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Category: Visualization

Displaying SSRS Usage Stats Through Grafana

Alessandro Alpi takes queries to view SQL Server Reporting Services data and visualize it in Grafana:

One of the problems that often occur in our organization as well as some of our customers, is to get immediate feedback about usage statistics of reports. Usually, the request of creating reports is out of control and some of them are executed only “that time” and not anymore. In the worst-case scenario, many of them aren’t executed at all and some of them could become even overlapped or duplicated.

Therefore, it is important to know the usage statistics, user by user and report by report, to make the reader aware of them, let him interpreting the values of the same query in multiple ways and graphical layouts. While this is not possible with a tabular format (unless you export the values using any external tools such as Excel) it is simpler when it comes to a dashboard.

And that’s where Grafana excels.

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Using Trellis Charts to Display Small Multiples Over Time

Mike Cisneros shows us the evolution of three-point shooting in the NBA using a trellis chart:

 This small multiple chart shows two variables for each team in the league for each of the last 30 seasons: on the x-axis, the number of 3-pointers attempted per game; on the y-axis, the percent of attempted 3-point shots that were successful. Each point is a single team in a single season. The individual panels step you forward in time as the data changes and evolves. They help you see how the pack of all NBA teams is inexorably moving towards more and more 3-point attempts per game (the data points shift rightwards as you progress through the frames). We can also see that there are no longer any teams with sub-30% shooting percentages on those attempts (illustrated by tighter clustering upwards as you move forward in time).

This is a good way of showing movement over time in a static medium, like a printed page. If you’re giving a presentation, this would probably be a bubble chart with a play axis.

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Using the Power BI Color Picker

David Eldersveld walks us through the Power BI color picker:

The new color picker allows colors in RGB format in addition to the hex color format that Power BI has used exclusively until now.

The new one also easily allows users to choose from a wider selection of shades and tones. This builds upon the simpler selection of hues and tints in the original.

In case you don’t know what David means, there is an excellent explanation of each term.

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Data Visualization in R and Python

Michelle Golchert contrasts libraries for visualizing data in R and Python:

Unlike R, Python – as a “general-purpose” programming language – does not include data visualization tools by default. However, Python also provides many libraries for this purpose, such as Matplotlib and Seaborn.

Python now also offers numerous packages (like plotnine and ggpy) which are equivalents of ggplot2 in R, and allow you to create plots in Python according to the same “Grammar of Graphics” principle.

This is an area where I think R has the upper hand at most levels: it’s easier to get started plotting with R (thanks to the built-in plots), it’s easier to do “intermediate-quality” plots (stuff you would use in an internal presentation), and you tend to have more control when building professional-quality plots. You can certainly create beautiful visuals in both languages, though.

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Conditional Formatting Line and Area Charts with Power BI

Soheil Bakkshi shows how we can conditionally format line and area charts with Power BI:

One of my customers asked me to show time series in line charts and area charts. But she want’s it to be conditionally formatted based on the average value over time. Let’s keep it simple, she wants to show “Sales by Year Month” in line chart, but, highlight the data points that are below “Average Sales per Year Month”. As you may know, we currently do not have the luxury of formatting line charts and area charts. But wait, this post is all about that. Let’s dig into it.

From the above scenario, you perhaps already guessed that we need to create a measure which defines the colour based on “Average Sales per Year Month” to be able to format the chart conditionally. If any data point is below the “Average Sales per Year Month” then we highlight it in Orange, if it is above the “Average Sales per Year Month” then we stick to the default colour.

Let’s do it.

This is definitely not straightforward, but once you see the process, it’s pretty neat.

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Designing a Layered Donut Chart in Power BI

Prathy Kamasani shares how to build a layered donut chart in Power BI:

You can view the full report here – https://prathy.com/portfolio/school-uk/

The tricky bit of any good data visualisation is finding interesting data, inspiration and story. In my report, my story was to show the comparison of Ofsted school ratings among the total number of schools. I also wanted to carry my story between report pages with colours.

So in this particular visual, I wanted to make it visually appealing, so I decided to show two metrics on each donut visual, the measure I want and the total number of schools. Then I resized each visual in a way so that they look like one visual. Those days there were no grouping, but now we can group them as one visual too. Also, when I first designed this report, default Power BI donut chart didn’t have an option to resize the ring, so I went for Circular gauge by MAQ software. Now we can use the default Donut chart too.

Click through for more details.

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Counting Tidyverse Package Arguments

Theo Roe has fun figuring out which tidyverse packages have the greatest number of available arguments in functions:

Before we start anything, I’d like to mention that most of the hard work came from nsaunders and his great blog post Idle thoughts lead to R internals: how to count function arguments.

Let’s get started.

The aim of this blog is to capture the number of arguments present in each function with packages of the tidyverse

Click through to see the code, as well as some methods of visualizing the results (methods which you can use in other situations).

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Custom Power BI Visuals with Charticulator

Kasper de Jonge shows how we can use Charticulator to build out custom visuals in Power BI:

Recently I attended an excellent session by Andy Kirk on the state of data visualization for 2019. One of the tools Andy is most excited about is called Charticulator. For those of you that haven’t heard about it, Charticulator is an open source project from Microsoft Research. Using a web UI you can design almost any charts by interactively specifying constraints (NO code).

The best news is that you can use these visuals directly in Power BI. You can even use your own data coming from the PBI data model. This came as news for many at Andy Kirk’s session so decided to do a quick post on it. Building very custom charts entices a lot of data artists but it is often hard to bring this to main stream BI product. The ease of the Charticulator and Power BI integration brings this type of data visualizations to a much broader audience.

This is a lot easier than telling people to learn D3, though Charticulator will necessarily have more limitations than writing all of the code yourself.

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Realistic-Looking Islands with R

Holger K. von Jouanne-Diedrich uses fractal math to create realistic-looking artificial islands:

Here we will turn this principle on its head and use it to actually create realistic-looking landmasses with R. The inspiration for this came from chapter 4 “Infinite Detail” of the book “Math Bytes” by my colleague Professor T. Chartier from Davidson College in North Carolina.

The idea is to start with some very simple form, like a square, and add more detail step-by-step. Concretely, we go through every midpoint of our ever more complex polygon and shift it by a random amount. Because the polygon will be getting more and more intricate we have to adjust the absolute amount by which we shift the respective midpoints. 

Click through for the code.

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Using PowerPoint to Create Power BI Layouts

Jon Fletcher has a good tip for snazzing up a Power BI dashboard:

First question, why bother with layouts?
Using layouts in Power BI allows a user to make their visuals stand out better, the page looks professional and more appealing to its audience.

Second question, why PowerPoint?
The default page size in Power BI desktop is 16:9, (this trick doesn’t work for other Power BI page sizes), which is identical to a PowerPoint slide.
Therefore whatever is designed in PowerPoint will fit onto a Power BI page perfectly. Also PowerPoint is very easy to use; most people are familiar with it.

Click through for an example. It’s easy to go overboard with this, but Jon does a good job of using a muted color so that the edges don’t overwhelm your eyes. I might knock it down a shade or two further from that, but regardless, this is a nice tip.

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