D3 tutorial for the "Introduction to D3" event hosted by the Columbia Data Science Society
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
data Initial commit Nov 22, 2016
images Fix power scale image/code Nov 22, 2016
README.md CDSS link Jul 28, 2017
index.html Fix power scale image/code Nov 22, 2016
template.html Change title on template Nov 23, 2016

README.md

Introduction to D3: Map Visualization

by Woojin Kim

This tutorial was created for the Introduction to D3 (Facebook event) event hosted by the Columbia Data Science Society. You can see the finished product here.

Table of Contents

Introduction

Why D3.js?

The main advantages of D3.js are that it's extremely flexible and ready for web:

  • Almost every aspect of D3 visualizations can be manipulated, from simple aspects like colours to more specific tasks like pixel-perfect positioning, path bezier curving. You have control over each data point as it comes in and can set the logic on how it is interpreted into a visual element. While this may seem daunting, especially from something like Excel or even ggplot2, it is also incredibly rewarding when you're able create exactly the visualization you envision.

  • It is a JavaScript library that integrates seemlessly into webpages. In comparison to many other data visualization tools that either create static images, require Flash applets, or something like Shiny to deploy R work; D3 just requires a simple script import on a HTML file and all the tools are ready to be integrated into your website. This also means everything is done client-side and you don't need anything more than a place to host your files.

  • It uses scalable vector graphics (SVG) to create the visualizations, which result in smooth graphics that scale well for different media.

Console is your best friend

Your browser's console is an interactive shell for JavaScript, much like running python on your terminal. On Firefox, you can bring it up using Cmd (⌘)/Ctrl (Windows) + Alt (⌥) + K; on Chrome, you can get it using Cmd (⌘)/Ctrl (Windows) + Alt (⌥) + J.

Here you can test snippets of code, see the raw outputs of your code, explore your variables, and much more. Whenever you have anything you want to see on the console, put the variable in console.log().

Note to working on Chrome

Chrome disables cross-origin requests for local files. In order to have files opened as expected, navigate to the folder on terminal and execute python -m SimpleHTTPServer 8000 to quickly create a server. You can now access the rendered page at http://localhost:8000/. Ctrl (⌃) + C to quit when you're done.

Data sources

For this tutorial, we will be looking at the county-level poverty rates in the United States. The files you'll need are included in the repo, but the sources are included below in case you wanted to find more information or wanted to explore different datasets:

Let's get coding!

Start with the template.html file. It has the basic template for a HTML page already written, as well as basic CSS definitions for the page and the tooltip we'll add. The finished code is on index.html, feel free to use that as a reference if you ever get confused. Hopefully by the end of this, you'll have something that looks similar!

D3.js library

We begin by importing the D3 library. You can either download the script and have it pointing locally or use a CDN. Go ahead import the CDN version by adding the following snippet somewhere in the header section:

<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.3.0/d3.min.js">
</script>

Data loading / pre-processing

Before we get to any visualization, we start by loading the data. We'll be making use of D3's queue() library to load two datasets then have it callback the visualization code when the data is finished loading. The datasets will be sent to d3.json() and d3.csv(), according to their file type. The following code block goes in between the <script> tags:

d3.queue()
    .defer(d3.json, "data/gz_2010_us_050_00_5m.json")
    .defer(d3.csv, "data/PovertyEstimates.csv")
    .await(function(error, map_json, data_csv) {
        // Visualization code to come
    });

Try putting console.log(data_csv) in the .await() block to see how the data looks in the browser. You'll notice that it's a bunch of objects corresponding to data from each county. Also take a look at the map data using console.log(map_json), you'll notice that all the data is nested under the features key. Inside each object, you'll see the shape of each county saved under geometry and basic facts saved under properties.

The unique ID that we can use to identify the different counties is called the FIPS county code, which is a combination of state and county codes you see under map data's properties. As it'll be more clear later, we want to be able to access the poverty dataset's rows using the FIPS code. To do this, we make use of d3.map(), which is basically a key/value map, much like a dictionary.

We want to create this map as we're loading the data. In D3, most of the time data is involved, you can invoke an anonymous function to process individual rows.

function(d) {
    // d corresponds to the row
    // d.FIPStxt or d['FIPStxt'] both returns the FIPS code for that row
}

We start by creating a map to be populated before the data loads. Then, we add an anonymous function on the d3.csv() loading. First we note that d3.csv() loads everything as strings. We need to store numbers as numbers, we achieve this by appending a variable with a + sign. Today, we will be looking at 'PCTPOVALL_2014', which is the "estimated percent of people of all ages in poverty 2014". We get the poverty rate with the key, then save the same variable as a number.

d['PCTPOVALL_2014'] = +d['PCTPOVALL_2014']

map.set()'s arguments are key and value, so we specify that we want to use the FIPS code to access the entire row's data. The updated code block should look like this now:

var data_map = d3.map();
d3.queue()
    .defer(d3.json, "maps/gz_2010_us_050_00_5m.json")
    .defer(d3.csv, "data/PovertyEstimates.csv", function(d){
        // Convert to number
        d['PCTPOVALL_2014'] = +d['PCTPOVALL_2014'];

        // Use the county's FIPS code to access that county's data
        return data_map.set(d['FIPStxt'], d);
    })
    .await(function(error, map_json, data_csv) {
        // Visualization code to come
    });

Lastly, we want to give D3 an easy array to iterate over when creating our map later. As we noticed earlier, all the map data is nested under the features key. We create an unpacked variable that is an array of all the counties (this code and all the code from now on goes inside the .await() block):

var counties = map_json['features'];

SVG setup

In order to draw our map, we need an SVG element to draw everything inside. We can place this SVG anywhere in the document; in this case, we'll use the <div id='map'> that I placed on the template. d3.select('#map') lets you select elements in the document (like you would with jQuery, for example); #map specifies the id of the div we're looking for.

We then use d3.append() to append an SVG element inside the div. (In case you're interested, the new element will be appended as the last child of the parent element)

var width = 1200,
    height = 600;

var svg = d3.select('#map').append('svg')
    .attr('height', height)
    .attr('width', width);

Note that we keep width and height in variables so that we can use these numbers later.

Geography setup

We want to achieve two things in this section: geo path handling and projection.

Since this tutorial is to get started on map visualizations, I won't go into much detail about paths. But long story short, D3 needs cartesian paths in order to know how to draw lines. Inside a GeoJSON file are many lists of coordinates that correspond to counties. d3.geoPath() helps us convert these into path instructions that D3 can follow.

We also need to tell it the scale in which to draw the map. Play around with the number until it fits your desired window size. We also set the center of the map to be the center of our SVG element, which we can calculate using width and height variables we stored earlier.

Since we're dealing with maps, projections are very important. Every projection has its advantages and disadvantages, and it's important to choose the correct one for your intended use. In this case, we'll be dealing with United States, which is typically difficult to fit in one continuous map easily due to Alaska and Hawaii. As a result, we'll be using a US-specific version of Albers equal area projection, d3.geoAlbersUsa(), that fits the entire map in one view (see this page for other projections). We save this projection as a variable and point it to the path generator.

var proj = d3.geoAlbersUsa()
            .scale(1300)
            .translate([width/2, height/2]);
var path_gen = d3.geoPath(proj);

Colour scale setup

We want to be able to map specific poverty rate values into different colours. Different data require different scales, used for different effect. Some of the more common scales include linear (d3.scaleLinear()), power (d3.scalePow()), log (d3.scaleLog()), quantile (d3.scaleQuantile()), and quantize (d3.scaleQuantize())

You have to be careful about the scale you choose. As you see below, the same data paints very different pictures:

Quantile

Quantile

Linear

Linear

Quantize

Power

Quantiles split the observations into number of bins specified in .range(). Linear uses a continuous scale based on the range. Outliers are better spotted on linear, but at the same time, the difference in levels isn't as clear for the rest. Use your judgement for which scale to use, in this tutorial we will be using quantiles. See the finished code for examples of other scales.

A great resource for getting colours is ColorBrewer 2.0. There are many options on the website, in general for continuous numerical data like this, I suggest sequential, single hue, colorblind safe, and as many classes as you want quantiles. Export option conveniently provides an array you can use directly in JavaScript.

ColorBrewer

In order for the scale function to determine the range, we need to create an array with all the values we will look at. Because our data is currently stored as a map, we unpack all the rows of the dataset, then use a map function to return the feature we are interested in.

var colors = ['#f7fbff','#deebf7','#c6dbef','#9ecae1',
                '#6baed6','#4292c6','#2171b5','#084594'];
var all_values = data_map.values().map( function(d){
    return d['PCTPOVALL_2014'];
});

Lastly, we create a quantile scale with d3.scaleQuantile(). .domain() corresponds to the data the scale will encounter, .range() corresponds to the range of colours the scale will output. We construct the scale like below:

var color_scale = d3.scaleQuantile()
                    .domain(all_values)
                    .range(colors);

Go ahead and try the scale with different values of poverty rates with something like console.log( color_scale(21) ). If all went well, it should output an RGB hex value corresponding to the rate. In this case: #2171b5.

The map

Finally on to the map!

Each of the county will be drawn as a path, which are basically list of coordinate instructions. We created path generators back in the geography setup section. In D3, you select the DOM element you want to attach your visualization elements, parse and bind the data to placeholder elements, and insert visualization elements for you to manipulate.

Here we break down the initial chained commands into what they do:

svg.selectAll('path')
                .data(counties)
                .enter()
                .append('path')
                .attr('d', path_gen)
  • svg.selectAll('path'): Under the SVG element we defined above, we "find" all the paths to attach our visualization to. Note that we don't actually have path elements to select, but the way D3 works, we need to "select" all the elements we will be adding at the beginning. Note that this is similar to how we selected the #map element from the DOM structure using d3.select('#map') above.

  • .data(counties): We parse the data with this command, which is run as many times as there are counties in our example.

  • .enter(): If we already had path elements to select on the first bullet point, we could bind individual data points to each of the path elements, but as I noted earlier, we don't have those yet. .enter() creates placeholder DOM elements to use with...

  • .append('path'): Now that we have data parsed and placeholders in place, we can append a path in svg for each one of the data points.

  • .attr('d', path_gen): How do we interpret the geometry data inside each county element? Luckily, we created a path generator above that can handle this. The path is defined by the attribute d and we're specifying that we use the path generator, path_gen, we made to interpret coordinates into paths.

Now we have a blank map, most likely coloured entirely black. The core of this visualization is the way that the counties are coloured and how the poverty rates are interpreted as colours to fill the shapes. The fill colour is specified with .style('fill', ...). We receive a row of data (from the county dataset) each time here, we again want to create an anonymous function to pick the data we want and return the colour we want.

Remember that we call the poverty data map using the five-digit FIPS code, which is a combination of state and county codes. The map dataset contains both of these info, but we need to concatenate the STATE and COUNTY fields to create the full code:

fips_code = d['properties']['STATE'] + d['properties']['COUNTY'];

The dataset is incomplete for the poverty dataset, so we also need to check if the map contains the key:

if (data_map.has(fips_code)) {
    // Color logic goes here
}

Now that we confirmed that the key exists, we ".get()" the row of poverty data corresponding to each FIPS code, extract the PCTPOVALL_2014 variable to feed to the color_scale() we created earlier and return that value:

poverty_data = data_map.get(fips_code);
data = poverty_data['PCTPOVALL_2014'];

return color_scale(data);

Congratulations, you now have a map showing the 2014 poverty rates in the United States!

Extras: Aesthetics

You'll notice that the colours are quite intense and there are also no borders between the counties. We address this by specifying line strokes for each shape and setting the opacity for the both the shapes and the strokes. Adjust the colours, the opacity, and the width to your liking.

.style('opacity', 0.8)
.style('stroke', '#a0a0a0')
.style('stroke-width', 0.8)
.style('stroke-opacity', 0.1)

Extras: Mouseover action / tooltip window

One of the great advantages of D3 is the interactivity that you can add to your visualizations. There is an endless amount of things you can do in this area, today I'll cover the mouseover logic and showing a tooltip window.

When someone hovers over a county, we want to highlight that county. For this, we use the .on('mouseover', ...) attribute. Again, we create an anonymous block to perform our actions. We will highlight by making the shape darker; since we lowered the opacity earlier, we can make it darker by setting the opacity to be higher.

How do we select the shape we're already on? Just ask for this, of course! d3.select(this) let you manipulate the shape you're already on inside a block. So we change the opacity when we mouseover as following:

d3.select(this)
    .style('opacity', 1);

There are a few ways of creating a tooltip window. My favourite way involves creating a blank invisible div that we populate and make visible only when the tooltip is needed. If you look at template.html, you'll notice that I created a blank tooltip div after the map and added some CSS definitions for it under <style>.

You'll notice that we have the same data unpacking logic as we had in the fill section above. The only difference is that we also unpack the name of the county using the Area_Name attribute.

This tooltip already has styles set and is default invisible, so we just need to make visible, set the location and the content:

  • d3.select('.tooltip'): We select the DOM element with the class .tooltip.

  • .style('visibility','visible'): We set the visibility attribute of this element as visible.

  • .style('top', d3.event.pageY+10 + 'px') and .style('left', d3.event.pageX+10 + 'px'): d3.event.pageX/Y returns the coordinates of where the mouseover event occurred. We add 10 pixels to each to account for the cursor's shape and set top and left elements of the div to position the tooltip.

  • .html('<strong>' + name + '</strong><br />Poverty rate: ' + poverty_rate + '%'): We write the HTML code for the content to go inside the tooltip div.

Now we have a tooltip window and highlighting! But wait, the highlighting doesn't disappear after I hover out and the tooltip is still visible after you leave the map. This is because we need to define a mouseout logic as well. It is pretty much the exact opposite of what we did above:

.on('mouseout', function(d) {
    // Make the county usual opacity again
    d3.select(this)
        .style('opacity', 0.8);

    // Hide the tooltip
    d3.select('.tooltip')
        .style('visibility','hidden');
});

Conclusion

There you have it, a basic interactive map! I hope this was helpful! Feel free to message me if you have any questions. If you find any errors on this tutorial, pull request away!

Other Resources