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Category: Visualization

Adding Pagination to Bar Charts

Published 2024-02-13 by Kevin Feasel

Riqo Chaar turns the page:

Good User Experience (UX) design is crucial in enabling stakeholders to maximise the insights that they are able to derive from Power BI reports.  One common challenge of report design is effectively managing and displaying large datasets in bar charts without overwhelming the user. This article will describe the process behind a method that can mitigate this issue: adding pagination to bar chart visuals. This visual will provide the following functionality:

  • A number of categories filter: users can specify how many categories they would like to see per bar chart page
  • A page filter: users can navigate to different pages to see more categories

Click through to see how. I tend to prefer Power BI dashboards be glanceable, so pagination defeat that purpose to some extent. But so does having to scroll through a large list.

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Power BI Themes Gallery

Published 2024-01-29 by Kevin Feasel

Seth Bauer shares a tip:

Welcome to today’s tutorial where we’ll explore the Power BI Tips+ Theme Generator and its incredible features designed to streamline your Power BI report building experience. In this walkthrough, we’ll guide you through the process of getting started with the Power BI Tips+ Gallery, focusing on the Sunset theme. By the end of this tutorial, you’ll be able to effortlessly integrate our pre-configured Gallery Projects into your Power BI reports. It doesn’t get easier than this!

Click through for the process, including a video on how to do it.

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Fixing Low-Contrast Gradient Bar Charts in Power BI

Published 2024-01-24 by Kevin Feasel

Meagan Longoria looks at contrast:

Since conditional formatting was released for Power BI, I have seen countless examples of bar charts that have a gradient color fill. If you aren’t careful about the gradient colors (maybe you just used the default colors), you will end up with poor color contrast. Luckily there are a couple of quick (less than 30 seconds for most people to implement) fixes that can improve your color contrast.

Click through for a video demonstration and two tips from Meagan.

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Accessibility Guidelines for Apps and Visuals

Published 2024-01-16 by Kevin Feasel

Benedict Ampea-Badu shares some guidelines:

We live in the height of the digital age, where the digital space has become a thriving community, with every person craving a great yet personalized experience. In this era, there is one centralized truth with undeniable clarity: Accessibility is no longer a mere option; it is the cornerstone that will lead to the creation of a truly welcoming community.

In this second part of our series on accessibility design, we will discuss essential topics that lie at the heart of crafting accessible digital environments. We’ll begin by throwing more light on three of the five fundamental visual patterns vital to your designs:

  • Color Contrasting
  • Font Sizing
  • Labelling and Iconography

Read on for good information and plenty of examples.

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Visualizing Power BI Import Dependencies as a Graph

Published 2024-01-09 by Kevin Feasel

Chris Webb builds graphs, but not those types of graphs–the other type of graphs:

A few years ago a new pair of Profiler events was added for Power BI Import mode datasets (and indeed AAS models): the Job Graph events. I blogged about them here but they never got used by anyone because it was extremely difficult to extract useful data from them – you had to run a Profiler trace, save the trace file, run a Python script to generate a .dgml file, then open that file in Visual Studio – which was a shame because they contain a lot of really interesting, useful information. The good news is that with the release of Semantic Link in Fabric and the ability to run Profiler traces from a Fabric notebook it’s now much easier to access Job Graph data and in this blog post I’ll show you how.

Read on to see an example of it in action.

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Exploratory Data Analysis with F# and Plotly

Published 2023-12-26 by Kevin Feasel

Matt Eland is speaking my language (F#):

One of the most common tasks with data roles is the need to perform exploratory data analysis (EDA).

With EDA a data scientist, data analyst, or other data-oriented programmer can:

  • Understand the value distributions of their data
  • Identify outliers and data anomalies
  • Visualize correlations, trends, and relationships between multiple variables

Exploratory data analysis usually involves:

  1. Loading the data into a DataFrame
  2. Performing descriptive statistics to identify the raw shape of the data
  3. Visualizing variables of interest on their own or with other variables.

In this article I’ll walk you through the process of loading data from a sample dataset into a Microsoft.Data.Analysis DataFrame (the kind featured in ML.NET). Next, we’ll look at the descriptive statistics the DataFrame class provides and then explore the process of creating some simple visualizations with Plotly.NET.

Read on for the scenario and analysis.

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The Updated Stacked Bar Chart in Power BI

Published 2023-12-21 by Kevin Feasel

Tom Martens reviews an updated visual:

Personally, the stacked bar chart holds a special place in my heart when it comes to data visualization. It’s the tool I find myself using most frequently, which is why I decided to share a template using Deneb that I’ve been utilizing for a considerable amount of time: https://www.minceddata.info/2023/11/12/the-better-rectangular-pie-chart/

With the December 2023 release of Power BI Desktop, I can almost create the Deneb visual, which is fantastic as it eliminates the need for an additional custom visual. It’s important to note that while I’m a huge fan of Deneb, I also serve as the Power BI/Fabric sherpa in a large organization, and for this, I always try to reduce overall system complexity.

Click through for a fairly complex example of the visual.

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Data Modeling for Sankey Charts in Power BI

Published 2023-12-14 by Kevin Feasel

Marco Russo and Alberto Ferrari explain when Sankey charts can actually make sense:

Picture this: you manage a company that sells subscription services on the web, and you want to track the evolution of your customers by analyzing different events to understand how many customers start a trial before they purchase or how many renew or cancel their subscriptions.

The report should look like this: the darker flow indicates the number of customers who skipped the trial and went directly from a phone call to purchasing a subscription.

Read on for that sales funnel example and how you can prepare the data to make best use of Power BI’s Sankey chart visual.

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Building a Radar Chart in Power BI with SVG

Published 2023-12-06 by Kevin Feasel

Stephanie Bruno doesn’t need a built-in radar chart visual:

Radar (or spider) charts are a way to look at multiple metrics, perhaps with a different range of values for each metric, on a single chart. In this example, we’ll look at characteristics of Taylor Swift songs from a Spotify dataset (I have a daughter who still hasn’t forgiven me for not getting tickets to the Eras tour, so hopefully this will make up for it). A matrix with the radar SVG allows us to quickly compare these song characteristics (you can get the dataset and the descriptions of the characteristics here). There are existing radar/spider custom visual charts that are great, but none of them currently have a small multiple option, so we can’t use them to create the visual below, for example.

Click through to see the full example.

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The theme() Function in ggplot2

Published 2023-11-30 by Kevin Feasel

Jack Kennedy shows off a function:

The theme() function in {ggplot2} is awesome. Although it’s only one function, it gives you so much control over your final plot. theme() allows us to generate a consistent, in-house style for our graphics, modify the text within our plots and more. Getting comfortable with theme() will really take your {ggplot2} skills up a notch.

Theming visuals can have an outsized impact on how easy the output is to understand, so understanding how theme() works is important. Also, if your company has specific theming or marketing standards, you can usually build them with the theme() function and then save that theme for reuse later.

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