Tag: datavisualization

OriginLab’s Graph Gallery: A blast from the past

OriginLab’s Graph Gallery: A blast from the past

Continuing my recent line of posts on data visualization resources, I found another repository in my inbox: OriginLab’s GraphGallery!

If I’m being honest, I would personally advice you to look at the dataviz project instead, if you haven’t heard of that one yet.

However, OriginLab might win in terms of sentiment. It has this nostalgic look of the ’90s, and apparently people really used it during that time. Nevertheless, despite looking old, the repo seems to be quite extensive, with nearly 400 different types of data visualizations:

Quantity isn’t everything though, as some of the 400 entries are disgustingly horrible:

There’s so much wrong with this graph…

What I do like about this OriginLab repo is that it has an option to sort its contents using a random order. This really facilitates discovery of new pearls:

Thanks to Maarten Lambrechts for sharing this resource on twitter a while back!

treevis.net – A Visual Bibliography of Tree Visualizations

treevis.net – A Visual Bibliography of Tree Visualizations

Last week I cohosted a professional learning course on data visualization at JADS. My fellow host was prof. Jack van Wijk, and together we organized an amazing workshop and poster event. Jack gave two lectures on data visualization theory and resources, and mentioned among others treevis.net, a resource I was unfamiliar with up until then.

treevis.net is a lot like the dataviz project in the sense that it is an extensive overview of different types of data visualizations. treevis is unique, however, in the sense that it is focused on specifically visualizations of hierarchical data: multi-level or nested data structures.

Hans-Jörg Schulz — professor of Computer Science at Aarhus University in Denmark — maintains the treevis repo. At the moment of writing, he has compiled over 300 different types of hierachical data visualizations and displays them on this website.

As an added bonus, the repo is interactive as there are several ways to filter and look for the visualization type that best fits your data and needs.

Most resources come with added links to the original authors and the original papers they were first published in, so this is truly a great resources for those interested in doing a deep dive into data visualization. Do have a look yourself!

7 Reasons You Should Use Dot Graphs, by Maarten Lambrechts

7 Reasons You Should Use Dot Graphs, by Maarten Lambrechts

In my data visualization courses, I often refer to the hierarchy of visual encoding proposed by Cleveland and McGill. In their 1984 paper, Cleveland and McGill proposed the table below, demonstrating to what extent different visual encodings of data allow readers of data visualizations to accurately assess differences between data values.

DOI: 10.2307/2288400

Since then, this table has been used and copied by many data visualization experts, and adapted to more visually appealing layouts. Like this one by Alberto Cairo, referred to in a blog by Maarten Lambrechts:

cleveland_mcgill_cairo
Via http://www.thefunctionalart.com/

Now, this brings me to the point of this current blog, in which I want to share an older post by Maarten Lambrechts. I came across Maarten’s post only yesterday, but it touches on many topics and content that I’ve covered earlier on my own website or during my courses. It’s mainly about the relative effectiveness and efficiency of using dots/points in data visualizations.

Basically, dots are often the most accurate and to the point (pun intended). With the latter, I mean in terms of inkt used, dots/points are more efficient than bars, or as Maarten says:

Points go beyond where lines and bars stop. Sounds weird, especially for those who remember from their math classes that a line is an infinite collection of points. But in visualization, points can do so much more then lines. Here are seven reasons why you should use more dot graphs, with some examples.

http://www.maartenlambrechts.com/2015/05/03/to-the-point-7-reasons-you-should-use-dot-graphs.html

Maarten touches on the research of Cleveland and McGill, on a PLOS article advocating avoiding bars for continuous data, and on how to redesign charts to make use of more efficiënt dot/point encodings. I really loved this one redesign example Maarten shares. Unfortunately, it is in Dutch, but both graphs show pretty much the same data, though the simpler one better communicates the main message.

Do have a look at the rest of Maarten’s original blog post. I love how he ends it with some practical advice: A nice lookup table for those looking how to efficiently use points/dots to represent their n-dimensional data:

  • For comparisons of a single dimension across many categories: 1-dimensional scatterplot.
  • For detecting of skewed or bimodal distributions in 2 variables: connect 1-dimensional scatterplots (slopegraphs)
  • For showing relationships between 2 variables: 2-dimensional scatterplots.
  • For representing 4-dimensional data (3 numeric, 1 categorical or 4 numerical): bubble charts. Can also be used for 3 numerical dimensions or 2 numeric and 1 categorical value.
  • For representing 4-dimensional data +  time: animated bubble chart (aka Rosling-graph)
Data Visualization Style Guide Repositories

Data Visualization Style Guide Repositories

Amy Cesal put together (1) this great overview of style guides for data visualization practice. Moreover, in the original tweet, Amy refers to other great repositories such as (2) this PolicyViz one and (3) this humongous one by Adele.

Amy’s list includes many references to the best practices used by some of the leading data journalism companies, such as the BBC, or professional data companies like Salesforce and IBM.

As I’m worried that this great repository may not stand the test of time on the current Google Docs location, here are the base URLs once more:

URL of guidelines Company name
https://sunlightfoundation.com/2014/03/12/datavizguide Sunlight Foundation
https://cfpb.github.io/design-manual/data-visualization/data-visualization.htmlConsumer Financial Protection Bureau
https://knightcenter.utexas.edu/mooc/file/tdmn_graphics.pdfDallas Morning News
https://urbaninstitute.github.io/graphics-styleguide/The Urban Institute
http://code.minnpost.com/minnpost-styles/MinnPost
https://public.tableau.com/profile/bbc.audiences#!/vizhome/BBCAudiencesTableauStyleGuide/HelloBBC Audiences
https://www.ibm.com/design/v1/language/experience/data-visualization/IBM
https://style.ons.gov.uk/category/data-visualisation/Office for National Statistics
https://www.ibcs.com/standardsInternational Business Communication Standards (IBCS®)
https://data.london.gov.uk/blog/city-intelligence-data-design-guidelines/London City Intelligence
https://www.bbc.co.uk/gel/guidelines/how-to-design-infographicsBBC
https://polaris.shopify.com/design/data-visualizationstShopify
https://ux.opower.com/opattern/how-to-charts.htmlOpower
https://www.consults-iot.comConsults-IoT.Com LLP
https://ux.mailchimp.com/patterns/dataMailChimp
https://material.io/design/communication/data-visualization.htmlGoogle- Material Design
https://lightningdesignsystem.com/guidelines/charts/Salesforce
https://github.com/glosophy/CatoDataVizGuidelines/blob/master/PocketStyleBook.pdfCato Institute
https://bbc.github.io/rcookbook/BBC
https://docs.microsoft.com/en-us/office/dev/add-ins/design/data-visualization-guidelinesMicrosoft
https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-visualization-for-human-perceptionACI

If you have any resources or style guides to contribute to Amy’s list, you can do so via this link.

Python for R users

Python for R users

Wanting to broaden your scope and learn a new programming language? This great workshop was given at EARL 2018 by Mango Solutions and helps R programmers transition into Python building on their existing R knowledge. The workshop includes exercises that introduce you to the key concepts of Python and some of its most powerful packages for data science, including numpy, pandas, sklearn, and seaborn.

Have a look at the associated workshop guide that walk you through the assignments, or at the github repo with all materials in Jupyter notebooks.

Recreating graphics from the  Fundamentals of Data Visualization

Recreating graphics from the Fundamentals of Data Visualization

Claus Wilke wrote the Fundamentals of Data Visualization – a great resource that’s definitely high on my list of recommended data visualization books.

In a recent post, Claus shared the link to a GitHub repository where he hosts some of the R programming code with which Claus made the graphics for his dataviz book. The repository is named practical ggplot2, after the R package Clause used to make many of his visuals.

Check it out, the page contains some pearls and the code behind them, which will help you learn to create fabulous visualizations yourself. Some examples:

Via https://htmlpreview.github.io/?https://github.com/clauswilke/practical_ggplot2/blob/master/health_status.html
Via https://htmlpreview.github.io/?https://github.com/clauswilke/practical_ggplot2/blob/master/corruption_human_development.html

Here’s the original tweet in case you want to see the responses.

Creating plots with custom icons for data points

Creating plots with custom icons for data points

Data visualizations that make smart use of icons have a way of conveying information that sticks. Dataviz professionals like Moritz Stefaner know this and use the practice in their daily work.

A recent #tidytuesday entry by Georgios Karamanis demonstrates how easy it is to integrate visual icons in your data figures when you write code in R. You can simply store the URL location of an icon as a data column, and map it to an aesthetic using the ggplot2::geom_image function.

Do have a closer look at Georgios’ github repository for week 21 of tidytuesday. You will probably have to alter the code a bit to get it to work. though!

For those who haven’t moved away from base R plotting functions yet, here’s a good StackOverflow item showing how to use icons in both base R and tidyverse.

Now that I think of it, the above probably uses the same methods that were used to make this amazing Game of Thrones map in R.