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Category: Visualization

Visual Principles

I have a post looking at three visual principles important to creating good dashboards:

In European languages, we read from left to right and from top to bottom.  In Middle Eastern languages like Hebrew and Arabic, we read from right to left and top to bottom.  In ancient Asian languages (particularly Chinese), we read from top to bottom and right to left, but in modern Chinese, we read left to right and top to bottom.  As far as Japanese goes, we read every which way because YOLO.  The way we read biases the way we look at things.

There has been quite a bit of research done on looking at where we look on a screen or on a page. I’m going to describe a few layouts, but focusing on research done on Europeans.  If you poll a group of Israeli or Saudi Arabian readers, flip the results.

Read the whole thing.  The second part of that comes out soon.

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Know Your Audience

I continue my series on dashboard visualization:

Before you build a dashboard, you have to know your audience.  If you don’t know who your viewers will be and where their interests lie, you run the risk of building a dashboard which fails to serve their needs.  When that happens, people stop looking at your dashboard.  In order to increase the likelihood that your dashboard will be useful, I have a few critical questions:

  1. Who is your intended audience?
  2. How will your intended audience use your dashboard?
  3. What actions do you want them to take as a result of what they see?
  4. Are you showing the right measures in the right way?
  5. What cultural differences might matter?

The rest of this post will drill into each of these concepts.

These are the types of questions which can make the difference between a dashboard people love and a dashboard people never use.

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What Is A Dashboard

I’ve started a new series on data visualization:

There are a few things which make dashboards useful:

  • Ideally, the dashboard is a “single pane of glass.”  By that, I mean that all relevant indicators are visible on the screen at the same time.  With my car, it’s close but no cigar:  I can see one of miles traveled, average fuel mileage, or current fuel mileage at a time.  If I want to see a different item, I need to hit a button on the steering wheel to scroll through those options.  By contrast, the TV show dashboard has everything on a single screen with no scrolling or switching required.

  • Key Performance Indicators (KPIs) are readily apparent.  For the TV show dashboard, we have a couple key metrics on display:  episode rating and number of votes as sourced from IMDB at the time I pulled those numbers.

  • Relevant KPIs are bunched together in a logical fashion.  On the top half of the dashboard, we see two visuals relating to average rating by show.  The bottom half show rating & user vote counts for the three highest-rated shows.

  • Layouts are consistent between dashboard elements and between related dashboards.  On the TV show dashboard, bars and columns use a single, consistent color.  Also, shows have thematic colors:  Daredevil in red, Jessica Jones blue, Punisher black, etc.  If I had a second dashboard for season two, I would want to use the same theme.

Read on for more details about what a dashboard is and some of the sundry forms of dashboards.

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Design Concepts: Affordances

Meagan Longoria continues her design concepts series:

Affordances allow us to take advantage of common experiences and cultural standards to create immediate understanding of our designs. Examples of affordances in physical products include:

  • A coffee cup with a handle suggests that you should grip the cup by the handle.

  • Buttons on doorbells are for pushing.

  • The material and shape of balls suggest they are for throwing and bouncing.

Read the whole thing.  If you want to learn more about affordances, Don Norman’s The Design of Everyday Things is a great starting point.

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ChartAccent Power BI Custom Visual

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the ChartAccent LineChart Custom Visual. This visual is a custom line chart that allows you to annotate individual data points, data series and ranges.

This visual runs the risk of getting very “noisy” but that can be fine if you’re building a presentation and want to build a somewhat complicated, annotated visual.

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Using The Squint Test

Meagan Longoria gives us the squint test:

While you can definitely perform the Squint Test on your report within Power BI Desktop, I recommend also testing in a browser once the report is deployed to PowerBI.com or to the Power BI Report Server portal since colors and objects may be slightly different there.

The Squint Test is also used in web page design, so web developers have made tools to aid them in this check. While just squinting at the page is perfectly sufficient, using a browser extension or another tool allows you to easily share your findings with others. In the Chrome Browser, there is a free extension called The Squint Test. This extension places an eye icon near the top right of the browser window. Clicking the icon provides a slider that allows you to increase or decrease the amount of blur applied to the page.

Meagan also has an example of applying this test and picks a dashboard where she can make some improvements, so check it out.

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The ggplot2 Books

Hadley Wickham has a couple of books which teach a lot about ggplot2.  The first book I’d recommend is his and Garrett Grolemund’s R For Data Science book, which is available for free online:

To map an aesthetic to a variable, associate the name of the aesthetic to the name of the variable inside aes(). ggplot2 will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as scaling. ggplot2 will also add a legend that explains which levels correspond to which values.

The colors reveal that many of the unusual points are two-seater cars. These cars don’t seem like hybrids, and are, in fact, sports cars! Sports cars have large engines like SUVs and pickup trucks, but small bodies like midsize and compact cars, which improves their gas mileage. In hindsight, these cars were unlikely to be hybrids since they have large engines.

Wickham also has the source to build his ggplot2 book online.  If you don’t want to build the source, you also have the option of buying the book.

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A Layered Grammar Of Graphics

Hadley Wickham describes some of the decisions he made when putting together ggplot2:

In the examples above, we have seen some of the components that make up a plot:
• data and aesthetic mappings,
• geometric objects,
• scales, and
• facet specification.
We have also touched on two other components:
• statistical transformations, and
• the coordinate system.
Together, the data, mappings, statistical transformation, and geometric object form a layer. A plot may have multiple layers, for example, when we overlay a scatterplot with a smoothed line.

This isn’t an article about how to use ggplot2; rather, it’s an article about implementation decisions.  To that end, I think it’s useful to see some of the logic behind ggplot2’s decisions.

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The Grammar Of Graphics

Leland Wilkinson has written the book on how we should write systems which visualize data:

This book was written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data. It presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. This foundation was designed for a distributed computing environment (Internet, Intranet, client-server), with special attention given to conserving computer code and system resources.

There’s no free copy of this book, and it’s a very expensive textbook. For most people, you’ll get more from derivative works, but if you’ve thought about putting together a graphics library, this is a must-read.

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Data Visualization For Social Science

I’ve started reading Kieran Healy’s book, Data Visualization For Social Science.  He has a free draft available online, and it automatically builds nightly so you’re seeing the latest version.  From the preface:

This book is a hands-on introduction to the principles and practice of looking at and presenting data using R and ggplot. R is a powerful, widely used, and freely available programming language for data analysis. You may be interested in exploring ggplot after having used R before, or be entirely new to both R and ggplot and just want to graph your data. I do not assume you have any prior knowledge of R.

After installing the software we need, we begin with an overview of some basic principles of visualization. We focus not just on the aesthetic aspects of good plots, but on how their effectiveness is rooted in the way we perceive properties like length, absolute and relative size, orientation, shape, and color. We then learn how to produce and refine plots using ggplot2, a powerful, versatile, and widely-used visualization library for R (Wickham 2016a). The ggplot2 library implements a “grammar of graphics” (Wilkinson 2005). This approach gives us a coherent way to produce visualizations by expressing relationships between the attributes of data and their graphical representation.

Through a series of worked examples, you will learn how to build plots piece by piece, beginning with scatterplots and summaries of single variables, then moving on to more complex graphics. Topics covered include plotting continuous and categorical variables, layering information on graphics; faceting grouped data to produce effective “small multiple” plots; transforming data to easily produce visual summaries on the graph such as trend lines, linear fits, error ranges, and boxplots; creating maps, and also some alternatives to maps worth considering when presenting country- or state-level data. We will also cover cases where we are not working directly with a dataset, but rather with estimates from a statistical model. From there, we will explore the process of refining plots to accomplish common tasks such as highlighting key features of the data, labeling particular items of interest, annotating plots, and changing their overall appearance. Finally we will examine some strategies for presenting graphical results in different formats, and to different sorts of audiences.

I’m less than halfway through the book so far, but it is quite an approachable look at the ggplot2 library with a bit of discussion on what makes for quality graphics.

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