Take-home Exercise 1

Creating data visualisation beyond default

Published

April 6, 2023

Creating data visualisation beyond default

The Task

Age-sex pyramid is an analytical visualisation commonly used by demographers to reveal the structure of population by gender and age group. In this take-home exercise, you are required to reveal the demographic structure of Singapore at planning area level by using age-sex pyramid method. Instead of plotting a single age-sex pyramid, however, you are required to display nine selected planning areas on a single view by using trellis display (also know as small-multiple plot).

The Designing Tool

For the purpose of this take-home exercise, Tableau desktop should be used to design the analytical visualisation.

The Data

Singapore Residents by Planning Area / Subzone, Age Group, Sex and Type of Dwelling, June 2022 should be used to prepare the analytical visualisation. It is available at Department of Statistics, Singapore(in short SingStat).

The Write-up

The write-up of the take-home exercise should include but not limited to the followings:

  • A reproducible description of the procedures used to prepare the analytical visualisation. Please refer to the senior submission I shared for example 1 and 2.

  • A write-up of not more than 500 words to discuss the patterns reveal by the analytical visualisation prepared.

Submission Instructions

This is an individual assignment. You are required to work on the take-home exercises and prepare submission individually.

The specific submission instructions are as follows:

  • The analytical visualisation must be prepared by using Tableau Desktop. The final workbook must be uploaded onto Tableau Public.
  • The write-up of the take-home exercise must be in Quarto html document format. You are required to publish the write-up on Netlify.
  • Provide the links to the Take-home Exercise write-up, github repository and Tableau Public onto eLearn (i.e. Take-home Exercise section)

Submission date

Your completed take-home exercise is due on 22nd January 2023, by 11:59pm evening.

Peer Learning