Assignment 3

To be completed before 21-3-2021(Thurs) 1200hrs

This repo link will be of the format https://<username>.github.io/HASS-assignment3
In the forked repo create a basic index.html file, and make it visible on GitHub repo pages (it's under the settings tab, under GitHub pages).
You can put your CSS style tags, HTML and JS script code into one file, or organize them into files/directories.

We'll be looking at HDB's Resale flat prices.
Download the CSV. HDB now no longer publishes this as an API. I suspect because this is probably because it was impacting their server costs.
You probably have to do some data manipulation. Whether it is in the CSV or via code or via API query, it's up to you.

The dataset is huge, so frame one simple hypothesis that you are interested in.
E.g. What are transactions in only Ang Mo Kio like in the last 6 months?
E.g. I want to sell my flat. Pick that block and maybe a few other blocks to analyze.

Up to you. Do you want to put everything into one scatterplot chart? Do a simple boxplot?
Feel free to come up with derived columns, and/or filter away data that is not relevant to your hypothesis.
If you are doing summary statistics, D3 has functions like D3.group to make your life easier. Or use an external library like tidyr.
Have a very, very simple writeup (a few lines) to explain your hypothesis and choice.
I'll get all of you to quickly present your hypothesis and chart once the assignment is in.

I'm expecting one chart (if D3), but you are free to do more than one (esp. if not using D3).
You may prefer not to use D3 and that is fine. However, please don't just use Excel and then screenshot an image.
Up to you how much interactivity you want to put into the piece (if D3). Good interactivity takes a ton of work.
We'll be doing small code assignments for this course, so I'm not expecting anything complicated. Save it for the project.

Frame your hypothesis carefully so that you don't have to kill yourself doing this assignment.
With higher order libraries like Plots you can churn out exploratory charts a lot faster.
The point of this assignment is to think about how to approach a bigger dataset (data), frame your hypothesis (task abstraction), then execute it (idiom).

Questions?

Chi-Loong | V/R