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Category: Visualization

Using a Tree Map as a Legend in Power BI

Prathy Kamasani makes clever use of a tree map:

I recently worked on two projects where the client wanted to show multiple metrics sliced by the same categorical data. For example, seeing how various metrics are performing over different regions or different product groups. A use case like this can be achieved in many ways; probably the best approach is to use small multiples functionality or to keep it simple, five same visuals with different metrics.

Let’s look into it with energy consumption data. Here, I want to show metrics 1 to 5 on different income levels over the years.

I like this solution when you have multiple graphs off of the same base data, like in the small multiples scenario Prathy shows us.

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Building Custom ggplot2 Palettes

Nicola Rennie busts out the beret and fancy palette board:

Choosing which colours to use in a plot is an important design decision. A good choice of colour palette can highlight important aspects of your data, but a poor choice can make it impossible to interpret correctly. There are numerous colour palette R packages out there that are already compatible with {ggplot2}. For example, the {RColorBrewer} or {viridis} packages are both widely used.

If you regularly make plots at work, it’s great to have them be consistent with your company’s branding. Maybe you’re already doing this manually with the scale_colour_manual() function in {ggplot2} but it’s getting a bit tedious? Or maybe you just want your plots to look a little bit prettier? This blog post will show you how to make a basic colour palette that is compatible with {ggplot2}. It assumes you have some experience with {ggplot2} – you know your geoms from your aesthetics.

Click through to see how you can build a palette and use it across multiple ggplot2 charts.

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Creating Line Charts in Excel

Amy Esselman builds a line chart:

A line chart is a simple graph that is familiar to most audiences. Lines are great for showing continuous data, such as plotting how the value of something changes over time. In this post, we will cover how to create a line chart in Excel, using a sample dataset from a community exercise: table takeaways. The information is about an annual corporate fundraiser to provide meals to those in need. You can download the file here to follow along as we build the line chart. 

It might be that I’ve spent too much time in Power BI but creating charts in Excel seems a lot harder than it needs to be. This is especially true once you throw some unused columns into the mix.

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Visualizing SHAP Values in R with shapviz

Michael Mayer announces a new package:

SHAP (SHapley Additive exPlanations, Lundberg and Lee, 2017) is an ingenious way to study black box models. SHAP values decompose – as fair as possible – predictions into additive feature contributions.

When it comes to SHAP, the Python implementation is the de-facto standard. It not only offers many SHAP algorithms, but also provides beautiful plots. In R, the situation is a bit more confusing. Different packages contain implementations of SHAP algorithms

Read on to see how shapviz works, how to install it, and the types of visuals you can create from it.

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Editing a Legend in Excel

Mike Cisneros provides tips on how to edit chart legends in Excel:

One essential element of our charts and graphs rarely gets the attention it deserves: the legend. 

Without a clear and thoughtfully-incorporated legend, viewers of our data communications will struggle to understand exactly what we’re presenting to them. Any additional effort an audience needs to devote to solving the mystery of “which data series is green?” or “what’s the difference between square data markers and circles?” is energy they won’t have to put towards grasping your visual’s important insights. A well-designed legend will remove that cognitive burden. 

Click through for eight such tips.

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When to Use a Map Visual

Mick Cisneros explains when to use map visuals:

That ubiquity has given all of us an increased familiarity with maps, as well as a deeper affinity for them. (Probably a dependence as well!) It’s natural, then, to want to use a map to visualize data that has a geographic dimension. Why not, right? There is an obvious upside: audiences are drawn to the way they look, as it’s a more memorable image than the same old bar chart or line graph. Not to mention: it’s fun to make maps!

The problem is that maps look interesting, but their very nature limits our options for visualizing data within them. Per a recent paper by Franconeri, Padilla, Shaw, et. al., here are a couple of the comparisons that people are very good at making, perceptually:

Read on for a comparison of good map versus bad map. Just because something has a geographical component doesn’t mean you should map it.

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Square Area Charts

Simon Rowe fills in a grid:

A square area chart—also known as a waffle chart—is a square display.  They often consist of a 10×10 grid containing 100 individual squares. The squares are then coloured based on the data that’s being visualised. The chart gets its food-based name from its resemblance to a tasty waffle. (Sometimes this type of chart is referred to as a “unit chart,” although that term applies more appropriately to a broader category of visualisations, which tend to use discrete icons, rather than squares, to represent the values.)

Click through for several good use cases.

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Forcing Color Scheme by Data Element

Reza Rad forces a particular color scheme in Power BI:

You can set the color in every visual in Power BI easily. You can also set the color of multiple visuals at the same time using Themes. However, what if you want to set the same color for the same data point? For example, You want the Gender Female to be always colored Orange in all the charts and visuals. In Power BI, as of now, you cannot set a data point color. However, there is an easy solution for that, which I explained in this article and video.

Click through for the answer. Generally I’d say something along the lines of “instead of doing this, just have one color and take advantage of cross-filtering to highlight the element people care about.” But if you do have a multi-measure categorical set with a small number of categories, color can be a differentiator and at least this helps you keep consistent colors across visuals.

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Reviewing the Gartner BI Magic Quadrant

Matt Allington takes a look:

I update this blog article regularly to keep track of how Microsoft is progressing on its strategic journey in building a world class BI platform.  Gartner released the 2022 magic quadrant for Business Intelligence in March 2022.  As expected (by me at least), Microsoft is continuing its trail blazing and has maintained a clear lead over the other contenders in both ability to execute and completeness of vision.  I first wrote this blog article in 2019 to show the trend over time. Since then, I have been collecting the new Gartner magic quadrant images and I have combined them all (2015 – 2022) into an animated gif file (see below).   The visualisation was built using Power BI (what else) so it is very easy to see the changing nature of the competitive environment.  Note that Microsoft started behind back in 2015, and arguably hit the lead in 2018.

Also check out Matt’s video on how to translate spatial values to numbers so Power BI can plot them.

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Creating an Info Button in Power BI

Kristyna Hughes shows how to create an info tooltip in Power BI:

The steps below will walk through how to add an information icon to the report, making a tooltip page containing your additional information, and enabling the tooltip to allow users to hover over the icon and see the information.

This can be quite useful, especially as it gets context information out of the way of users after they don’t need it anymore. That’s important for dashboards you expect people to look at regularly.

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