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Category: Visualization

The Problem with Stacked Bar Graphs

Rita Fainshtein looks at a stacked bar graph:

Let’s look at what motivated our choice and whether it is indeed a graph that will convey accurate messages to those who read it.

Here are some reasons why this type of visualization is preferred:

1. The graph on the right is one of the “recommended” graphs provided by tools (both Excel and Power bi).

2. It seems logical: there is a height comparison between case managers, clear separation between client types and the graph looks colorful and appealing.

But behind this, there are some major problems with the visual. Read on to learn what those are and for one alternative visual which can be better.

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Alt Text in R

Nicola Rennie looks at different ways to incorporate alt text in R-based images:

Alt text (short for alternative text) is text that describes the appearance and purpose of an image. Alt text has multiple purposes, the main one being that it aids visually impaired users to better understand your content when the alt text is read aloud by screen readers. Alt text is also used in place of an image if it fails to load, which means that users with poor internet connection are more likely to be able to engage with your content.

The ggplot2 example was an interesting one, as I hadn’t ever added alt text to an image there.

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Building a Shiny App to Show Star Maps

Benjamin Smith builds a UI:

Recently, I released a R package called starBliss that aimed to replicate the output of a e-commerce site called MapsForMoments – a site which lets users order custom prints of the night sky on the date of their choosing (usually a special occasion such as a birthday, first date, wedding etc.) and allows them to choose a style, and add some custom text. It was a great experience getting to build the package which replicated the MapsForMoments product and I was shocked to see how well it was received when I posted about it- with the Github receiving over 30 stars at the time of writing this blog!

I decided to take this to the next level by trying to build a similar UI in shiny which allows the user to create a custom star map and not need to use the R console. In this blog I share my experience constructing and showcase the “free alternative” to MapsForMoments – starBlissGUI!

Click through to see how you can run the app, as well as a sample output.

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Customizing Shiny Apps with shinydashboard

Mandy Norrbo isn’t satisfied with the defaults:

Using {shinydashboard} is great for creating dashboard prototypes with a header-sidebar-body layout. You can quickly mock up a professional looking dashboard containing a variety of outputs, including plots and tables.

However, after a while, you’ll probably have had enough of the “50 shades of blue” default theme. Or, you might have been asked to to follow company branding guidelines, so you need to replace the default colours with custom ones.

Click through for a walkthrough of what is available for customization and how to do it.

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Tracking Home Heating Oil Prices in R

Steven Sanderson charts some prices:

If you live in New York and rely on heating oil to keep your home warm during the colder months, you know how important it is to keep track of heating oil prices. Fortunately, with a bit of R code, you can easily access the latest heating oil prices in New York.

The code uses the {dplyr} package to clean and manipulate the data, as well as the {timetk} package to plot the time series.

Read on for an overview of what the code does, followed by the code itself and a time series plot at the end.

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AR and VR in Data

Corrinna Peters differentiates augmented reality from virtual reality:

Virtual Realty (VR) and Augmented Reality (AR) is everywhere, with a broad variety of applications across many industries, and the potential to revolutionise many others. The potential of VR and AR technology is endless and drives digital transformation. Lots of market research studies are projecting that VR and AR is forecasted to grow exponentially in the next few years. With this in mind, the questions people are starting to ask themselves are – what does this mean for me? What does this mean for my business? How will this change data and analytics? What are the differences?

In the medium term, I am quite pessimistic on the topic. There are specific use cases where virtual reality can be interesting, such as a virtual house walk-through. But for the most part, the problem with VR is that optical quality is still not good enough, meaning that a lot of people struggle to use a VR headset for more than an hour or so before getting nauseous. There are also problems with the lack of tactile sensation (and haptic feedback can only go so far) and ergonomic challenges when you’re constantly raising your arms to perform actions.

Augmented reality has an easier sell, though, in cases where you’re willing to hold a phone or tablet up against something. For this scenario, think museum pieces, where you hold the phone up and get more information about the piece, artist, and style. Google does have AR for walking directions, with the cost of burning a whole bunch of battery life. But the general failure of HoloLens and wearable AR devices, as well as the inherent privacy concerns from flashing your active camera around crowded areas, dampen the mood a bit for AR.

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Visualizing PyTorch Models

Adrian Tam describes a model:

PyTorch is a deep learning library. You can build very sophisticated deep learning models with PyTorch. However, there are times you want to have a graphical representation of your model architecture. In this post, you will learn:

  • How to save your PyTorch model in an exchange format
  • How to use Netron to create a graphical representation.

Click through for the article, which is mostly about training the PyTorch model. Visualizing it turns out to be pretty easy with the right tool.

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Thoughts on the New Power BI Accessible Themes

Meagan Longoria is moderately pleased:

Everyone’s vision is a little different. It is rare (impossible?) that a color theme is accessible for everyone. For instance, while many people with color vision deficiency have trouble distinguishing red and green hues, others have trouble distinguishing blue hues. So when we optimize to accommodate one condition, we may make things more difficult for another condition. This happened with the change in accent color in Power BI Desktop from yellow to teal. Changing to teal increased color contrast, which was great for people with low vision, but it caused new issues for some people with color vision deficiency.

While I am very happy to see these new color themes, I hope everyone understands that they aren’t just generally accessible for all uses. As mentioned in the blog post, they specifically have better color contrast to achieve a contrast of at least 3:1, which is the contrast recommended by WCAG for non-text content.

Read the whole thing. There’s a delicate balancing act between having a complete color scheme and satisfying a variety of needs. It sounds like this theme doesn’t quite cut it, though hopefully there will be some improvements in the future.

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Calculating Log Likelihood Ratios with jeva

Peter M.B. Cahusac takes us through a jamovi package:

Ever wanted to try doing an evidential analysis? You may have found it difficult to find a statistical platform to do it. Now there is the jamovi module jeva which can provide log likelihood ratios for a range of common statistical tests.

Imagine for a moment that we wish to carry out a statistical test on our sample of data. We do not want to know whether the procedure we routinely use gives us the correct answer with a specified error rate (such as the Type I error) – the frequentist approach. Nor do we want to concern ourselves with possible a priori probabilities of hypotheses being true – the Bayesian approach. We need to know whether a statistic from this particular set of data is consistent with one or more hypothetical values. Also, let’s say that we weren’t happy with how much data we had collected (a familiar problem?), and just added more when convenient. Welcome to the likelihood (or evidential) approach!

Read on for an explanation and how to try jeva out.

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