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Category: Visualization

Building a Pareto Chart in Power BI

Published 2024-04-22 by Kevin Feasel

Riqo Chaar get more than 80% of the way there:

The Pareto principle, commonly referred to as the 80/20 rule, is a concept of prioritisation.

It states that for many outcomes, 80% of the outputs are derived from 20% of the inputs. Although this isn’t a universal truth, this pattern has been observed in many different cases. For example, a large proportion (80%) of the revenue a particular business generates may primarily be associated with only a small proportion (20%) of big-selling products. This concept is related to the law of diminishing returns and poses the following question: If, after reaching a certain level of output (80%), significantly more effort is required to achieve further increases in this output, is this additional effort worth it?

In this article, we demonstrate the process (using DAX expressions) of creating a Pareto chart in Power BI.

Read on to see how you can build a chart like this. It’s a very popular style of chart for manufacturing scenarios.

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The Value of Multiple Graphs

Published 2024-04-10 by Kevin Feasel

Alex Velez shares some advice:

A tip I regularly share when providing data visualization feedback is to use multiple graphs instead of packing several series into a single chart. Although it is important to be concise, people are often surprised to hear that when it comes to the number of graphs we share, fewer isn’t always better.

Read on for the advice. It makes a lot of sense for several reasons, as Alex shares. One additional reason is that it goes toward Edward Tufte’s argument about information density: multiple smaller visuals allow you to put more relevant information into the same amount of physical space. This makes your visuals more impactful as a result.

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Accessibility Features in Power BI

Published 2024-04-08 by Kevin Feasel

Elena Drakulevska takes us through some of the accessibility features in Power BI:

As data professionals, we’re constantly striving to create reports that effectively communicate insights to our audience. Did you know that within Power BI, there are hidden accessibility features that can enhance the usability for all users—with minimal effort on your part? Today, we’re digging into this overlooked aspect of Power BI and exploring how these features can elevate your data visualization projects.

Click through for three features.

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The Power of the (Scatter) Plot

Published 2024-04-05 by Kevin Feasel

Kurt Buhler digs into scatter plots:

In many reports, it is common to find large tables or matrixes with a lot of information. Business users come to these reports to get the details they need to answer questions, make decisions, and take actions. However, these detailed tables can be inefficient, requiring users sort, drilldown, and filter them to find actionable data.

Consider the following example of a matrix to analyze gaps in year-over-year growth by product, and dissect those by region. In this scenario, the intended purpose of the report is for a sales team to analyze why we did not hit our revenue growth target.

Read on to learn what you can do with a scatter plot, either natively in Power BI or via custom visual.

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Tips for Dealing with Large Spatial Datasets

Published 2024-04-05 by Kevin Feasel

Rhian Davies consults the map:

I love playing with spatial data. Perhaps because I enjoy exploring the outdoors, or because I spend hours playing Geoguessr, or maybe it’s just because maps are pretty but there’s nothing more fun than tinkering with location data.

However, reading in spatial data, especially large data sets can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and breaking large data sets into more manageable chunks.

Click through for three tips when dealing with spatial data. The code is in R but the tips make sense in any language.

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ggbrick in CRAN

Published 2024-03-22 by Kevin Feasel

Dan Oehm notes another brick in the wall:

If you’re looking for something a little different, ggbrick creates a ‘waffle’ style chart with the aesthetic of a brick wall. The usage is similar to geom_col where you supply counts as the height of the bar and a fill for a stacked bar. Each whole brick represents 1 unit. Two half bricks equal one whole brick.

It has been available on Git for a while, but recently I’ve made some changes and it now has CRAN’s tick of approval.

Click through to see how you can use it. This style of waffle chart, in the right scenario, can be quite useful, providing a high-level view and also giving you some idea of fine-grained magnitudes. H/T R-Bloggers.

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Ways to Use Sort Order in Bar Charts

Published 2024-03-21 by Kevin Feasel

Mike Cisneros demands order:

When you’re visualizing categorical data, sorting the bars in your chart is usually a straightforward task. Or is it?

In most cases, you probably take the category with the largest value and stick that in the prime spot, the leftmost slot on the horizontal axis. Then, you proceed from left to right in descending order of value. Easy peasy.

But it’s not always that simple, as Mike points out.

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