Category "Visualization"


[D3 in Depth]:

>D3 in Depth aims to bridge the gap between introductory tutorials/books and the official documentation.

I have found D3 extremely useful for creating dynamic plots on dashboard style websites for monitoring data pipelines. This looks an excellent resource for learning it.

via [FlowingData]

[D3 in Depth]: http://d3indepth.com “D3 in Depth”
[FlowingData]: http://flowingdata.com/2016/08/29/d3-in-depth/ “D3 in Depth”

[Colorgorical] is an alternative to [ColorBrewer] with a few different options for creating color tables. For example, you can add a couple specific colors that should be in the color table and let Colorgorical figure out the others which maximizes the perceptual difference between the colors. Colorgorical seems particularly well suited to generating qualitative color tables, e.g., to find sufficiently different colors for each line in a plot.

via [FlowingData].

[Colorgorical]: http://vrl.cs.brown.edu/color “Colorgorical”
[ColorBrewer]: http://colorbrewer2.org “ColorBrewer”
[FlowingData]: http://flowingdata.com/2016/08/22/colorgorical-generates-color-schemes-for-you/ “Colorgorical generates color schemes for you”

Motivated by the below chart of the age distribution of Olympic athletes, Junk Charts presents several techniques to [visualize multiple distributions]:

Age distribution of Olympic athletes

Candidates include the traditional boxplots used by statisticians as well variations and a stack of histograms. I think violin plots, suggested by a commenter, are a nice compromise showing the full distribution.

[visualize multiple distributions]: http://junkcharts.typepad.com/junk_charts/2016/08/various-ways-of-showing-distributions.html “Various ways of showing distributions”

[John Nelson] produced this beautiful map of how the boundaries of US droughts have changed over the last five years with data from the [US Drought Monitor]:

Link via [FlowingData].

[John Nelson]: https://twitter.com/John_M_Nelson “@John_M_Nelson”
[US Drought Monitor]: http://droughtmonitor.unl.edu “United States Drought Monitor”
[FlowingData]: http://flowingdata.com/2016/07/07/moving-drought-boundaries/ “Moving drought boundaries”

[Part 2] (of what promises to be a four part series) of the great comparison of Google Maps and Apple Maps by Justin O’Beirne. See [part 1] before starting with part 2.

[Part 2]: http://www.justinobeirne.com/essay/cartography-comparison-2 “Cartography Comparison: Google Maps & Apple Maps”

[part 1]: http://michaelgalloy.com/2016/05/12/google-maps-missing-cities.html “Google Maps’ missing cities”

This is an example of using a [clever color key] that doubles as a histogram showing the distribution of the corresponding areas.

By the way, this post is from a great [series] about small ways to make better visualizations.

[clever color key]: http://www.visualisingdata.com/2016/04/little-visualisation-design-part-14/ “Little of Visualization of Design: Part 14”

[series]: http://www.visualisingdata.com/2016/03/little-visualisation-design/ “All the ‘Little of Visualisation of Design'”

Nice example of [why rainbow color tables can be misleading]:

> Regular readers will be aware of the #endrainbow campaign to reduce the use of rainbow colour palettes in scientific figures. At the recent EGU conference, I gave a talk on ‘making better figures’, which included an example of a published conclusion which was incorrect due to the use of a rainbow colour scheme.

via [@asoconnor] via [@rsimmon]

[why rainbow color tables can be misleading]: http://www.climate-lab-book.ac.uk/2016/why-rainbow-colour-scales-can-be-misleading/ “Why rainbow colour scales can be misleading”
[@rsimmon]: https://twitter.com/rsimmon/status/725683215587926016 “Rob Simmon tweet”
[@asoconnor]: https://twitter.com/asoconnor/status/725708638497677312 “Amanda O’Connor tweet”

This is a [great article] about the change in balance between cities and roads in Google Maps between 2010 and 2016.

He also compares Google Maps versions to an old printed map:

> Even though it’s from the early 1960s, the old map is more balanced than the Google map.

There are a lot of visualization lessons to be learned from cartography.

[great article]: http://www.justinobeirne.com/essay/what-happened-to-google-maps “What Happened to Google Maps?”

This is a great idea to visualize the [punctuation in novels]:

> Inspired by a series of posters, I wondered what did my favorite books look like without words. Can you tell them apart or are they all a-mush? In fact, they can be quite distinct.

[punctuation in novels]: https://medium.com/@neuroecology/punctuation-in-novels-8f316d542ec4#.l9honlb9a “Punctuation in novels”

Robert Simmon [redesigns] a graphic:

> Creating scientific graphics can be difficult: most scientists and engineers lack training in design, deadlines are tight, compromises must be reached between team members and management, and the available tools may be limited. Fortunately, many design guidelines are simple and easy to execute, which I’ll show by re-designing the following graph, originally presented by NASA at the 2015 American Geophysical Union fall meeting.

I would not have removed the inset map of the region because I thought it added useful visual context to the exact lat/lon location in the subtitle, but maybe that is just my unfamiliarity with that area. I also wondered what the graphic would look like if it used a Brewer diverging color table for the colors in each band instead of red for all positive values and blue for all negative values.

[redesigns]: http://blogs.scientificamerican.com/sa-visual/data-visualization-advice-for-scientists “Data Visualization Advice for Scientists”

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