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Data visualization

Data visualization is the graphic representation of data and trends.
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James Potter jamespotter.dev

Is Hacker News a good predictor of future tech trends?

HN as tech trend? The results seem conclusive, but drop a comment below to share your thoughts on what James shared, his methodology, etc.

There’s a general belief in the tech circles I inhabit that Hacker News is a useful indicator for up-and-coming technologies that will hit the mainstream within the next few years. So I picked some of the major tech topics of the past fifteen years to see if that’s really true. Can I convince myself that checking the HN front page multiple times a day is a useful and productive exercise?

Is Hacker News a good predictor of future tech trends?

Jacob Zelko github.com

Javis.jl - Julia animations and visualizations

For the past year, my friend, Ole Kröger, and I have been developing a native animation package in Julia called, Javis.jl. Through our development process, we have been able to build a nearly 70 person developer community, sponsor Google summer of code students, and help new Julia programmers create powerful visuals! We recently presented the tool at JuliaCon, and were able to show its use for educational outreach and beyond.

Our hope is that this open source tool can be used by programmers, educators, professionals and researchers from across the globe to convey their ideas in winsome and understandable ways!

Data visualization schleiss.io

Plotting the source code "TODO" history of the most popular open source projects

It’s fun seeing the proliferation of TODO comments over time on these bastions of open source. One not-surprising (but still unfortunate) trend: they all pretty much move up and to the right 📈, but a few have had some dramatic reversals 📉 at certain points in time. Go had a crazy month in April 2018 & TypeScript’s TODOs exploded in the Spring of 2018.

Stack Overflow stackoverflow.blog

How often do people actually copy/paste from Stack Overflow? Now we know

April Fool’s may be over, but once we set up a system to react every time someone typed Command+C, we realized there was also an opportunity to learn about how people use our site. Here’s what we found.

TLDR; one in four users copy something within five minutes of hitting a page. But this blog post (and accompanying podcast episode) goes deep into the details and lays it all out for you with pretty charts.

Petr Stribny stribny.name

Which programming languages pay the most? I made my own salary charts...

Synthesized insights from Stack Overflow’s 2020 survey data:

The dataset has 33,447 salary data points which probably isn’t that many given that there are probably around 25 million software developers in the world. You have been warned.

Despite Petr’s warnings, he did go through some trouble to make the data as good as possible (short of, you know, finding or creating more data sources 😉).

Which programming languages pay the most? I made my own salary charts...

Mike Bostock observablehq.com

Did I learn anything from 10 years of D3.js?

Mike Bostock celebrates D3’s 10th by reflecting on what he’s learned over the years. There’s a lot to glean from Mike’s reflections. I really enjoyed this sentiment under the “Don’t go it alone” section:

To avoid entrusting your emotional wellbeing to internet randos (see #8), you must develop relationships with a small, stable group of people that you respect. In other words, find a team (or community) that can provide validation, feedback, support, and mentorship. Maybe this is obvious to everyone but me — yes, Mike, friends are good — but I feel like it’s worth repeating today when so much human interaction happens at a distance.

Python github.com

Apache Superset – a data visualization and data exploration platform

Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.

This has been around long enough to be picked up by the Apache Foundation, but somehow it’s avoided my radar until today. The visualizations you can achieve with it are impressive, to say the least.

Apache Superset – a data visualization and data exploration platform

Startups blog.excalidraw.com

One year of Excalidraw

I love posts like these from startups/projects that share how they’re doing over time:

Excalidraw started as a way to procrastinate on January 1st, 2020, and ended up being a fully fledged whiteboard product only one year later! In this post, we’ll go over the most important features that made Excalidraw great at being a virtual whiteboard for sketching hand-drawn like diagrams.

They detail their open source tech stack, new features the team shipped last year, cool things people are doing with the tool, and more.

(The tool itself, btw, looks totally rad and is definitely something I’ll be toying with over the coming weeks.)

Data visualization awslabs.github.io

A library to display an interactive editor for any graph-like data

Diagram Maker is a framework & data format agnostic library that is fully customizable in terms of look & feel as well as behavior. It also exposes a declarative interface to reduce the code required to integrate the library in any application and comes with many interactive features built in.

See it in action on their interactive demo.

A library to display an interactive editor for any graph-like data

React github.com

visx – airbnb's visualization components built with D3 and React

At Airbnb, we made it a goal to unify our visualization stack across the company and in the process, we created a new project that brings together the power of D3 with the joy of React.

The library boasts small bundle sizes due to package splitting, is un-opinionated about integrations like state management and animations (if that’s a feature for you), and is explicitly not a charting library.

As you start using visualization primitives, you’ll end up building your own charting library that’s optimized for your use case. You’re in control.

Practical AI Practical AI #86

Exploring the COVID-19 Open Research Dataset

In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.

The Changelog The Changelog #390

Visualizing the spread of Coronavirus

Harry Stevens is a Graphics Reporter at The Washington Post and the author of “Why outbreaks like coronavirus spread exponentially, and how to ‘flatten the curve’” — the most popular post in The Washington Post’s online history.

We cover the necessary details of this global pandemic, the journalist, coding, and design skills required to be a graphics reporter, the backstory on visualizing this outbreak, why Harry chooses R over Python, advice for aspiring graphics reporters, and how all of this came together at the perfect time in history to give Harry a chance to catch lightning in a bottle.

Jack McKew jackmckew.dev

Simulating a virus outbreak with JavaScript

Jack builds on this post from Harry Stevens on The Washington Post to create an interactive virus outbreak simulator with JavaScript, HTML5, and Canvas. It simulates the effectiveness of lockdowns, social distancing, PPE, and more. Jack shares the code too.

This post will simulate how viruses can spread throughout a community and implement a variety of different parameters to see how these affect the simulation.

Simulating a virus outbreak with JavaScript

Data visualization gabgoh.github.io

An interactive epidemic calculator

This calculator lets you tweak things like R0, incubation time, and hospitalization rate to see how affect the results. From the author:

At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging. I created this calculator as an attempt to address this gap in understanding.

An interactive epidemic calculator

Harry Stevens washingtonpost.com

Extensive social distancing helps to 'flatten the curve'

Graphics reporter Harry Stevens from The Washington Post helps us see the impact of “social distancing” with this coronavirus simulator. He shows the effects of four simulations — a free-for-all, an attempted quarantine, moderate social distancing, and extensive social distancing.

Harry goes on to say, “moderate social distancing will usually outperform the attempted quarantine, and extensive social distancing usually works best of all.”

To simulate more social distancing, instead of allowing a quarter of the population to move, we will see what happens when we let just one of every eight people move.

Extensive social distancing helps to 'flatten the curve'

Lauren Gardner arcgis.com

COVID-19 (2019-nCoV) real time dashboard

This interactive dashboard was created by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University to visualize and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The data collected and displayed are freely available on GitHub.

Below are a few notable pull-quotes from this correspondence on The Lancet’s Infectious Diseases journal.

The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds.

The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable…

Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks.

For more updates and resources follow Lauren Gardner on Twitter or read the readme.

COVID-19 (2019-nCoV) real time dashboard
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