Tallest Buildings Dataset Visualization¶

Let us pull a dataset of tallest buildings.

ChatGPT Prompt

Create a table of tallest buildings in the world with 
various attributes like height, year built, location, and others. 
Draw the table in markdown enclosing in "```".

Generates this table with extra attributes including the ones specified in the prompt.

ChatGPT Completion

Rank	Building Name	Height (m)	Height (ft)	Year Completed	City	Country	Primary Use
1	Burj Khalifa	828	2,716	2010	Dubai	United Arab Emirates	Mixed Use
2	Shanghai Tower	632	2,073	2015	Shanghai	China	Office
3	Abraj Al Bait Clock Tower	601	1,971	2012	Mecca	Saudi Arabia	Mixed Use
4	Ping An Finance Center	599	1,965	2017	Shenzhen	China	Office
5	Lotte World Tower	555	1,819	2017	Seoul	South Korea	Mixed Use
6	One World Trade Center	541	1,776	2014	New York City	United States	Office
7	Guangzhou CTF Finance Centre	530	1,739	2016	Guangzhou	China	Hotel/Residential/Office
8	Tianjin CTF Finance Centre	530	1,739	2019	Tianjin	China	Hotel/Office
9	CITIC Tower	528	1,732	2018	Beijing	China	Office
10	TAIPEI 101	508	1,667	2004	Taipei	Taiwan	Office

Now we define a prompt for visualizing the table using vegalite charts.

ChatGPT Prompt

Visualize this table choosing appropriate vegalite chart 
and generate the code for the visualization.

The generated code handles mixed data types intelligently and is able to identify appropriate chart design based on given attributes and types.

{ "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "data": { "values": [ {"Building Name": "Burj Khalifa", "Height (m)": 828, "Height (ft)": 2716, "Year Completed": 2010, "City": "Dubai", "Country": "United Arab Emirates", "Primary Use": "Mixed Use"}, {"Building Name": "Shanghai Tower", "Height (m)": 632, "Height (ft)": 2073, "Year Completed": 2015, "City": "Shanghai", "Country": "China", "Primary Use": "Office"}, {"Building Name": "Abraj Al Bait Clock Tower", "Height (m)": 601, "Height (ft)": 1971, "Year Completed": 2012, "City": "Mecca", "Country": "Saudi Arabia", "Primary Use": "Mixed Use"}, {"Building Name": "Ping An Finance Center", "Height (m)": 599, "Height (ft)": 1965, "Year Completed": 2017, "City": "Shenzhen", "Country": "China", "Primary Use": "Office"}, {"Building Name": "Lotte World Tower", "Height (m)": 555, "Height (ft)": 1819, "Year Completed": 2017, "City": "Seoul", "Country": "South Korea", "Primary Use": "Mixed Use"}, {"Building Name": "One World Trade Center", "Height (m)": 541, "Height (ft)": 1776, "Year Completed": 2014, "City": "New York City", "Country": "United States", "Primary Use": "Office"}, {"Building Name": "Guangzhou CTF Finance Centre", "Height (m)": 530, "Height (ft)": 1739, "Year Completed": 2016, "City": "Guangzhou", "Country": "China", "Primary Use": "Hotel/Residential/Office"}, {"Building Name": "Tianjin CTF Finance Centre", "Height (m)": 530, "Height (ft)": 1739, "Year Completed": 2019, "City": "Tianjin", "Country": "China", "Primary Use": "Hotel/Office"}, {"Building Name": "CITIC Tower", "Height (m)": 528, "Height (ft)": 1732, "Year Completed": 2018, "City": "Beijing", "Country": "China", "Primary Use": "Office"}, {"Building Name": "TAIPEI 101", "Height (m)": 508, "Height (ft)": 1667, "Year Completed": 2004, "City": "Taipei", "Country": "Taiwan", "Primary Use": "Office"} ] }, "mark": "bar", "encoding": { "y": { "field": "Building Name", "type": "nominal", "sort": {"field": "Height (m)", "order": "descending"} }, "x": {"field": "Height (m)", "type": "quantitative"}, "color": { "field": "Primary Use", "type": "nominal", "scale": { "range": ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf" ] }, "tooltip": [ {"field": "Building Name", "type": "nominal"}, {"field": "Height (m)", "type": "quantitative"}, {"field": "Year Completed", "type": "ordinal"}, {"field": "City", "type": "nominal"}, {"field": "Country", "type": "nominal"}, {"field": "Primary Use", "type": "nominal"} ] }, "config": { "view": {"continuousHeight": 400, "continuousWidth": 600} } } }