A Collection of 10 Data Visualizations You Must See

Introduction

Writing codes is fun. Creating models with them is even more intriguing. But things start getting tricky when it comes to presenting our work to a non-technical person.

This is where visualizations comes in. They are one of the best ways of telling a story with data. In this article, we look at some of the best charts and graphs people have created using tools like Python, R, and Tableau, among others.

I have also included the link to the source code or the official research paper, so you can attempt to create these visualizations on your machines or just get a general understanding of how it was created.

Let’s get into it.

Visualizing the tree line using solar panels

This is a beautiful graph where the author has visualized the trees around his house using solar panel data and the position of the sun.

You can access the source code here.

Tool Used: R

Calculating the Age of the Universe

Using data from Hyperleda, the author created this visualization in R to calculate the age of the universe. Astonishingly, his calculations were only off by -0.187% from the accepted age of the universe.

You can access the source code here.

Tool Used: R

Rendering the Moon using Earth’s Colors

This is a rendering of how the moon would look if it was filled with Earth’s colors. A spectacular effort using the moon’s topography which was converted to colormap using matplotlib and cpt city. The 3-D model was created with blender and python.

You can access the source code here.

Tool Used: Python

Gaussian Distribution

What’s impressive about this graphic is that it was created using JavaScript in a HTML document (you read that correctly).

You can access the source code here.

Tool Used: JavaScript

1.3 Billion Taxi Trips in New York City

An absolutely gorgeous firefly-like visualization. The author collected, cleaned and plotted the pick-up and drop-off locations of all taxi rides in New York between January 2009 and June 2016.

You can access the source code here.

Tool Used: Python

Instability of an unsteered bicycle

This surreal visualization shows 800 runs of a bicycle being pushed to the right. For each run, the path of the front wheel is shown until the bicycle fell over. The research paper is written in a humorous tone as well, adding to the already fascinating effort of creating self-riding bicycles.

You can access the research paper here.

Tool Used: Python

The World Seen Through 17,000 Travel Itineraries

According to the author, “Each city is plotted with its (lat, lng) coordinates and connected to any other city that came after it in someone’s itinerary. For example, London is connected to Paris because there’s at least one itinerary going from the former to the latter. The countries were clustered together and colored using the Louvain Modularity. All countries of the same color have trips that go between each other more often than other countries.”

You can further read about it, and access the source code files, here.

Tools Used: Tableau, Gephy

The BB-8 Droid

If you are a fan of the Star Wars franchise then this one is for you. The author created this droid using shapes (and a couple of other libraries) in R.

You can access the source code here.

Tool Used: R

Visualizing Solar Eclipses

The author has visualized solar eclipses over 5 millennia based on type, date, duration, and latitude. A spectacular effort.

You can access the Tableau workbook here.

Tool Used: Tableau

The Jimi Hendrix Experience

This Tableau visualization contains all of Jimi Hendrix’s live performances between 1967 and 1970. It includes which songs were played and their frequency, where the concerts were held, among other insightful data.

You can access the Tableau workbook here.

Tool Used: Tableau

These are just a few of the amazing visualizations out there. Have you come across any others recently? Let us know using the comments section below!

Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.