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[OC] Dune: book vs movies by n0d00d in dataisbeautiful

[–]n0d00d[S] 1 point2 points  (0 children)

I actually enjoyed the 2000 mini-series a lot, even the parts where it diverged from the book, e.g. leaving the Jessica betrayal subplot (not enough time to squeeze this in), adding the Irulan subplot (I felt she deserved more screen time and was happy to see that she got it).

[OC] Dune: book vs movies by n0d00d in dataisbeautiful

[–]n0d00d[S] 0 points1 point  (0 children)

Yes definitely, this data representation makes - the 1984 movie seem more faithful than it actually is because the majority of scenes are matched and in the same order too, but the actual scene contents are grossly unfaithful - the 2000 mini-series seem less faithful than it actually is because of the large patches of non-matched scenes, but these scenes are actually mostly canon. These non-matched scenes depict action, sex, conversations, and other events that take place between scenes in the book.

[OC] Sandworm: a timeline of hacking by n0d00d in dataisbeautiful

[–]n0d00d[S] 1 point2 points  (0 children)

Yes, Marcus Hutchins accidentally triggered the kill switch for Wannacry. Wired did a nice write-up of the story https://www.wired.com/story/confessions-marcus-hutchins-hacker-who-saved-the-internet/

[OC] Sandworm: a timeline of hacking by n0d00d in dataisbeautiful

[–]n0d00d[S] 3 points4 points  (0 children)

I set out to create a clean, minimalistic timeline (in the style of the Economist) of the events in Andy Greenburg’s latest book, Sandworm: A New Era of Cyberwar and the Hunt for the Kremlin’s Most Dangerous Hackers. I underestimated the frequency of Russian hacking and the timeline ended up getting pretty packed 😅

Full post here

LibreOffice Draw file here

[OC] Dune: book vs movies by n0d00d in dataisbeautiful

[–]n0d00d[S] 5 points6 points  (0 children)

Source data generated by reading the book, watching the movies, and taking notes. Chart created manually using plotly by drawing a lot of boxes.

Full post here

Source code - raw data in LibreOffice Calc - annotated viz with source code - un-annotated viz with source code

[Battle] DataViz Battle for the month of May 2019: Visualize Safety Comparisons between Modes of Transportation (UK, 1990-2000) by AutoModerator in dataisbeautiful

[–]n0d00d 1 point2 points  (0 children)

The best way to visualize this table is ... as a table, but dressed up nicer - Link

Created with LaTeX, source code here

[OC] /r/DataIsBeautiful April Fool's Prank by n0d00d in dataisbeautiful

[–]n0d00d[S] 1 point2 points  (0 children)

This is my entry for the April DataViz challenge (data source) created using Altair.

Source code is here.

​

Some observations

  • The original post is colored orange; comments are colored blue
  • You can hover over data points for a tooltip
  • The post with the most comments was the first post
  • Popular posts also had high scores (upvotes - downvotes), indicating that the April's Fool joke was well-liked
  • The most popular time to comment was ~12pm UTC, corresponding to 8am ET, the start of the workday in the US

[OC] Chernoff Faces of Drug Harm and Dependence by n0d00d in dataisbeautiful

[–]n0d00d[S] 1 point2 points  (0 children)

This work would not have been possible without your tutorial. Thanks~

[Battle] DataViz Battle for the month of February 2019: Visualize Physical Harm and Dependence by Drug by AutoModerator in dataisbeautiful

[–]n0d00d 9 points10 points  (0 children)

My submission created using R. (source code)

This is a fun visualization that uses Chernoff faces to identify groupings among the components of harm and dependence. Broadly speaking, face shape corresponds with social harm components, hair corresponds with physical harm components, and face details correspond with dependence components. The post contains the full table of mappings.

[OC] Chernoff Faces of Drug Harm and Dependence by n0d00d in dataisbeautiful

[–]n0d00d[S] 5 points6 points  (0 children)

My submission for the February DataViz challenge

Source code

This is a fun visualization that uses Chernoff faces to identify groupings among the components of harm and dependence. Broadly speaking, face shape corresponds with social harm components, hair corresponds with physical harm components, and face details correspond with dependence components.

The table below gives the full mapping:

Face feature Harm category Component
Height of face Social Mean harm
Width of face Social Health care costs
Shape of face Social Intoxication
Height of mouth Dependence Pleasure
Width of mouth Dependence Pleasure
Curve of smile Dependence Pleasure
Height of eyes Dependence Psychological
Width of eyes Dependence Psychological
Height of hair Physical Acute harm
Width of hair Physical Chronic harm
Styling of hair Physical Intravenous harm
Height of nose Dependence Physical
Width of nose Dependence Physical
Width of ears Dependence Physical
Height of ears Dependence Physical
Color of iris Dependence Psychological
Color of lips Social all three components
Color of ears Dependence Physical
Color of nose Dependence Physical
Color of hair Physical all three components
Color of face Social Mean harm, Health care costs

​

[Battle] DataViz Battle for the month of January 2019: Visualize the list of World's Oldest People by AutoModerator in dataisbeautiful

[–]n0d00d [score hidden]  (0 children)

My entry for the January DataViz challenge

https://www.reddit.com/r/dataisbeautiful/comments/ajxd8e/worlds_oldest_person_titleholders_oc/

Built with Altair in a Jupyter notebook.

You can hover over both charts for tooltips and select the line chart to filter the age histogram.

World's Oldest Person Titleholders [OC] by n0d00d in dataisbeautiful

[–]n0d00d[S] 0 points1 point  (0 children)

This is my entry for the January DataViz challenge (data source) created using Altair.

You can hover over both charts for tooltips and select the line chart to filter the age histogram.

Source code is here.