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Category: Visualization

Power BI Matrix Preview

Published 2017-03-20 by Kevin Feasel

Matt Allington reports that Power BI Desktop supports a new type of matrix (in preview):

Expand, Collapse, Drill and Filter

Expand and collapse behaves just like a pivot table however with a slightly different UI. The new matrix experience is however entirely consistent with the chart drill experience so it is very intuitive.

The new cross filter behaviour is of course not possible in a regular pivot table in Excel (without VBA). You can select any column, row or cell in the matrix and it will cross drill the other visuals on the canvas as can be seen above.

This looks like an interesting change, and Matt shows how to enable the preview.

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Storyboarding: Uncovering The Problem

Published 2017-03-17 by Kevin Feasel

Jonathan Stewart continues his series on storyboarding:

If you have heard my “Data Visualization: How to truly tell a great story!” presentation, you will have heard me mention about using a storyboard to get a better understanding of the problem. Cole Nussbaumer Knaflic does a great job of introducing this concept in her book “Storytelling with Data” which is a great read and an excellent reference tool for anyone in the data viz world.

I have adapted to using her basic storyboard as my basis for my development and we will use it today as the foundation of our series.

Jonathan ends with a set of sample questions to ask.  These are just starter questions, but they’ll help uncover important but hidden business requirements.

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Analyzing The Ramones

Published 2017-03-10 by Kevin Feasel

Salvino Salvaggio uses R to analyze The Ramones:

Musical purists always reproached the Ramones for knowing a couple of chords only and making an excessive use of them. Data show that the band knew at least… 11 different chords (out of too-many-to-bother-counting possibilities) although 80% of their songs were built on no more than 6. And there is no evidence of a sophistication of the Ramones’ compositions over time.

It’s a fun analysis with all the R code attached.  This fun analysis, however, includes n-gram analysis, sentiment analysis, and token distribution analysis.

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Getting Familiar With Shape Maps

Published 2017-03-10 by Kevin Feasel

Reza Rad shows how to generate a shape map in Power BI:

I have previously written some blog posts about Map visuals in Power BI. One of them was specifically about Filled Map, titled as Filled Map; the Good, the Bad, the Ugly! Why? you need to read that post to find the reason. In this post I want to explain the power of Shape Map which is one of the visuals Power BI team published recently. This visual is still at preview mode at the time of writing this post. This visual is much more powerful than what it looks. The actual power behind it is that you can have your own map added to it. Let’s take a closer look at this visual with an example. If you want to learn more about Power BI; read Power BI from Rookie to Rock Star.

It’s an interesting look at a new visual.

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Air Travel Route Maps With ggplot2

Published 2017-03-09 by Kevin Feasel

Peter Prevos wants to create a pretty map of flights he’s taken:

The first step was to create a list of all the places I have flown between at least once. Paging through my travel photos and diaries, I managed to create a pretty complete list. The structure of this document is simply a list of all routes (From, To) and every flight only gets counted once. The next step finds the spatial coordinates for each airport by searching Google Maps using the geocode function from the ggmap package. In some instances, I had to add the country name to avoid confusion between places.

The end result is imperfect (as Peter mentions, ggmap isn’t wrapping around), but does fit the bill for being eye-catching.

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Power BI Themes

Published 2017-03-08 by Kevin Feasel

Gogula Aralingam notes that Power BI can now support basic skinning:

The March 2017 update of Power BI Desktop comes with a preview of Themes. Right now it is in its simplest of forms: You manually create a JSON file that has a very few attributes that can set basic color themes to your reports. So all you have to do is create file that looks like this:

Click through for an example.  This isn’t a true fix for the lack of Color Vision Deficiency support, but you can plug in safe colors (for example, this article includes some) and skirt the issue until there’s real support.

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ggraph

Published 2017-02-28 by Kevin Feasel

David Smith has a post on a new R package to display graphs:

A graph, a collection of nodes connected by edges, is just data. Whether it’s a social network (where nodes are people, and edges are friend relationships), or a decision tree (where nodes are branch criteria or values, and edges decisions), the nature of the graph is easily represented in a data object. It might be represented as a matrix (where rows and columns are nodes, and elements mark whether an edge between them is present) or as a data frame (where each row is an edge, with columns representing the pair of connected nodes).

The trick comes in how you represent a graph visually; there are many different options each with strengths and weaknesses when it comes to interpretation. A graph with many nodes and edges may become an unintelligible hairball without careful arrangement, and including directionality or other attributes of edges or nodes can reveal insights about the data that wouldn’t be apparent otherwise. There are many R packages for creating and displaying graphs (igraph is a popular one, and this CRAN task view lists many others) but that’s a problem in its own right: an important part of the data exploration process is trying and comparing different visualization options, and the myriad packages and interfaces makes that process difficult for graph data.

Click through for more information as well as a mesmerizing animated image.

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