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Category: Visualization

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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Building an App with Streamlit

Published 2023-11-28 by Kevin Feasel

Riqo Chaar demonstrates Streamlit:

Off-the-shelf solutions for interactive data app development such as Microsoft Power BI are great – they allow users to easily develop data apps using a GUI. However, Power BI’s ease of use comes at the expense of reduced functionality. This is where programming languages such as Python, JavaScript or C# shine – you can practically code anything you like!

This blog will focus on Streamlit as a means of building interactive data apps. Streamlit is an open-source Python library that enables rapid creation of web apps (including, but not limited to, data apps) with minimal code. It acts as an intermediary between the easy-to-use, but functionally-limited characteristics of Power BI and the functionally-enhanced, but difficult-to-use characteristics of other programming tools such as JavaScript or C#.

I’ve grown to like Streamlit a lot. It’s really simple to put together a good-looking page, similar to Shiny in R.

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Fixing a Double Donut Chart

Published 2023-11-21 by Kevin Feasel

Amy Esselman drags a donut chart:

Here we have not one, but two donuts! It takes a while to figure out what this data represents. Based on the title, it appears that we’re meant to compare the categories of the donuts across the two charts. With the sections in different places, this is rather challenging. 

One may argue that the colors and donut form make the data more visually interesting; however, this often hinders our ability to understand the data. Let’s look at some strategies to make this information easier to comprehend.

Read on for advice around converting two donut charts into something humans can more easily interpret.

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Simulating a Bivariate Normal Distribution in R

Published 2023-11-10 by Kevin Feasel

Steven Sanderson isn’t content with a univariate normal distribution:

Imagine two variables, like height and weight, that exhibit a joint distribution. The bivariate normal distribution captures the relationship between these variables, describing how their values tend to cluster around certain means and how they vary together. It’s like a two-dimensional bell curve, where the peak represents the most likely combination of values for both variables.

Click through to learn a bit more about bivariate normal distributions, including ways to plot one and show its density function.

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Plotting a Cumulative Distribution Function in R

Published 2023-11-09 by Kevin Feasel

Steven Sanderson builds a plot:

Before delving into the world of R programming, let’s first grasp the fundamental concept of a CDF. Imagine a group of students eagerly awaiting their exam results. The CDF for their scores would depict the probability of encountering a student with a score less than or equal to a specific value. For instance, if the CDF indicates a value of 0.7 at 80%, it implies that there’s a 70% chance of finding a student with a score of 80 or lower.

Read on to see how you can calculate this in a dataset and then plot the CDF.

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