Press "Enter" to skip to content

Category: Visualization

Azure Data Explorer Web Updates

Published 2022-07-05 by Kevin Feasel

Michal Bar has a few updates to the Azure Data Explorer web tool:

We are focused on continuously improving the results exploration experience in ADX web UI, to make it easy and intuitive. Our goal is to provide an easy-to-use UI so that you will not be required to re-write KQL queries in order to perform light-weight data exploration.

Click through to see how you can search and filter within the results pane (something I’d like to see in other Microsoft data platform tools like SSMS), create series panels on charts from KQL, and more.

Comments closed

Stacked Bar Charts

Published 2022-07-01 by Kevin Feasel

Alex Velez takes us through stacked bar charts:

A few years ago, we posted a question on this blog that is as relevant today as it was years ago: “Is there a good use case for a stacked bar chart?” 

Stacked bars are everywhere; you’ve likely seen them in a recent report, a dashboard, or in the media. Despite their prevalence, they are commonly both misused and misunderstood. In this guide, we’ll aim to rectify these mishaps by sharing examples, clarifying when you should (and shouldn’t) use a stacked bar chart, and discussing best practices for stacking bars. 

Read on for plenty of good advice around when to use stacked (either regular stacked bar charts or 100% stacked), horizontal vs vertical, and how to format them when it does make sense to drop one in.

Comments closed

Finding Key Influencers with Power BI

Published 2022-06-30 by Kevin Feasel

Gauri Mahajan looks at the key influencers visual in Power BI:

Once the Key Influencers are added to the Power BI report, it would look as shown below. The visual would be empty by default. The key areas that are required to make this visual works are Explain section and Analyze By section. The Analyze section is used to point to the variables or attributes that we intend to analyze. The Explain By section is used to point to the variables or attributes that may be influencing the attributes specified in the Analyze section.

I’ve found this visual to be pretty interesting if you have a good dataset.

Comments closed

Power BI Smart Narratives

Published 2022-06-29 by Kevin Feasel

Gauri Mahajan shapes the narrative:

To make it easier for the end-user, this job may be done by report or business analysts who may pre-analyze the reports, manually form textual narratives that summarize the key highlights in the report. While it solves the challenge in question, it opens a possibility of analysts’ bias getting introduced in the report, and the end-user may or may not agree with the narrative. Some systems solve this issue by employing complex machine learning / natural language processing / other artificial intelligence-based mechanisms to auto-generate smart textual narratives that summarizes the key highlights of the data. Though this approach works, it requires a significant number of resources and hard-to-find skills which is outside the bounds of a normal end-user who may want to use a reporting tool in a self-service manner and build a dashboard.

Modern reporting solutions like Tableau, AWS QuickSight, Microsoft Power BI, and others in similar league have been offering a feature to generate key insights using built-in AI/ML in the reporting tool which enables an end-user to extract insights as well as enables a report developer to have a smart visual that auto-updates the insights based on the change in the data.

In practice, this ends up being more of a fun toy than a really practical solution. Part of the issue is that decent analysis is hard, even more so when you have to develop something before even seeing the data or having any priors around feature importance.

Comments closed

Using a Tree Map as a Legend in Power BI

Published 2022-06-28 by Kevin Feasel

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.

Comments closed

Building Custom ggplot2 Palettes

Published 2022-06-24 by Kevin Feasel

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.

Comments closed

Creating Line Charts in Excel

Published 2022-06-24 by Kevin Feasel

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.

Comments closed

Visualizing SHAP Values in R with shapviz

Published 2022-06-14 by Kevin Feasel

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.

Comments closed

Editing a Legend in Excel

Published 2022-06-13 by Kevin Feasel

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.

Comments closed

When to Use a Map Visual

Published 2022-05-30 by Kevin Feasel

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.

Comments closed