d3-scale VS d3

https://github.com/d3/d3-scale for the linear scale mostly

scale system

On the other hand, for the Y-axis, both charts present a scale with the same domain, ending in a nice round value (100). However, the number of ticks is different.

d3-scale Documentation

Let's start small and build axes. To build axes, we need to define scales first. Under the hood VisX uses d3, so we it's really useful to review d3 scales documentation. D3 will use math to help translate our numbers to pixel locations inside the container.

Before we render our horizontal axis, we have to remember about scales. Scales are functions that are responsible for mapping data values to visual variables. I don't want to dive too deep into this topic, but if you're interested in further reading, you can check out scales documentation. We want our x-axis to display labels from data, so for this we will use scaleBand.

We have used several utilities methods from d3 part of the scale module to correctly map our axis with the datas (scaleLinear, scaleBand). If you open your navigator you see now an svg element with two axis but no data yet.

Ah, sorry–misunderstood. Looking at your data it seems like you‘re mapping the raw values to color values, and you seem to have an extreme difference between values (high standard deviation). Try something like d3-scale to normalize the values and make them closer together (to be clear: you should still display the correct values to the user, but when visualizing them, you can use a more logarithmic scale to get a better color spread).

Yes this was done with a combination of GSAP Scrolltrigger https://gsap.com/docs/v3/Plugins/ScrollTrigger/ and https://d3js.org/

d3 - very power visualization library enabling dynamic visualizations. docs

Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).

D3.js: A robust library to visualize your data and create interactive data-driven visualizations.

They are images so it could be any number of things, datawrapper, charts.js, d3.js to name a few options.

I made this interactive visualization that attempts to show the real-time frequency and location of births around the world. A country’s annual births (i.e. the country’s population times its birthrate) were distributed across all of the populated locations in each country, weighted by the population distribution (i.e. more populated areas got a greater fraction of the births). Data Sources and Tools Population and birthrate data for 2023 was obtained from Wikipedia (Population and birth rates). Population distribution across the globe was obtained from Socioeconomic Data and Applications Center (sedac) at Columbia University. Data is processed and visualized at a 1 degree x 1 degree resolution, each of which has a different probability of a birth occurring in a specific time period. D3.js was used to create the map elements and html, css and javascript were used to create the user interface.

When I go to the homepage of types/d3 the only hint for any kind of documentation is what seems to be the main github page of d3. It's highly possible I'm missing something here, so sorry if I am but I can't find any documentation of how you are supposed to type these library objects.

Website: D3.js official site

D3js