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Category: Visualization

Superheat

Published 2017-02-06 by Kevin Feasel

David Smith shows off a very cool heatmap package called superheat:

While the superheat pacakge uses the ggplot2 package internally, it doesn’t itself follow the grammar of graphics paradigm: the function is more like a traditional base R graphics function with a couple of dozen options, and it creates a plot directly rather than returning a ggplot2 object that can be further customized. But as long as the options cover your heatmap needs (and that’s likely), you should find it a useful tool next time you need to represent data on a grid.

The superheat package apparently works with any R version after 3.1 (and I can confirm it works on the most recent, R 3.3.2). This arXiv paper provides some details and several case studies, and you can find more examples here. Check out the vignette for detailed usage instructions, and download it from its GitHub repository linked below.

Click through for some great-looking examples.

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R Visuals In Power BI

Published 2017-01-25 by Kevin Feasel

Ryan Wade ties ggplot2 visuals into Power BI:

The package that we are going to use to develop our custom visualization is ggplot2. The ggplot2 package is arguably the most popular data visualization package in R. It is based on the “grammar of graphics” concept that was created by the statistician, Leland Wilkinson. The ggplot2 package allows you to approach creating charts and graphs in the same manner that Bob Ross approached painting trees in the forest. With ggplot2 you are able to start with a blank canvas and add layers upon layers via short code snippets that builds on each other until you end up with the desired visualization.

The pbix file that is being used in this blog can be found here: http://bit.ly/2jwoCyP. The GentleIntroToR_ChartExample.pbix file contains an example of using R to create a box plot chart that shows the distribution of player scores for the L.A. Lakers. Chiclet slicers were added that allows you to filter by division and/or opponent. The R visualization was created in four steps.

Check out the PBIX file.

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Animating Visuals In R

Published 2017-01-17 by Kevin Feasel

Tomaz Kastrun shows how to create animated charts in R using ggplot2:

In addition to R code, the ImageMagic program needs to be installed on your machine, as well. Also the speed, quality and many other parameters can be set, when creating animated gif.

Animated gif can be also included into your SSRS report, your Sharepoint site or any other site – like my blog 🙂 and it will stay interactive. In Power BI, importing animated gif as a picture, unfortunately will not work.

Be very careful with this, as not everything supports animated GIFs and you can make some really painful graphs if you try hard enough…

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Sankey Bar Charts

Published 2017-01-12 by Kevin Feasel

Devin Knight continues his custom visuals series:

In this module you will learn how to use the Sankey Bar Chart Power BI Custom Visual.  The Sankey Bar Chart is used to show a flow of data between different stages of a process.

It’s an interesting mix of sankey, bar chart, and funnel.  In other words, you may only have one thing you can use it for, but it’ll be a really good use.

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Sorting By Column In Power BI

Published 2017-01-12 by Kevin Feasel

Reza Rad explains how to sort a column by another column’s value in Power BI visuals:

Problem happens when you want a Text field to be ordered based on something different than the value of the field. For example if you look at above chart you can see that months ordered from April to September. This is not order of months, this is alphabetical order. If you change the sorting of visual, it will only change it from A to Z, or Z to A. To make it in the order of month numbers you have to do it differently.

Read on for the solution.

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7 Visualizations In R

Published 2016-12-30 by Kevin Feasel

Dikesh Jariwala provides sample R code for seven common visualizations:

In your day-to-day activities, you’ll come across the below listed 7 charts most of the time.

  1. Scatter Plot
  2. Histogram
  3. Bar & Stack Bar Chart
  4. Box Plot
  5. Area Chart
  6. Heat Map
  7. Correlogram

We’ll use ‘Big Mart data’ example as shown below to understand how to create visualizations in R. You can download the full dataset from here.

That’s a nice set of visuals, covering a broad swath of potential visualization scenarios.

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Horizontal Funnel

Published 2016-12-30 by Kevin Feasel

Devin Knight shows off the horizontal funnel Power BI custom visual:

In this module you will learn how to use the Horizontal Funnel Power BI Custom Visual.  The Horizontal Funnel functions somewhat similar to the traditional funnel but it allows you to display a secondary measure and has a few more customizations than you would normally get. You’ll find that the Horizontal Funnel is great for displaying a flow of data.

One of the better non-sales uses of funnels I’ve seen is tracking completion rates on multi-page forms or multi-step processes.  If you see a huge drop-off at one step in the process, it might indicate a bug in the form or some incongruity with the end user’s expectation.

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Linear Gauge Custom Visual

Published 2016-12-22 by Kevin Feasel

Devin Knight shows off the linear gauge custom visual in Power BI:

In this module you will learn how to use the Linear Gauge Power BI Custom Visual.  The Linear Gauge would often be used to visualize a KPI. It gives you the ability to compare an actual vs target as well as showing up to two trend lines.

This can be a very useful visual.  The tricky part is that the bars aren’t scaled the same, so when your eyes want to compare bar lengths, it can get a little confusing.

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Dial Gauge

Published 2016-12-15 by Kevin Feasel

Devin Knight explains the dial gauge custom visual:

  • The effectiveness of gauges on dashboards is an often debated topic.

  • The Dial Gauge is completely data driven. Which means not only must your measure (drives the needle) come from a dataset but also the different thresholds ranges must come from your dataset too.

  • There are no specific Format settings for the Dial Gauge, which does limit you a bit with what you can do with this gauge.

There are certain scenarios in which I think the dial gauge works well.  The best scenario is the the same as its analog counterpart:  when you are measuring a single continuous variable with a safe range and meaningful range differences.  This scenario occurs less often than you might think.

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