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Category: Visualization

Extracting Numerical Data Points From Images

Published 2019-02-19 by Kevin Feasel

Matt Allington visualizes changes in the Gartner magic quadrant for BI tools:

Today Gartner released the 2019 magic quadrant for Business Intelligence.  As expected (by me at least), Microsoft is continuing its trail blazing and now has a clear lead over Tableau in both ability to execute and completeness of vision.  I thought it would be interesting to see a trend over time for the last 5 years, as this is the time period that I have been a professional Power BI Consultant.  I needed some way to extract the numerical data points from the images I had collected.  This article shows you how to do that.  Here is the final output – a scatter chart with a play axis in Power BI of course.

I was just commenting the other day about how somebody should do this and Matt went and did it.

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Power BI Violin Plots

Published 2019-02-15 by Kevin Feasel

Meagan Longoria shows off a violin plot custom visual in Power BI:

A violin plot is a nifty chart that shows both distribution and density of data. It’s essentially a box plot with a density plot on each side. Box plots are a common way to show variation in data, but their limitation is that you can’t see frequency of values. In other words, you can see statistics such as min, max, median, mean, or quartiles, but you can’t see the individual values nor how often they occurred.

Read on for a review of the custom visual available for violin plots, including areas where it does well and where it falls short at present.

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Generating Plots Like The BBC

Published 2019-02-12 by Kevin Feasel

David Smith has some notes on bbplot, a ggplot2 extension the BBC uses for its graphics:

If you’re looking a guide to making publication-ready data visualizations in R, check out the BBC Visual and Data Journalism cookbook for R graphics. Announced in a BBC blog post this week, it provides scripts for making line charts, bar charts, and other visualizations like those below used in the BBC’s data journalism. 

I’m still reading through the linked cookbook but it’s a good one.

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Generating Reports In Word Via Flow

Published 2019-01-28 by Kevin Feasel

Chris Webb has an example of taking a data set and generating a report in a Word document:

The idea is to loop through the rows in the Excel table and use the data on each row to populate the content controls in the template and then create a new Word document. Here’s a Flow that does this:

The steps are a bit convoluted, but they work. Chris mentions at the end why people might want to do this, and I’ll reiterate that: I’ve been in several discussions over the years where people want to embed data inside a document without manual intervention, and using tools like Reporting Services, that has not been pretty.

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Visualization Failures

Published 2019-01-24 by Kevin Feasel

Stephanie Evergreen talks about two specific instances of self-inflicted visualization failure:

There’s a solid argument to be made that the scales in these charts shouldn’tstart at zero because we wouldn’t see any difference between the two years; all the lines would look flat. But there’s also a solid reason why they should start at zero—maybe I’m exaggerating the change if I don’t. Only the people who work closely with this data would know what kind of scale would fit best given the context of this foundation.

However, people on social media took notice of what they thought was a failure of mine and one commenter tweeted that “there’s no way [a dataviz Godfather] would approve this visual.” So, I got up the guts and sent the whole thing to the Godfather himself.

The Godfather wrote back: “To be honest, almost everything about your redesign is deceitful.” Ouch. I may have actually shed tears over this one. I was devastated.

There’s a good reminder here that failure is a critical part of learning.

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Using Plotly In Power BI

Published 2019-01-22 by Kevin Feasel

Kara Annanie shows how you can R integration in Power BI to push Plotly visuals to users:

In the example, above, we’ve created a line chart visualization using Plotly and we’ve decided to put labels on the graph, but only on the first and last points of the line graph. This graph would be particularly useful to show 13 months of data overtime, where the left-most label shows January of last year, for example, and the right-most label shows January of this year, for example. The user could still view the trend across the year between both January data points.

Click through for a pair of videos and some notes on how to get started.

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Combining Plots In R With cowplot

Published 2019-01-17 by Kevin Feasel

Abdul Majed Raja shows how to use the cowplot library in R to merge together independent plots into a single image:

The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce a unified single plot.

In the below code, We will build three different histograms using the R’s in-built dataset iris and then assign one by one to an R object. Finally, we will use cowplot function plot_grid() to combine the two plots of our interest.

The only thing that disappointed me with cowplot is that its name has nothing to do with cattle.

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R htmlTable Updates

Published 2019-01-08 by Kevin Feasel

Max Gordon has some updates to the htmlTable package:

Even more common than grouping columns is probably grouping data by rows. The htmlTable allows you to do this by rgroup and tspanner. The most common approach is by using rgroupas the first row-grouping element but with larger tables you frequently want to separate concepts into separate sections. Here’s a more complex example. This has previously been a little cumbersome to to counting the rows of each tspanner but now you’re able to (1) leave out the last row, (2) specify the number of rgroups instead of the number of rows. The latter is convenient as the n.tspanner must align with the underlying rgroup. 

I haven’t used this package before, but it does look interesting. H/T R-bloggers

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gganimate Now On CRAN

Published 2019-01-04 by Kevin Feasel

Thomas Lin Pedersen announces that gganimate is now available on CRAN:

While this commit was done in the autumn 2017, nothing further happened until I decided to make gganimate the center of my useR 2018 keynote, at which point I was forced (by myself) to have some sort of package ready by the summer of 2018.
A fair amount of users have shown displeasure in the breaking changes this history has resulted in. Many blog posts have already been written focusing on the old API, as well as code on numerous computers that will no longer work. I understand this frustration, of course, but both me and David agreed that doing it this way was for the best in the end. I’m positive that the new API has already greatly exceeded the mind-share of the old API and given a year the old API will be all but a distant memory…

Read on for information on these breaking changes, and how the changes will make life easier in the long run. And stay for the fireworks. H/T R-Bloggers

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Reviewing Word Associations With R

Published 2018-12-18 by Kevin Feasel

Julia Silge does some exploratory analysis on the Small World of Words project:

The Small World of Words project focuses on word associations. You can try it out for yourself to see how it works, but the general idea is that the participant is presented with a word (from “telephone” to “journalist” to “yoga”) and is then asked to give their immediate association with that word. The project has collected more than 15 million responses to date, and is still collecting data. You can check out some pre-built visualizations the researchers have put together to explore the dataset, or you can download the data for yourself.

It’s an interesting analysis of the data set, mixed in with some good R code.

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