GHC19: Data Science and Pretty Charts
I don’t know what fun sounds like to you. But data science and pretty charts are a match for me. I’m also on this quest to explore various areas of IT that I think might be a fit for where I go next. While this probably isn’t based on reality, the idea of making sense of data and telling a story from it kind of appeals to me.
Circa 2007, you would be hard-pressed to find a browser that supported SVG without a plugin. Super Vector Graphics are an image type like logos and shapes that can scale without looking pixel-y. The other kind of image type is raster. JPG and GIFs fall into this category. We don’t care about them in the session. Maybe for hot dog/not hot dog tests, but not this.
Lastly, Document Object Model (DOM) allows you to modify web pages client-side without reloading the web page. In the olden days, to modify DOM you probably wrote browser-specific code for Internet Explorer and Netscape.
You can do some really fancy sh*t with it. While there are other charting libraries that you could use like Google Chart, you will trade away the flexibility and scalability that you can get from the D3.js libraries.
See more examples here at d3 Gallery on GitHub.
- Dataset. In response to an attendees question about where to find some useful data to experiment with the D3.js library, the presenters recommended GitHub for datasets and also data.gov
Upon leaving this session, I felt pretty excited that I could use D3.js and create my charts. As a sometimes mind-mapper, the collapsible tree layout appeals to me. I would love to create dynamic mind-maps based on some data in a file.
Can you think of something for which you would love to create a chart or graph?
PS — If you are interested in completing this workshop, here is the link that the presenters shared with us: https://observablehq.com/@project-essex/d3-workshop.