To visualize the d3 modules being used, I made a log scaled scatter plot of forced directed Chernoff faces. By transforming many different attributes of our dataset into friendly glyphs, Chernoff faces allow us to understand multidimensional datasets. The encoding scheme is probably self explanatory, but I've included it below just in case:
'face': ƒ('dependentsCount')
'hair': ƒ('description', 'length')
'mouth': ƒ('downloads')
'nosew': ƒ('githubContributers')
'noseh': ƒ('githubIssues')
'eyew': ƒ('githubStars')
'eyeh': d => Math.random()
'brow': ƒ('repoSize')
I used the following modules:
Data is from nprms.io - see download-data.js to your generate your own listing of modules with different data points.