Static graphs are a big improvement over no graphs but we can all agree that static information is not particularly engaging. On the web there is no presenter to talk over a picture. It is the role of a visualisation to grab the reader’s attention and get its point across. Making a graph interactive is a good step towards increasing its understandability. This post in an addendum to the previous tutorial on how to make a line chart. It will explore two techniques of making the previous project interactive.Continue reading “TUTORIAL: Making an Interactive Line Chart in D3.js v.5”
The time has come to step up our game and create a line chart from scratch. And not just any line chart: a multi-series graph that can accommodate any number of lines. Besides handling multiple lines, we will work with time and linear scales, axes, and labels – or rather, have them work for us. There is plenty to do, so I suggest you fire off your D3 server and let’s get cracking.Continue reading “TUTORIAL: Making a Line Chart in D3.js v.5”
Or should I say more advanced than the construction from the previous post. This part of the tutorial will cover scales and axes. Let the fun begin!Continue reading “TUTORIAL: Advanced Bar Chart in D3.js v.5”
This is actually happening! I got myself together (the key is to more time is less Netflix, people) and wrote up a couple of examples in D3.js version 5 (yes, version 5!) that should get people started in the transition over to the tricky number 5. The guide assumes that you have some basics in D3 (you have an idea about SVG, DOM, HTML, and CSS), or better yet that you come from an earlier version. In this chapter we’ll create a simple bar chart. The objectives of the day are: data upload from a csv, data format setup, and drawing the data. As basic as this! Next time we will tackle scales and grids.
Make sure to check out my library for more fun examples!Continue reading “TUTORIAL: Simple Bar Chart in D3.js v.5”
Spoiling you as usual, I have another exciting D3 example for today: merging historical maps! I’ve been meaning to cover this topic ever since I developed a similar project for my Master’s thesis 3 years ago. Merging maps is challenge-worthy for every D3 enthusiast as it requires a number of things to be aligned: the data format should be compatible with D3.js, the maps should be drawn in the same projection, and cover the same time period as country or regional boundaries are far from static. I will demonstrate the idea by mashing up two maps: a digitalised map of II Polish Republic from 1934 with European boundaries from 1939.
Off topic: If you sent me an email via the contact form in the past and I have ignored you – believe me I haven’t. It was the Google Mail filter that treated all incoming messages from the blog as spam (until a week ago). If that was a fun or supportive message – please resend! I am also on twitter as @EveTheAnalyst
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.Continue reading “Making a Map in D3.js v.5”
Point-in-polygon is a textbook problem in geographical analysis: given a list of geocoordinates return those that fall within a boundary of an area. You could feed the algorithm a list of all European cities and it will recognise which of them belong to Sri Lanka and which to a completely random shape you drew on planet Earth. It applies to many scenarios: analyses that aren’t based on administrative boundaries, situations in which polygons change over time, or problems that aren’t geographical at all, like computer graphics. Not so long ago, I turned to point-in-polygon to generate a set of towns and villages to plot on a map of Poland from 1933. Such list has not been made available on the web and I wasn’t super keen on typing out thousands of locations. Instead, I used that mathematical cookie-cutter to extract only those locations from today’s Poland, Ukraine, Belarus, and Russia that were present within the interwar Poland boundaries. In this post I will show how to perform a point-in-polygon analysis in R and possibly automate a significant chunk of data preparation for map visualisations.Continue reading “R | Point-in-polygon, a mathematical cookie-cutter”
More often than not geographical data visualisation is performed on a a single country or a cluster of countries rather than on all 195 of them. Just as typically, acquired datasets have more features than what’s needed for the analysis. While D3.js allows for filtering the datasets so that we have full control over the visualisation’s output, the size of original datasets can slow down your website load times. To reduce this impact, datasets can be cropped beforehand. This post will explain how to shrink a standard Eurostat geographical dataset to just a handful of countries with OGR2OGR.Continue reading “Extracting countries from GeoJSON with OGR2OGR”
Summary: Intro | Measuring the importance of gossip | Too good to be true | Arbitrary measures produce arbitrary results | tl;dr
Disclaimer: The probability computation of the article is a recap of a talk delivered by Professor Mark Whitehorn at the University of Dundee in 2015, and at PASS Business Analytics Conference in San Jose, CA in 2014. Opinions expressed are my own.
Aren’t we post-NPS hype yet? Such was my thinking until a random article came up on my feed: as one of its core objectives, a tech giant was planning to improve its Net Promoter Score by 2020. A quick internet search told me there are some companies very excited about increasing their NPS. Google Trends suggests the Net Promoter methodology is on the steady growth rate since 2004; a mortal blow to my presumption. There is something problematic about the Net Promoter methodology that I’d like to talk about: on one hand an indicator of an outstanding business delivery, on the other a possibly dangerous framework for workforce assessment. This article decomposes the NPS algorithm, reviews its criticism, and tests its validity from the probability perspective. I have based the scenario and the probability computation on an excellent talk delivered by professor Mark Whitehorn. If you happen to be a manager, a person whose performance is scored with NPS, you are into probability computations, or simply you like debunking managerial fads then this is a tale for you.