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Category: Visualization

Data Visualization in Python

Published 2022-03-28 by Kevin Feasel

Mehreen Saeed uses a few data visualization libraries in Python:

Data visualization is an important aspect of all AI and machine learning applications. You can gain key insights of your data through different graphical representations. In this tutorial, we’ll talk about a few options for data visualization in Python. We’ll use the MNIST dataset and the Tensorflow library for number crunching and data manipulation. To illustrate various methods for creating different types of graphs, we’ll use the Python’s graphing libraries namely matplotlib, Seaborn and Bokeh.

Bokeh results can look really nice, although it does feel like it requires a lot more developer time and effort to get it right. Click through for examples of each of the three libraries.

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Data Communication and Culture

Published 2022-03-24 by Kevin Feasel

Alex Velez notes one additional way that people may interpret your charts differently from your expectations:

Before I share some considerations for presenting data internationally, I want to acknowledge that I am not an expert on different cultures and audiences. In this post, I simply share some of my experiences with the hope that others will provide additional commentary for increased learning. If you have related thoughts, please share in the comments. 

Let’s consider five observations of regional differences I’ve encountered while communicating data.

Read on for those observations.

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Visualizing Networks of R Library Usage

Published 2022-03-18 by Kevin Feasel

Bryan Shalloway has fun with network plots:

In previous posts and threads I’ve alluded to the potential utility of visualizing the relationships between parsed functions/packages and files as a network plot.

I added the function network_plot() to funspotr. In this post I’ll simply output the network plots of the parsed-out packages from the code collections discussed in the prior two posts:

Click through for interactive plots of what different people in the R community use.

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The Right Amount of Detail on a Visual

Published 2022-03-11 by Kevin Feasel

Mike Cisneros answers a tricky question:

How much context, then, is necessary to include, so that we have an understandable (but un-cluttered) visual, presented with enough background information for the viewer to grasp its meaning, with the key insights and recommended actions emphasized?

When we’ve addressed this question in the past, we’ve relied on the always-true, if sometimes unsatisfying, response of, “It depends.” Every situation is unique, and there’s no checklist or scorecard you can use in every circumstance to ensure that you’ve hit the perfect amount of detail.

Read on for two factors upon which it depends.

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Making Visual Elements Glow in Excel

Published 2022-03-03 by Kevin Feasel

Vincent Granville does some marketing:

I explain here how to do it in Excel. But the principle is general. You can produce this type of visualization with other tools. There are many features in Excel that allow you to generate marketing-style pictures. I never really used them, as the result can be cheesy. If overused, you end up with material that looks like advertising from a Casino, or like the “old world wide web”, where blinking fonts and documents with neon colors were popular. But I recently decided to give it a try again, using extreme moderation. I believe my experiment was successful. I will leave it to the reader to have a final say about it.

It turns out a lot less gaudy than I originally imagined.

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Conditional Formatting with the New Power BI Desktop Formatting Pane

Published 2022-03-03 by Kevin Feasel

Gilbert Quevauvilliers puts a feature’s face on a milk carton:

I am sure everyone can agree that the new formatting pane is an awesome change.

But at the same time, I have found it a challenge to find settings with the new format pane.

In this blog post I will show you to find the conditional formatting which appears to have gone missing in the new format pane?

Click through to find out.

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All about Boxplots

Published 2022-02-28 by Kevin Feasel

Amy Esselman explains what a boxplot is:

The “box” part of a boxplot outlines the lower and upper quartiles. Inside the box is a line that indicates the median value. There are lines that extend outside the box—known as the whiskers—to depict the range of values in a given dataset. If there are outliers, then individual dots in line with the whiskers are plotted to denote the extreme values. 

Click through for a depiction of the plot as well as several alternative depictions which can include more information at the cost of added complexity.

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Plotting Multiple Columns on a Legend in Power BI

Published 2022-02-17 by Kevin Feasel

Jason Cockington has a workaround:

At a recent training course, one of the students asked if it was possible to add two different columns on the legend of a line chart, so that when a selection is made on a second slicer the chart splits to reveal multiple lines.

Given others in the class showed interest in the subsequent conversation, I decided to create a short blog so that everyone could benefit.

The short answer is “no” but the longer answer is more interesting.

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Visualizing High-Density Regions with R

Published 2022-02-16 by Kevin Feasel

The rOpenSci team covers the history of the gghdr package:

This was how being a newcomer to rOpenSci OzUnconf 2019 felt. It was incredible to be a part of such a diverse, welcoming and inclusive environment. I thought it would be fun to blog about how it all began, and the twists and turns we experienced along the way as we developed the gghdr package. The package provides tools for plotting highest density regions with ggplot2 and was inspired by the package hdrcde developed by Rob J Hyndman. The highest density region approach of summarizing a distribution is useful for analyzing multimodal distributions and can be composed of numerous disjoint subsets. For example, the histogram of the highway mileage (hwy) data from the mpg dataset (a) shows that cars with 6 cylinders (cyl) are bimodally distributed, which is reflected in the highest density region (HDR) boxplot (c) but not in the standard boxplot (b). Hence, we see that HDRs are useful in displaying multimodality in the distribution.

Read on for a short history of an interesting package.

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Verbalizing a Chart

Published 2022-02-16 by Kevin Feasel

Alex Velez reminds us of the spoken side of communication:

I’m confident that I could overcome some of these design challenges by effectively explaining the graph to someone else. Will it be a perfect data communication? No—but sometimes, we have to deal with less-than-ideal circumstances like time limitations, or not having control over our designs. Knowing how to verbalize a graph can be a practical solution when faced with these constraints.

I should caveat this by clarifying that my intention is not to say that we shouldn’t spend time on our visualizations. But too often, we focus only on the visual. We believe that a graph or a picture is worth a thousand words. Or maybe we assume that because we created the chart, we will automatically know how to talk through it. I am super guilty of this!

Read on for some tips on vocalizing a visual.

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