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Category: Visualization

Residuals

Published 2016-08-24 by Kevin Feasel

Simon Jackson discusses the concept of residuals:

The general approach behind each of the examples that we’ll cover below is to:

  1. Fit a regression model to predict variable (Y).

  2. Obtain the predicted and residual values associated with each observation on (Y).

  3. Plot the actual and predicted values of (Y) so that they are distinguishable, but connected.

  4. Use the residuals to make an aesthetic adjustment (e.g. red colour when residual in very high) to highlight points which are poorly predicted by the model.

The post is about 10% understanding what residuals are and 90% showing how to visualize them and spot major discrepancies.

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Not Catching Them All

Published 2016-08-23 by Kevin Feasel

Hanjo Odendaal explains clustering techniques using Pokemon:

To collect the data on all the first generation pokemon, I employ Hadley Wickam’s rvest package. I find it very intuitive and can handle all of my needs in collecting and extracting the data from a pokemon wiki. I will grab all the Pokemon up until to Gen II, which constitutes 251 individuals. I did find the website structure a bit of a pain as each pokemon had very different looking web pages. But, with some manual hacking, I eventually got the data in a nice format.

This probably means a lot more to you if you grew up in front of a Game Boy, but there’s some good technique in here regardless.

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Aster Plots

Published 2016-08-23 by Kevin Feasel

Devin Knight looks at the Aster Plot in his latest Power BI visualization video:

The Aster Plot allows a category that dives the chart and up to 2 measures.

  • The first measure controls the depth of each section

  • The second measure controls the width of each section

I have to admit that I’m not a fan of the Aster Plot.  It has all the disadvantages of pie and torus charts (specifically, that humans have a hard time discerning differences in angles) while making it more complex and comparing across a second dimension as well.

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Visualizations In Five Lines Of Code

Published 2016-08-23 by Kevin Feasel

David Smith highlights a Sharon Machlis article showing visualizations in up to five lines of R code:

I’ve reproduced Sharon’s code and charts below. I did make a couple of tweaks to the code, though. I added a call to checkpoint(“2016-08-22”) which, if you’ve saved the code to a file, will install all the necessary packages for you. (I also verified that the code runs with package versions as of today’s date, and if you’re trying out this code at a later time it will continue to do so, thanks to checkpoint.) I also modified the data download code to make it work more easily on Windows. Here are the charts and code

It’s really easy to get basic visualizations within R, and these are better than basic visualizations.

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Bullet Charts

Published 2016-08-22 by Kevin Feasel

Devin Knight continues his custom visuals series:

The bullet chart is a variation of a bar graph but designed to address some of the problems that gauges have.

  • Allows you to split chart by categories

  • Visuals can be vertical or horizontal

Some of the visualizations in this series have been hit-or-miss for me.  I’m on the fence about bullet charts:  they seem potentially useful, but also rather dense.  I like my visuals to be self-explanatory, and I’d be concerned that if I showed this to management, I’d have to explain what’s going on in more detail than I’d like.

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Power BI Calendar Visualization

Published 2016-08-11 by Kevin Feasel

Devin Knight continues his Power BI visualization series and looks at a custom calendar visual:

  • Allows you to visualize a data point on each date on the calendar.

    • The darker the color, the higher the value or density of values.

  • If you have multiple rows on the same date they are aggregated together

  • The Calendar Visualization can be used for cross filtering. Meaning you can select a square in the calendar and it will filter other visuals down to the date you picked.

This is an interesting visual.  It’s dense, but not difficult to understand.

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Quality Graphics With R

Published 2016-08-09 by Kevin Feasel

David Smith discusses building high-quality visuals with R:

Note the use of an attractive colour pallette, style-compatible fonts, and even the official Olympic icons for the sports. I just took a screenshot here, but if you click through to the actual site you’ll notice that these graphics are also scale-independent (you can zoom in on your browser and they’ll look better, not worse) and even interactive (pop-ups appear with country-specific data when you hover over a bar).

Duc-Quang has been generous enough to provide the R code behind these charts if you’d like to try your hand at something similar. The data themselves were scraped from the official Rio 2016 site. The bar charts were created using a standard geom_bar plot using ggplot2, with a custom theme to set the font to OpenSans Condensed. The interactive elements were added using the ggiraph package and the geom_bar_interactive function. The chart titles (including the icons) were created as HTML headers directly, which was then exported along with the interactive charts using the save_html function.

I’m impressed that this all comes from R.  There’s a good bit of work involved in getting this going, but you can get professional-grade graphics quality with R, and that’s pretty cool.

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Scatter Charts

Published 2016-08-01 by Kevin Feasel

Reza Rad shows how to use a scatter chart in Power BI:

Scatter chart is a built-in chart in Power BI that you can show up to three measure with a categorization in it. Three measures can be visualized in position of X axis, Y axis, and size of bubbles for scatter chart. You can also set up a date field in play axis, and then scatter chart will animate how measure values are compared to each other in each point of a time. Let’s start building something simple with this chart and see how it is working in action. At the end of example you will see a summary chart as below;

This is primarily for viewing changes in groups of data over time.  You don’t want too many data points on the map or it gets too confusing.

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