, we will learn how to build ternary plot programmatically using R for visualising and analysing population structure of Singapore.

Hands-on Exercise 5b - Visual Correlation Analysis

In this exercise, we will learn how to visualise correlation matrix using R. There are 3 main sections of this exercise. First, we will learn how to create a correlation…

Hands-on Exercise 5c: Heatmap for Visualising and Analysing Multivariate Data

In this exercise, we will learn how to use R to plot static and interactive heatmap for visualising and analysing multivariate data.

Hands-on Exercise 5d - Visual Multivariate Analysis with Parallel Coordinates Plot

In this exercise, we will learn how to:

Hands-on Exercise 5e - Treemap Visualisation with R

In this exercise, we will learn how to design treemap using appropriate R packages. The hands-on exercise consists of three main section. First, you will learn how to…

Take-home Exercise 6 - Visualing and Analysing Time-Oriented Data

In this exercise, we will be learning how to create the following visualisations:

Take-home Exercise 7a: Choropleth Mapping with R

In this exercise, we will learn how to plot functional and truthful choropleth maps using tmap package.

Hands-on Exercise 7b: Visualising Geospatial Point Data

In this exercise, we will learn how to: - convert an aspatial data file into simple point feature data frame, and assign it an appropriate projection reference to the newly…

Hands-on Exercise 7c: Analytical Mapping

In this exercise, we will learn how to plot analytical map such as rate map, percentile map and boxmap.

Hands-on Exercise 8: Modelling, Visualising and Analysing Network data with R

[Added date modified field on 16 Mar as I realised it was missing]

Hands-on Exercise 9

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

In-Class Exercises

In-class Exercise 1: Now You See it!

First, let us ensure that the required R packages have been installed and import the relevant data for this hands-on exercise.

In-class Exercise 2: Horizon Plot

First, let us ensure that the required R packages have been installed and import the relevant data for this hands-on exercise.

In-class Exercise 3

Updated this exercise with kriging

In-class_Ex04

First, let us install and launch the relevant packages needed for this exercise using pacman::p_load().

Take-Home Exercises

Take-home Exercise 1

Programme for International Student Assessment (PISA) is a study conducted by the Organisation for Economic Co-operation and Development (OECD) that measures 15-year-olds’…

Take-home Exercise 2

In this take-home exercise, I will be using the data visualisation design principles and best practices learnt in ISSS608 Lesson 1 and 2 to improve on my peer’s…

Take-home Exercise 3: Be Weatherwise or Otherwise

According to Ministry of Sustainability and the Environment, the daily mean temperature is projected to increase by 1.4 to 4.6 Degree Celsius by the end of the century.

Take-home Exercise 4: Prototyping Time Series Module for Shiny Application

In this exercise, we will be preparing the time-series forecasting module of the proposed Shiny Application and complete the following task:

Extras

Additional practices and experiments can be found here!

Post-Lesson Thoughts 1: Annotations

[Updated with references on 16 Jan 2024]

Exploring and Forecasting Time Series using timeTK and Modeltime

Work in progress