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Linkoteca. D3

This tutorial is meant for D3 v5-v7.

This tutorial is a quick intro to D3.js, a Javascript library for creating interactive data visualizations in the browser. D3 is built on top of common web standards like HTML, CSS, and SVG.

This is not designed to be a deep dive — this tutorial will teach you how to learn D3 and give you a high-level understanding of this powerful tool.

Broadly speaking there are 4 steps to setting up a force simulation:

  • create an array of objects
  • call forceSimulation, passing in the array of objects
  • add one or more force functions (e.g. forceManyBody, forceCenter, forceCollide) to the system
  • set up a callback function to update the element positions after each tick

cola.js (A.K.A. «WebCoLa») is an open-source JavaScript library for arranging your HTML5 documents and diagrams using constraint-based optimization techniques.

It works well with libraries like D3.js, svg.js, and Cytoscape.js. The core layout is based on a complete rewrite in Javascript of the C++ libcola library.

It also has an adaptor for d3.js that allows you to use cola as a drop-in replacement for the D3 force layout. The layout converges to a local optimum unlike the D3 force layout, which forces convergence through a simple annealing strategy. Thus, compared to D3 force layout:

There’s three steps to add zoom and pan behaviour to an element:

  • call d3.zoom() to create a zoom behaviour function
  • add an event handler that gets called when a zoom or pan event occurs. The event handler receives a transform which can be applied to chart elements
  • attach the zoom behaviour to an element that receives the zoom and pan gestures

Quantile and quantize scales have very similar names, confusingly so. Here’s how to remember their meanings:

Quantiles are defined, in statistics, as separating a population into intervals of similar sizes (the 10% poorest, the 50% tallest, the 1% richest…); a quantile scale is essentially defined by its domain, a fixed set of values — when applied to a new value, it will then compute its ranking with respect to the initial distribution, and map that ranking to the output range.

To Quantize means to group values with discrete increments — like expressing a list of floating point numbers with 1 decimal digit, or rounding time to the closest minute. It is the output range that is discretized, and such a scale allows to transform an initial continuous range into a discrete set of classes.

Threshold scales allow us to directly specify the cut values that separate the classes.

When, on a print map, 1 cm figures a real distance of 1 km on the terrain, we say that the map has a 1:100,000 scale.

But scales are not limited to a proportional ratio (or rule of three) between an actual distance and a length on paper. More generally, they describe how an actual dimension of the original data is to be represented as a visual variable. In this sense, scales are one of the most fundamental abstractions of data visualization.

Scales from the d3-scale module are functions that take as input the actual value of a measurement or property. Their output can in turn be used to encode a relevant representation.

A pretty specific title, huh? The versioning is key in this map-making how-to. D3.js version 5 has gotten serious with the Promise class which resulted in some subtle syntax changes that proven big enough to cause confusion among the D3.js old dogs and the newcomers. This post guides you through creating a simple map in this specific version of the library. If you’d rather dive deeper into the art of making maps in D3 try the classic guides produced by Mike Bostock.

This is part of a series of examples that describe the basic operation of the D3.js force layout.

…linkDistance tells the force layout the desired distance between connected nodes. It may seem strange that D3 doesn’t simply compel all links to be that distance. The force layout, however, takes other factors into account as well, which sometimes prevents it from achieving the exact link distance in all cases.

…charge, so named because it’s a property that acts like electrical charge on the nodes. With force-directed graphs in particular, charge causes nodes in the graph to repel each other. This behavior is generally desirable because it tends to prevent the nodes from overlapping each other in the visualization.

Python geopandas choropleth map

There are different ways of creating choropleth maps in Python. In a previous notebook, I showed how you can use the Basemap library to accomplish this. More than 2 years have passed since publication and the available tools have evolved a lot. In this notebook I use the GeoPandas library to create a choropleth map. As you’ll see the code is more concise and easier to follow along.

Many datasets are intrinsically hierarchical. Consider geographic entities, such as census blocks, census tracts, counties and states; the command structure of businesses and governments; file systems and software packages. And even non-hierarchical data may be arranged empirically into a hierarchy, as with k-means clustering or phylogenetic trees.

This module implements several popular techniques for visualizing hierarchical data:

Node-link diagrams show topology using discrete marks for nodes and links, such as a circle for each node and a line connecting each parent and child. The “tidy” tree is delightfully compact, while the dendrogram places leaves at the same level. (These have both polar and Cartesian forms.) Indented trees are useful for interactive browsing.

Adjacency diagrams show topology through the relative placement of nodes. They may also encode a quantitative dimension in the area of each node, for example to show revenue or file size. The “icicle” diagram uses rectangles, while the “sunburst” uses annular segments.

Enclosure diagrams also use an area encoding, but show topology through containment. A treemap recursively subdivides area into rectangles. Circle-packing tightly nests circles; this is not as space-efficient as a treemap, but perhaps more readily shows topology.