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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": "", "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} } } }