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Category: Visualization

Powershell Colors and Icons

Published 2022-09-13 by Kevin Feasel

Jeff Hill has fun with terminals:

If you have followed along with me before, chances are you know I like color. Color helps me zoom in on the information I want faster. Color can help me know if I have the right number of curly braces. Modern browsers and word processors show a red squiggly line when you misspell a word. Your eye is drawn to that bit of color that is different than the rest of the page. Keep reading and I’ll show you how to add some spice to your PowerShell experience.

I heartily endorse the mention of Windows Terminal later on. If you haven’t tried it before, it’s a worthy successor to CMD and the basic Powershell terminal.

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The Joy of Treemaps

Published 2022-09-08 by Kevin Feasel

Simon Rowe answers describes one of my favorite often-inappropriate visuals:

Dr Shneiderman developed the “treemap” in order to visualise this large amount of data—with multiple levels of folders and subfolders—in an efficient way, without taking up too much screen real estate. The treemap uses a series of nested rectangles, sized proportionally to the corresponding data value, to deliver an organised and multi-level view into any hierarchical data set.

Treemaps get misused a lot but are really valuable in specific scenarios. Click through to learn when (and when not) to use a treemap.

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Visualizing Delay Times on Subway Stations

Published 2022-09-08 by Kevin Feasel

Benjamin Smith looks for delays:

Any Torontonian who has commuted regularly on the TTC has probably experienced their fair share of delays on the subway. Having experienced a few recently I was inspired to visualize the average delay times across all stops on the subway. What are the stations with the longest delays on average this past year? Could we make a nice visual with it?

Click through for the end result as well as the process to get there.

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Creating a Parameterized Table in Tableau

Published 2022-09-02 by Kevin Feasel

Jordan Witcombe does some slicing and dicing:

Let’s say you’re planning to present a large table of information and you want the user to be able to filter based on the top or bottom X number of records. Due to the size of the dataset we would like the customer to have the ability to filter based on more than one column for their comparison. Making a large dataset much easier to digest.

Read on to see how you can accept user input in Tableau and simplify the viewable data.

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Trying out Shiny Python

Published 2022-08-25 by Kevin Feasel

Jamie Owen kicks the tires on Py-shiny:

We would posit (see what we did there) that R-{shiny} has been a boon for data science practitioners using the R language over the last decade. We know that in our Python work, we have certainly been clamouring for something of the same ilk. And whilst there are other frameworks that we also like, streamlit and dash to name a couple, neither of them has filled us with the same excitement and confidence that shiny did in R to build both simple and complex bespoke web applications. With RStudio Posit conf in action the big news from July 27th was the alpha release of Py-{shiny} which was a source of great interest for us, so we couldn’t resist installing and starting to build.

If you are familiar with R-shiny already, then much of the py-shiny package will feel familiar to you (albeit with a couple of things having been renamed). However we will approach the rest of this post assuming that a reader does not have that prior experience and take you through building a simple shiny application to display plots on subsets of a dataset.

I’m curious how much take-up there will be for the library, given that there are several good competitors on Python.

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Building a Google Analytics Dashboard using RShiny

Published 2022-08-23 by Kevin Feasel

Pascal Schmidt builds a Shiny dashboard:

I participated in the R Shiny 2021 contest and published an application similar to the Google Analytics dashboard app. For that, I used the Google Analytics API and the Google Search Console API to pull my own data from my blog directly into the application.

The application uses the shinyauthr library because the dashboard can be adjusted for each user who has their own username and password. On the first page, there are some visualizations for page views, devices used, etc. On the second page, there is a time-series model that tries to predict my page views two months in advance.

In particular, this post covers a couple of the foundational pieces, with future posts getting into more detail on other components. H/T R-Bloggers.

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Theming and Contrast Adjustments for Diffify

Published 2022-08-08 by Kevin Feasel

Tim Brock continues a series on theming diffify:

It’s difficult to design a website that is “just right” for everyone. For instance, while reds and greens can be difficult to discern for some dichromats and anomalous trichromats, most trichromats have no such problem (peak daylight sensitivity lies in the yellow part of the spectrum, between red and green). Moreover, these colours also have common cultural semantics (though these do, of course, vary by culture). We also care about aesthetics.

Because of this conflict and more besides, we decided the best approach to making the site more accessible was through “theming”. 

Click through to see what this entails.

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Apache eCharts for Python

Published 2022-08-05 by Kevin Feasel

Mark LItwintschik looks at another charting library:

The Apache eCharts project is a web-based charting library. It was started in 2013 and built using 77.5K lines of TypeScript. It is well documented and has over 200 examples of its API’s usage. The examples allow you to toggle between light/dark mode and there is a cheat sheet and a theme builder with several tasteful presents to choose from.

This is a library I hadn’t heard of before but Mark shows it off a bit.

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Visualizing Data in Python

Published 2022-08-03 by Kevin Feasel

Mark Litwintschik provides some recommendations:

There are two major phases of data analysis. The first is building up a basic understanding of a new dataset. Once this is done there is a second phase of understanding what’s changing over time and if there are any new outliers.

For the first phase, I find Tableau to be more productive than writing code in a Jupyter Notebook. For the second phase, I like to build periotic Airflow jobs that send charts and Excel files to operational channels on Slack. These are formatted to be mobile-friendly and allow me to do more of my work on a phone rather than being chained to a laptop. This also means access is controlled via Slack rather than a custom web app.

Mark also covers some examples with Altair.

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