A Fine Slice Of SQL Server
Devin Knight continues his visualization series with the Stream Graph:
Key Takeaways
Works and looks similar to a Stacked Area Chart but with a wiggle feature that gives it a more fluid look and feel
Great for displaying data that changes over time
At first, I read this as “Steam Graph,” which made it sound like a steampunk visualization with unnecessary pipes and mechanical accouterments, but alas, it was not meant to be. I do like the stream graph visual, though.
Comments closedDevin Knight continues his custom visuals series:
In this module you will learn how to use the Sunburst chart Power BI Custom Visual. The Sunburst chart is similar to the standard donut chart but allows you to show multiple level at once, which is great when presenting hierarchical data.
I dunno; looks a little pie charty to me…
Comments closedDevin Knight continues his custom visuals series with the bubble chart:
Each category is defined as a bubble on the visual.
The size of the bubble is defined by a measure.
The Bubble chart does allow for cross filtering. Meaning you can click on a bubble in the chart and it will filter other report items
This is a nice eye-catcher image, like you might see in a news article.
Comments closedDevin Knight continues his Power BI custom visuals series:
In the Table Heatmap the color of the boxes is determined by the value in your measure.
Only 1 category field can be used, which will dynamically generate the number of columns based on the number of distinct values your field has.
The number and types of colors can be changed using some of the settings we’ll discuss below.
I can see the table heatmap being a good visual for calendars.
Comments closedDevin Knight looks at the Tornado chart:
The Tornado has a few limitation that should be aware of before using
If there’s a legend value it should only have 2 distinct values
Each distinct category values is a separate bar with left or right parts
Alternatively, you can have two measure values and compare them without a legend
I’m split on whether I like the tornado or not. It is intuitive and information-dense, which are two major factors in its favor. It is, however, difficult to read and compare. This seems like a useful “big picture” chart, but you’d want to organize the data in a different way when you start drilling down.
Comments closedSimon Jackson discusses the concept of residuals:
The general approach behind each of the examples that we’ll cover below is to:
Fit a regression model to predict variable (Y).
Obtain the predicted and residual values associated with each observation on (Y).
Plot the actual and predicted values of (Y) so that they are distinguishable, but connected.
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.
Comments closedHanjo 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.
Comments closedDevin 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.
Comments closedDavid 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.
Comments closedDevin 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.
Comments closed