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Category: Visualization

Building A Concatenated Tooltip In Power BI

Published 2017-05-25 by Kevin Feasel

Devin Knight has started a new series, walking through problems his clients have faced implementing Power BI solutions.  In this edition, Devin wants to build a comma-delimited list to display on a tooltip:

This works perfectly for Stock because it automatically summarizes the value but, you’ll notice above that the tooltip for Subcategory has an interesting behavior. Rather than displaying the list of the values in Subcategory it actually just show the very first value. This happens because the Tooltip field requires that any column used in it be able to aggregate or roll up the values into what’s shown on the chart. Since Subcategory is just a text field Power BI automatically applies the FIRST function to return back the first value that appears. You could optionally change this from FIRST to either LAST, COUNT, or COUNTDISTINCT.

So the real problem I want to solve here is rather than only showing the first subcategory how do I list all the subcategories in a comma separated list in the tooltip? Let’s walk through a couple possible designs to this solution.

Read on for two different designs, including the code to implement the solutions.

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Building A Spinning Globe With R

Published 2017-05-19 by Kevin Feasel

James Cheshire shows how to use R to create an image of a spinning globe:

It has been a long held dream of mine to create a spinning globe using nothing but R (I wish I was joking, but I’m not). Thanks to the brilliant mapmate package created by Matt Leonawicz and shed loads of computing power, today that dream became a reality. The globe below took 19 hours and 30 processors to produce from a relatively low resolution NASA black marble data, and so I accept R is not the best software to be using for this – but it’s amazing that you can do this in R at all!

Now all that is missing is a giant TV and an evil lair.

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Pretty R Plots

Published 2017-05-18 by Kevin Feasel

Simon Jackson has a couple posts on how to use ggplot2 to make graphs prettier.  First, histograms:

Time to jazz it up with colour! The method I’ll present was motivated by my answer to this StackOverflow question.

We can add colour by exploiting the way that ggplot2 stacks colour for different groups. Specifically, we fill the bars with the same variable (x) but cut into multiple categories:

Then he follows up with scatter plots:

Shape and size

There are many ways to tweak the shape and size of the points. Here’s the combination I settled on for this post:

There are some nice tricks here around transparency, color scheme, and gradients, making it a great series.  As a quick note, this color scheme in the histogram headliner photo does not work at all for people with red-green color-blindness.  Using a URL color filter like Toptal’s is quite helpful in discovering these sorts of issues.

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Narrative Custom Visual

Published 2017-05-11 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Narrative Power BI Custom Visual.  The Narrative visual is developed by Narrative Science and it gives you the ability to automatically deliver analysis of your data.  The results look similar to a final report that a analyst might provide after spending weeks with your data.

It’s not going to replace a seasoned analyst (or any analyst at all, frankly) but if you need a couple paragraphs of text summing up a trend, it’s a good start.

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Outliers In Histograms

Published 2017-04-27 by Kevin Feasel

Edwin Thoen has an interesting solution to a classic problem with histograms:

Two strategies that make the above into something more interpretable are taking the logarithm of the variable, or omitting the outliers. Both do not show the original distribution, however. Another way to go, is to create one bin for all the outlier values. This way we would see the original distribution where the density is the highest, while at the same time getting a feel for the number of outliers. A quick and dirty implementation of this would be

hist_data %>% 
  mutate(x_new = ifelse(x > 10, 10, x)) %>% 
  ggplot(aes(x_new)) +
  geom_histogram(binwidth = .1, col = "black", fill = "cornflowerblue")

Edwin then shows a nicer solution, so read the whole thing.

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Time Brush Custom Visual

Published 2017-04-26 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Time Brush Power BI Custom Visual.  The Time Brush gives you the ability both filter your report and see a graphics representation of your data at the same time. The name Time Brush comes from the behavior used when you select the values you’d like to filter.

The use of color is an interesting take on combining continuous data points with categorical representations of those points.

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Ignoring SSAS Dynamic Formatting

Published 2017-04-19 by Kevin Feasel

Chris Webb shows that tools like Power BI ignore formatting in SCOPE statements:

What’s more (and this is a bit strange) if you look at the DAX queries that are generated by Power BI to get data from the cube, they now request a new column to get the format string for the measure even though that format string isn’t used. Since it increases the amount of data returned by the query much larger, this extra column can have a negative impact on query performance if you’re bringing back large amounts of data.

There is no way of avoiding this problem at the moment, unfortunately. If you need to display formatted values in Power BI you will have to create a calculated measure that returns the value of your original measure, set the format string property on that calculated measure appropriately, and use that calculated measure in your Power BI reports instead:

Click through for more details and a workaround.

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