FlowingData rounds up his list of best visualization projects of 2016:

Visualization continues its merging into the everyday — less standalone and more of a medium that blends with words. I think this is partially because of a concentration on mobile. There’s simply less visual space on a phone than there is a giant computer screen, so the visualization is stripped or split up into smaller pieces that are more easily digested while scrolling.

For example, the Rhythm of Food shows the popular food by time of year:

And if that is not enough for you, here’s a roundup of 2016 visualization roundups.

Awesome Python:

A curated list of awesome Python frameworks, libraries, software and resources.

Worth checking out when looking for an existing solution.

National Geographic collects the best maps of 2016:

It’s been a good year for map lovers. Whether you’re into old maps, new maps, or new ways of interacting with old maps, there was much to cheer about in 2016.

via kottke.org

The NSF has opened up voting for the People’s Choice for visualizations in the Photo, Illustration, Poster/Graphic, Interactive, and Video categories.

Voting closes Sunday December 4 at 11:59 p.m. PST.

Upgrading from XQuartz from 2.7.9 to 2.7.11 on macOS Sierra broke IDL widgets for me:

~$ idl
IDL Version 8.5.1, Mac OS X (darwin x86_64 m64).
(c) 2015, Exelis Visual Information Solutions, Inc., a subsidiary of Harris

IDL> xloadct
Error: attempt to add non-widget child "dsm" to parent "idl" which supports
only widgets

The fix that worked for me were the following two commands:

sudo mv /opt/X11/lib/libXt.6.dylib{,.bak}
sudo cp /opt/X11/lib{/flat_namespace,}/libXt.6.dylib

Downgrading to 2.7.9 (but not 2.7.10) also worked for me.

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

I plot a lot of data on daily cycles, where there is no data collected at night. Let’s mock up some sample data with the following simple code:

IDL> x = [findgen(10), findgen(10) + 25, findgen(10) + 50]
IDL> seed = 0L
IDL> y = randomu(seed, 30)
IDL> plot, x, y

Then I get a plot like this:

This plot doesn’t show the nightly breaks in data well. Connecting the last data point collected from a day to the first data point collected the next day emphasizes the trend between these points, which may not be appropriate.

I have been using a fairly simple routine to insert NaNs into the data to break the plot into disconnected sections. For example, modify the above data for plotting with:

IDL> new_y = mg_insert_nan(x, y, [10.0, 35.0], new_x=new_x)
IDL> plot, new_x, new_y

The new plot shows the gaps between the “days” in the data:

James Hague writes:

Though my fascination with Forth is long behind me, I still tend toward minimalist programming, but not in the same, extreme, way. I’ve adopted a more modern approach to minimalism:

Use the highest-level language that’s a viable option.

Lean on the built-in features that do the most work.

Write as little code as possible.

I think this is good advance, but I would add one more point about having as few third party dependencies as possible that tries to balance the last point to write as little code as possible.

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.

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.

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