This month I am working with financial data, so I thought it was a good idea to refresh my financial mathematics knowledge replicating some simple Capital Asset Pricing Modelling (CAPM) in Tableau. The CAPM is a set of mathematic rules used to price assets, based on the general idea that for an investment to be worth it, its risk must be rewarded by an appropriate rate of return. In this blog, I will show how to use Tableau to build the simplest CAPM model: the Efficient Frontier for a two-assets portfolio.
eah, it’s that time of the year again, when Tableau hosts another round of the #IronViz competition. This time the topic is “Safari”: Animals & Plants.
After playing around with a couple of datasets like the IUCN list of threatened species, and the London Fire Brigade animal rescue records, I decided to have a close look to some of the most interesting animals in the world: Penguins!
Here below is my final submission, built around a map of the Antarctic Penguins colonies which uses a polar projection in Tableau. If you want to know I achieved it, with a mix of alteryx and mapbox magic, this post is for you.
I really appreciate the power of constructive criticism in the #DataViz space. Given the importance of the audience in the data visualization process, feedback from peers is a gift to ask for and to encourage.
When criticizing a visualization, there are both technical and ethical rules to be followed, and I think most of them can be found in two main sources:
1. The #MakeoverMonday project, that every week picks a data visualization to improve, asking participants to stick to the original data, and constrain their time to a hour (to seek simplicity);
2. A framework recently published by Stephen Few, where he purposed a structured way to assess the quality of a data visualization.
Some week ago, my good friend, colleague and great data wrangler Ben Moss published a new viz about flight delays.
As an Italian expat in London, I can easily qualify as an interested audience, and I definitely had questions to ask the dashboard.
This peculiar cocktail of interest, good data, and friendship made me willing to apply to Ben’s dashboard the rules of both the MakeoverMonday project and the framework outlined by Stephen Few.
This week I spent my free time stretching and refreshing my (high school) knowledge of plotting exponential functions. Here below the end result: A visualization in Tableau of all the missions to Mars and the Moon so far, represented by curved lines and sorted by recency.
Last week, Buzzfeed released the output of a research they have carried on all the connections that Donald Trump holds with people and organizations.
I decided I wanted to visualize this dataset on a network chart, showing both the direct and indirect business connections of Trump. See below the end result (click for the interactive version).
On Tuesday, the world wake up with a new POTUS, and the media were filled with data visualizations of the result. An electoral dataviz that never gets old is the “choropleth map”, like the one below:
However, despite popularity and ease of interpretation, this map can be told inaccurate for a very simple reason: it suggests to the eye a disproportioned result, based on State’s areas, not actual votes.
At the beginning of this year, I decided to get involved in the Tableau Foundation Service Corps: a group of people volunteering for charities and no profit organizations using Tableau. At the time, I was really excited to join this project, as to me it represented a great opportunity to put my skills at the service of good causes.
And this has really been the case. Since then, I have been involved in a project that let me feel really helpful, and for which I am so grateful to who entrusted me with its implementation: the Humanitarian Response Map Review.
The output of the project is the dashboard below: basically a visual way for decision makers on emergency fields to quickly filter the multitude of maps created by various organizations and find the ones that suit their needs.
For this third and last Tableau #IronViz qualification round (follow the link and find my dataviz, if you want to vote for me!), I decided to build something using this cool dataset: a publicly available csv with ice extent in the northern hemisphere by region, updated daily.
As for the visualization, I chose to build a radial chart, because I had already built one focusing on the North Pole, and I quite liked the result. In this chart, each point is a day and each ‘circle’ is a year, while the ice extent is encoded in the distance from the center.
Using this dashboard, it is possible to get two information at a glance:
- The different seasonalities of the regions, that draw totally different shapes;
- The effect of climate change on the ice extent of different regions.