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He’s Glad You Asked

By Kenneth Chang

• Nov. 3, 2014

While giving a physics talk for high school students five years ago at the Massachusetts Institute of Technology, Randall Munroe could tell that his audience was, in his words, “not totally with me.”

He was trying to explain potential energy and power — not complex concepts, but abstruse.

So, in the middle of his three-hour presentation, Mr. Munroe, who is best known as the creator of the Web comic xkcd , switched gears to “ Star Wars .”

“I thought about the scene in ‘ The Empire Strikes Back ’ when Yoda lifts the X-wing out of the swamp,” he said in an interview. “It occurred to me as I was lecturing.”

Instead of abstract definitions (an object lifted upward gains potential energy because it will accelerate if dropped; power is the rate of change in energy), Mr. Munroe asked a question: How much Force power can Yoda output?

“And so I did a rough version of the calculation on the fly in the classroom, looking up the craft dimensions and measuring things in the scene on the projector in front of them,” Mr. Munroe said. “They all perked up.”

For most people, physics is not interesting in itself. “The tools are only fun when the thing you’re using them on is interesting,” he said.

The students started asking other questions. “What about the end of ‘The Lord of the Rings’ when Sauron’s eye explodes ?” he recalled. “How much energy is that?”

The experience inspired Mr. Munroe to start soliciting similar questions from his xkcd readers .

Mr. Munroe has now collected that work, including a version of his Yoda calculations and new material, into a book, “What If?” which has been on the nonfiction best-seller list since it was published in September.

As its cover asserts, the book is full of “serious scientific answers to absurd hypothetical questions.”

“It exercises your imagination, and his dry wit is charming,” said William Sanford Nye, better known as “Bill Nye the Science Guy,” who shared the stage with Mr. Munroe last month at New York Comic Con. “He does, for lack of a better term, absurd scenarios, but they’re very instructive.”

Some examples: What would happen if you tried to hit a baseball pitched at 90 percent the speed of light? (“The answer turns out to be ‘a lot of things,' ” Mr. Munroe writes, “and they all happen very quickly, and it doesn’t end well for the batter [or the pitcher].”) If every person on Earth aimed a laser pointer at the moon at the same time, would it change color? (“Not if we used regular laser pointers.”) How long could a nuclear submarine last in orbit? (“The submarine would be fine, but the crew would be in trouble.”)

The explanations are accompanied by the same stick-figure drawings and nerdy wit that made xkcd popular. (What does xkcd mean? It is meaningless. The comic’s website helpfully explains, “It’s just a word with no phonetic pronunciation.”)

What If You Had a Hypothetical Question That Needed Answering?

As a child, Mr. Munroe also asked questions. In the book’s introduction, he recounts wondering if there were more hard things or soft things in the world. The conversation made such an impression on his mother that she wrote it down and saved it.

The young Mr. Munroe, 5 years old, concluded the world contained about three billion soft things and five billion hard things.

“They say there are no stupid questions,” Mr. Munroe, now 30, writes. “That’s obviously wrong; I think my question about hard and soft things, for example, is pretty stupid.

“But it turns out that trying to thoroughly answer a stupid question can take you to some pretty interesting places.”

In the interview, he also remembered asking questions and sharing tidbits of knowledge with his fifth-grade teacher. “And he said, ‘O.K., you have a lot of stuff to tell me, and that’s good, but have you heard of the concept of quality over quantity?' ” Mr. Munroe said.

While a physics major at Christopher Newport University in Virginia, Mr. Munroe started working as an independent contractor on a robotics project at the nearby NASA Langley Research Center , and he continued after graduating. During that time, he started scanning his doodles and posting them on the web.

By mutual decision, the NASA contract ended in 2006. Mr. Munroe became a full-time cartoonist and moved to the Boston area, because, he said, he wanted a bigger city with geekier things to do. In 2012, he added the “What If?” feature to the website.

Now, he said, he receives thousands of questions a week. Many are obviously students looking for help with homework. Others can be answered simply: No.

“One of them was ‘Is there any commercial scuba diving equipment that would allow you to survive under molten lava?' ” Mr. Munroe said. “No, there’s not. There’s nothing complicated about the answer to that. It’s exactly what you think.”

Once he chooses a question worth answering, he spends a day or two immersing himself in research, mostly on the web, and then writing. He rarely consults experts, as he is often working at 4 a.m. and “there’s no point in trying to call someone.”

For the Yoda problem, he later refined the calculations. He watched the scene, timing the 3.5 seconds it took the front landing strut to rise out of the water. In the original “Star Wars,” a crew member squeezes past an X-wing strut. From that, he estimated the height at 1.4 meters.

Mr. Munroe could not find a published figure for the mass of an X-wing, but there is a published length — 12.5 meters . He then took the specifications of an Air Force F-22 fighter — 19 meters and 19,700 kilograms — and scaled them down to estimate the X-wing mass at 5,600 kilograms.

That gave an answer of peak power output of 19.2 kilowatts, which Mr. Munroe noted, can power a block of suburban homes.

(Or in the imperial units still used in American newspapers, that would be 12,300 pounds rising at a rate of 1.3 feet a second out of the swamp, producing a peak power of 14,200 foot-pounds a second.)

As for the exploding eye at the end of “Lord of the Rings,” Mr. Munroe says he does not remember the answer that he and the class arrived at, but he remembers looking up the air-pressure spike that would knock over a person and the explosive energy needed to produce a pressure spike that forceful at various distances. (A federal publication, “ The Effects of Nuclear Weapons ,” helpfully provides the data.) Then they approximated the distance between Sauron’s tower, where the eye hovered, and the Black Gate, knocked over by the blast.

One of his favorite replies, which ends the book, is to a question about earthquakes: “What if a Richter magnitude 15 earthquake were to hit America at, let’s say, New York City? What about a Richter 20? 25?”

The short answer is that a magnitude 15 earthquake would destroy the planet. “That’s not all that interesting,” Mr. Munroe said.

Then he flipped the question around. The scale, which is logarithmic, can also describe smaller rumblings of zero or negative magnitude. Each step downward just means the amount of energy released diminishes a factor by one thirty-second.

A quake of magnitude zero would release one thirty-second the energy of a magnitude 1 quake. Mr. Munroe calculated that it would be like the Dallas Cowboys’ running full tilt into the side of a garage.

And a magnitude of minus 15 would be a mote of dust landing on a table.

After so many questions involving mayhem, death and destruction, Mr. Munroe said, “It’s nice to leave the world alone for once and leave it quiet.”

Scientific Publishing Is a Joke

An XKCD comic—and its many remixes—perfectly captures the absurdity of academic research.

A real scientific advance, like a successful date, needs both preparation and serendipity. As a tired, single medical student, I used to feel lucky when I managed two good dates in a row. But career scientists must continually create this kind of magic. Universities judge their research faculty not so much by the quality of their discoveries as by the number of papers they’ve placed in scholarly journals, and how prestigious those journals happen to be. Scientists joke (and complain) that this relentless pressure to pad their résumés often leads to flawed or unoriginal publications. So when Randall Munroe, the creator of the long-running webcomic XKCD , laid out this problem in a perfect cartoon last week, it captured the attention of scientists—and inspired many to create versions specific to their own disciplines. Together, these became a global, interdisciplinary conversation about the nature of modern research practices.

The cartoon is, like most XKCD comics, a simple back-and-white line drawing with a nerdy punch line. It depicts a taxonomy of the 12 “Types of Scientific Paper,” presented in a grid. “The immune system is at it again,” one paper’s title reads. “My colleague is wrong and I can finally prove it,” declares another. The gag reveals how research literature, when stripped of its jargon, is just as susceptible to repetition, triviality, pandering, and pettiness as other forms of communication. The cartoon’s childlike simplicity, though, seemed to offer cover for scientists to critique and celebrate their work at the same time.

The concept was intuitive—and infinitely remixable. Within a couple of days, the sociologist Kieran Healy had created a version of the grid for his field; its entries included “This seems very weird and bad but it’s perfectly rational when you’re poor,” and “I take a SOCIOLOGICAL approach, unlike SOME people.” Epidemiologists got on board too—“We don’t really have a clue what we’re doing: but here are some models!” Statisticians , perhaps unsurprisingly, also geeked out: “A new robust variance estimator that nobody needs.” (I don’t get it either.) You couldn’t keep the biologists away from the fun (“New microscope!! Yours is now obsolete”), and—in their usual fashion—the science journalists soon followed (“Readers love animals”). A doctoral student cobbled together a website to help users generate their own versions. We reached Peak Meme with the creation of a meta-meme outlining a taxonomy of academic-paper memes. At that point, the writer and internet activist Cory Doctorow lauded the collective project of producing these jokes as “an act of wry, insightful auto-ethnography—self-criticism wrapped in humor that tells a story.”

Put another way: The joke was on target. “The meme hits the right nerve,” says Vinay Prasad, an associate epidemiology professor and a prominent critic of medical research . “Many papers serve no purpose, advance no agenda, may not be correct, make no sense, and are poorly read. But they are required for promotion.” The scholarly literature in many fields is riddled with extraneous work; indeed, I’ve always been intrigued by the idea that this sorry outcome was more or less inevitable, given the incentives at play. Take a bunch of clever, ambitious people and tell them to get as many papers published as possible while still technically passing muster through peer review … and what do you think is going to happen? Of course the system gets gamed: The results from one experiment get sliced up into a dozen papers, statistics are massaged to produce more interesting results, and conclusions become exaggerated . The most prolific authors have found a way to publish more than one scientific paper a week. Those who can’t keep up might hire a paper mill to do (or fake) the work on their behalf.

In medicine, at least, the urgency of COVID-19 only made it easier to publish a lot of articles very quickly. The most prestigious journals— The New England Journal of Medicine , the Journal of the American Medical Association , and The Lancet —have traditionally reserved their limited space for large, expensive clinical trials. During the pandemic, though, they started rapidly accepting reports that described just a handful of patients. More than a few CVs were beefed up along the way. Scientists desperate to stay relevant began to shoehorn COVID-19 into otherwise unrelated research, says Saurabh Jha, an associate radiology professor and a deputy editor of the journal Academic Radiology .

A staggering 200,000 COVID-19 papers have already been published, of which just a tiny proportion will ever be read or put into practice. To be fair, it’s hard to know in advance which data will prove most useful during an unprecedented health crisis. But pandemic publishing has only served to exacerbate some well-established bad habits, Michael Johansen, a family-medicine physician and researcher who has criticized many studies as being of minimal value, told me. “COVID publications appear to be representative of the literature at large: a few really important papers and a whole bunch of stuff that isn’t or shouldn’t be read,” he said. Peer-reviewed results confirming that our vaccines really work, for example, could lead to millions of lives being saved. Data coming out of the United Kingdom’s nationwide RECOVERY trial have provided strong evidence for now-standard treatments such as dexamethasone. But that weird case report? Another modeling study trying to predict the unpredictable? They’re good for a news cycle, maybe, but not for real medical care. And some lousy studies have even undermined the treatment of COVID-19 patients ( hydroxychloroquine has entered the chat).

I should pause here to acknowledge that I’m a hypocrite. “Some thoughts on how everyone else is bad at research” is listed as one of the facetious article types in the original XKCD comic, yet here I am rehashing the same idea, with an internet-culture angle. Unfortunately, because The Atlantic isn’t included in scientific databases, publishing this piece will do nothing to advance my academic career. “Everyone recognizes it’s a hamster-in-a-wheel situation, and we are all hamsters,” says Anirban Maitra, a physician and scientific director at MD Anderson Cancer Center. (He created a version of the “12 Types” meme for my own beloved field: “A random pathology paper with the phrase ‘artificial intelligence’ in the title.”) Maitra has built a successful career by running in the publication wheel—his own bibliography now includes more than 300 publications —but he says he has no idea how to fix the system’s flaws. In fact, none of the scientists I talked with could think of a realistic solution. If science has become a punch line, then we haven’t yet figured out how to get rid of the setup.

While the XKCD comic can be read as critical of the scientific enterprise, part of its viral appeal is that it also conveys the joy that scientists feel in nerding out about their favorite topics. (“Hey, I found a trove of old records! They don’t turn out to be particularly useful, but still, cool!”) Publication metrics have become a sad stand-in for quality in academia, but maybe there’s a lesson in the fact that even a webcomic can arouse so much passion and collaboration across the scientific community. Surely there’s a better way to cultivate knowledge than today’s endless grid of black-and-white papers.

2085: arXiv

Explanation [ edit ].

arXiv is a free online repository of electronic preprints of scientific papers in various fields, particularly in physics, math, and computer science. Scientists typically publish "preprint" versions of journal articles to arXiv, which are free to publish to and read. In this comic Megan remarks that academic journals must have a hard time getting by since their primary revenue is from researchers who pay to publish articles and readers who pay for subscriptions. Her remark seems to assume that arXiv must be a recent development, perhaps similar to the Sci-Hub project which began in 2011. However, Ponytail informs her that the arXiv project has been around since the 1990s (1991 to be exact).

After a panel of Megan looking contemplative, she remarks that that does not make sense at all. After all, why would publishing companies be able to make money from something that is free online? Ponytail tries to stop her from freaking out, so that her outrage does not inform others about the current arrangement and thus ruin the system. She uses the term " jinx ", which in common usage means to affect negatively by speaking about, to imply that this system is one that could break down if discussed.

Ponytail expressing confusion about the continued existence of scientific journals previously happened in 2025: Peer Review .

The title text refers to another project that is invaluable for internet research, the Internet Archive ( link to it here ). Internet Archive is a public archive of information, including public domain books and music. Internet Archive runs the Wayback Machine , an archive of backups of web pages all over the Web at various times that can be used to see past versions of a page, even if that site has since shut down. Internet Archive accepts submissions of any type of information, including new backups of web pages and newly-made public domain content. The title text argues that these two projects are so useful, yet make so little economic sense, that, if they did not exist, we would dismiss them as ideas that would never be viable. In addition, as "arXiv" is intended to be pronounced the same as "archive", both sites have URLs with a common pronunciation.

Transcript [ edit ]

To be fair, the UI is so bad that that alone is barrier enough for downloading the pdf. :D Also, people might now fight me, because it's really easy if you know what to do. Fabian42 ( talk ) 19:03, 14 December 2018 (UTC)

It appears this comic may be referencing current events where academics are pushing for more open access publishing and publishers are balking. In particular, see this article in the December 13th issue of Inside Higher Ed . Some key quotes from the article:

162.158.106.96 20:43, 14 December 2018 (UTC)

Here is information on how preprints are different than peer-reviewed publications http://holly.witteman.ca/index.php/2017/12/11/getting-access-to-paywalled-papers/

Can someone deduce the field Ponytail is working on?

What fields are they taking about? Which have been most open to sites like arXiv and which have been most reluctant? 108.162.246.161 19:46, 14 December 2018 (UTC)

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What xkcd tells us about dependencies and NIH syndrome

NIH. Not invented here. In software development, this means a preference for in-house code. People have strong feelings about this, but let’s try to ground ourselves in some actual analysis. When does it make sense?

A third-party dependency is code you don’t have to write, but it introduces risk. Some people say “if you can replace a dependency with an afternoon of coding, do it!”.

Is that right? Let’s ask xkcd.

Here is a favorite, often-cited graphic on automation from xkcd/1205 :

The table tells you how long you should spend to automate a task based on how often you do the task and how much time automation saves you.

This also works for thinking about code dependencies.

Assume that our time horizon is five years, like in xkcd. Except, we’ll make these changes:

• ‘how often you do the task’ → ‘how often will the dependency break?'
• ‘how much time you shave off’ →  ‘how long will it take to fix?'

Let’s assume a dependency breaks on you once a year . Let’s assume it takes half a day to write a workaround, test it, code review it, and deploy. Looking at the ‘yearly’ column and the ‘6 hours’ row, that tells us that we should spend at most one day writing a replacement.

Of course, this conclusion assumes you write perfect code that won’t need to be touched again for five years . ( Isn’t all your code like that? )

The real world can’t be so simple, right?

The alternative to taking an external dependency is to build your own replacement. There’s a cost to build it and because we don’t usually write perfect code, there’s a risk that it will break and an associated cost to that, just like with a dependency.

A replacement only makes (economic) sense if the cost of failures in the replacement is less than the cost of failures in the dependency .

If so, then there is ‘budget’ for writing the replacement.

Understanding the difference in failure costs – and thus the ‘budget’ for a replacement – requires comparing the dependency and replacement along two dimensions:

• How often do failures happen?
• How much does it cost when they do?

Most people jusifiably focus on the first as it’s the easiest to affect. If you can reduce the failure rate enough, then spending days on a replacement is easy to justify . But there are cases where failure cost matters, particularly if a replacement is faster to deploy.

Assessing risk of failures 🔗︎

First, we’re probably overestimating the risk of a dependency . And that might skew whether we think a replacement can do better.

Cognitive biases are mental heuristics to give rapid decisions in the face of uncertainty . They’re great for keeping humanity alive in a hostile world. They’re also known to introduce severe and systematic errors. Two are particularly relevant:

Availability bias – weighting recent or memorable experiences more heavily.

Confirmation bias – selectively considering evidence to support a preconception.

We remember most clearly the times that dependencies blew up on us , when they exasperated us, and they form the set of cases that we think about when we assess how likely a dependency is to fail.

Many projects of any size have dependencies and transitive dependencies in the dozens or hundreds. We forget how many dependencies just work and we discount them from our assessment of the likelihood of a dependency going bad on us.

That leads to considering a ‘portfolio’ effect. The more dependencies we have, the more likely that we’ll experience a problem with a dependency in any given period of time . In other words, the odds that some dependency will be a problem are much higher than the odds of any particular dependency being a problem.

So when we’re deciding on the risk of failure of a dependency – and whether we can do better – we need to be honest in our assessment of the risk of that particular dependency, not dependencies in general.

Next, we need to answer this question: what gives us any confidence that the code we’d write to avoid a dependency is any better than the dependency ? Or what would lead us to the opposite conclusion? Here are some factors worth considering:

• complexity of the business domain
• dependency project team and activity
• scope of use
• size and complexity of the codebase

Some domains are relatively simple and others are fiendishly complex. For example, string padding is simple. On the other side, we have topics like time-zone-aware date math, Unicode collation algorithms, SOAP, the 13 RFCs that define the rules for MIME, and so on.

The simpler the topic, the more confidence we might have that the replacement will be at least no worse than the dependency. The more complex the topic, the more skeptical we should be. We need to ask ourselves honestly whether we have specialized knowledge that’s better than the maintainers of the dependency .

ESR said in The Cathedral and the Bazaar that “given enough eyeballs, all bugs are shallow”. A popular, active dependency project, with many users and multiple contributors, should be more reliable than one with a single maintainer, few users, or a long list of unaddressed issues and open pull requests. Will the replacement have more eyeballs than the dependency? If a replacement will be used by a lot of internal teams, it’s going to have more eyes on it than if it’s used by just one team.

However, these questions might not have the same weight if we don’t need everything a dependency is offering us. If our scope is a tiny subset of the full feature set, then the complexity of reimplementing that subset might be less than the whole. How much of a dependency do we really need?

If we assume that bug density per line of code will be relatively comparable between a replacement and a dependency, then one way that a replacement might arrive at a lower failure rate is by having a lot less code . Less code usually means less complexity as well, making finding and fixing problems easier. Can the replacement be a lot smaller and simpler than the dependency it replaces?

To sum up, if want replacement reliability to be better than dependency reliability, we should look for cases where some or all of the following are true:

• The business domain is simple
• The dependency is not widely used or is poorly maintained
• The replacement can implement a subset of features with smaller, simpler code.

Assessing cost of failures 🔗︎

When comparing cost of failure, we need to focus on two things: the time to fix the failure and the time to deploy the fix.

Why just focus on time factors? Because the actual incident cost of the downtime, error, or whatever, is going to be the same for a dependency or a replacement. The only thing that differs is how long things take.

The time to fix a bug should be relatively similar for a dependency and a replacement . In both cases, a problem has to be replicated and diagnosed. In my experience, that’s the hard part of most bugs – the actual fixes are usually simpler.

That might not be true if the dependency is in a different language. If the dependency is a C library and the application isn’t C or C++, then a replacement in the project language might be faster to fix because of engineer fluency in the project language and tooling.

It’s easier to imagine a difference in deploy time between a dependency and a replacement. Can our dependency manager trivially swap in a local fork of deep dependency ? If so, then we’re insulated from a dependency maintainer that is slow to fix bugs, apply pull requests and release new versions. In a sense, it’s a free option to shift from a dependency to a replacement on demand.

If we can’t deploy an updated dependency or a local patch quickly, we’re in trouble if the ongong incident cost of failure is also high. Perhaps our dependency manager isn’t sufficient. Maybe we’re in a regulated environment where updates to dependency require a security review. These situations lean towards a replacement over a dependency.

Bringing it all together 🔗︎

Should you write a replacement or not?

Again, you need to have a good theory for why a replacement will have a lower failure rate than a dependency . It a simple thing? Will it have less code?

If you don’t see an advantage in failure rate, do you have a theory for better time to fix and deploy? If not, just stop. You’re done. Use the dependency.

Assuming the long-term economics favor a replacement solution, only then is it time to figure out the acceptable build cost. To approximate that, we can go back to an xkcd-style matrix. But we need to make two changes: a longer timeframe and a larger range of costs.

From thinking about risk assessment and the portfolio effect, we know that failure rate for any single dependency will be lower, so this chart goes out to five years.

For cost of a failure, we can reduce everything down to the number of engineer-days to fix and deploy. Make sure to consider the cost of the downtime until the fix is deployed! (An incident that costs $2,000 a day in lost revenue is comparable to the cost of an extra engineer to work the problem.)

Then we compare the position on the chart for the dependency and the replacement. If the time factors are comparable, then the cost factors are similar and you’ll be on one row of the chart. Find the column for the replacement and the column for the dependency, then the difference will be the number of engineer-days you can afford to build the replacement. If you can build your own in that time budget and deliver the decreased failure rate, then do it!

But we can generalize further from patterns in the chart since the accuracy of our estimates is probably poor anyway:

If the cost is small, it’s probably worth spending half-a-day to a day.

If the cost is big, it’s probably worth spending days to weeks.

But only if you’re sure you can reduce the failure rate!

[As I was writing this article, xkcd released a new comic called, appropriately, Dependency .]

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Ever wanted to browse all xkcd comics on a certain topic, or see all comics with a certain character, in your quest to find the perfect xkcd for your situation? This site is for you.

The underlying data is from explainxkcd.com and xkcd.com .

The search & browsing experience is powered by Typesense which is a fast, open source typo-tolerant search-engine. It is an open source alternative to Algolia and an easier-to-use alternative to ElasticSearch.

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The blag of the webcomic

Color Survey Results

Who in the rainbow can draw the line where the violet tint ends and the orange tint begins? Distinctly we see the difference of the colors, but where exactly does the one first blendingly enter into the other? So with sanity and insanity. —Herman Melville, Billy Budd

Orange, red? I don’t know what to believe anymore! —Anonymous, Color Survey

I WILL EAT YOUR HEART WITH A FUCKING SPOON IF YOU AKS ANY MORE QUESTIONS ABOUT COLORS —Anonymous, Color Survey

Thank you so much for all the help on the color survey.  Over five million colors were named across 222,500 user sessions.  If you never got around to taking it, it’s too late to contribute any data, but if you want you can see how it worked and take it for fun here .

First, a few basic discoveries:

• If you ask people to name colors long enough, they go totally crazy.
• “Puke” and “vomit” are totally real colors.
• Colorblind people are more likely than non-colorblind people to type “fuck this” (or some variant) and quit in frustration.
• Indigo was totally just added to the rainbow so it would have 7 colors and make that “ROY G. BIV” acronym work, just like you always suspected. It should really be ROY GBP, with maybe a C or T thrown in there between G and B depending on how the spectrum was converted to RGB.
• A couple dozen people embedded SQL ‘drop table’ statements in the color names. Nice try, kids.
• Nobody can spell “fuchsia”.

Overall, the results were really cool and a lot of fun to analyze.  There are some basic limitations of this survey, which are discussed toward the bottom of this post.  But the sheer amount of data here is cool.

By a strange coincidence, the same night I first made the color survey public, the webcomic Doghouse Diaries put up this comic (which I altered slightly to fit in this blog, click for original):

It was funny, but I realized I could test whether it was accurate (as far as chromosomal sex goes, anyway, which we asked about because it’s tied to colorblindness) [Note: For more on this distinction, see my follow-up post ] . After the survey closed, I generated a version of the Doghouse Diaries comic with actual data, using the most frequent color name for the handful of colors in the survey closest to the ones in the comic:

Basically, women were slightly more liberal with the modifiers, but otherwise they generally agreed (and some of the differences may be sampling noise).  The results were similar across the survey—men and women tended on average to call colors the same names.

So I was feeling pretty good about equality.  Then I decided to calculate the ‘most masculine’ and ‘most feminine’ colors.  I was looking for the color names most disproportionately popular among each group; that is, the names that the most women came up with compared to the fewest men (or vice versa).

Here are the color names most disproportionately popular among women:

• Dusty Lavender
• Butter Yellow

Okay, pretty flowery, certainly.  Kind of an incense-bomb-set-off-in-a-Bed-Bath-&-Beyond vibe.  Well, let’s take a look at the other list.

Here are the color names most disproportionately popular among men:

I … that’s not my typo in #5—t he only actual color in the list really is a misspelling of “beige”.  And keep in mind, this is based on the number of unique people who answered the color, not the number of times they typed it.  This isn’t just the effect of a couple spammers. In fact, this is after the spamfilter.

I weep for my gender.  But, on to:

Here are RGB values for the first 48 out of about a thousand colors whose RGB values (across the average monitor, shown on a white background) I was able to pin down with a fairly high degree of precision:

The full table of 954 colors is  here , also available as a text file  here (I have no opinion about whether it should be used to build a new  X11 rgb.txt except that seems like the transition would be a huge headache.)

The RGB value for a name is based on the location in the RGB color space where there was the highest frequency of responses choosing that name.  This was tricky to calculate.  I tried simple geometric means (conceptually flawed), a brute force survey of all potential center points (too slow), and fitting kernel density functions (math is hard). In the end, I used the average of a bunch of runs of a stochastic hillclimbing algorithm.  For mostly boring notes on my data handling for this list, see the comments at the bottom of the  xkcd.com/color/rgb/ page.

Spelling and Spam

Spelling was an issue for a lot of users:

Now, you may notice that the correct spelling is missing.  This is because I can’t spell it either, and when running the analysis, used Google’s suggestion feature as a spellchecker:

A friend pointed out that to spell it right, you can think of it as “fuck-sia” (“fuch-sia”).

Misspellings aside, a lot of people spammed the database, but there were some decent filters in place.  I dropped out people who gave too many answers which weren’t colors used by many other people.  I also looked at the variation in hue; if people gave the same answer repeatedly for colors of wildly varying hue, I threw out all their results.  This mainly caught people who typed the same thing over and over.  Some were obviously using scripts; based on the filter’s certainty, the #1 spammer in the database was someone who named 2,400 colors—all with the same racial slur.

Here’s a map of color boundaries for a particular part of the RGB cube.  The data here comes from a portion of the survey (1.5 million results) which sampled only this region and showed the colors against both black and white backgrounds.

The data for this chart is  here (3.6 MB text file with each RGB triplet named).  Despite some requests, I’m not planning to make a poster of any of this, since it seems wrong to take advantage of all this volunteer effort for a profit; I just wanted to see what the results looked like.  You’re welcome to print one up yourself (huge copy here ), but keep in mind that print color spaces are different from monitor ones.

Basic Issues

Of course, there are basic issues with this color survey.  People are primed by the colors they saw previously, which adds overall noise and some biases to the data (although it all seemed to even out in the end).  Moreover, monitors vary; RGB is not an  absolute color space .  Fortunately, what I’m really interested in is what colors will look like on a typical monitors, so most of this data is across the sample of all non-colorblind users on all types of monitors (>90% LCD, roughly 6% CRT).

Color is a really fascinating topic, especially since we’re taught so many different and often contradictory ideas about rainbows, different primary colors, and frequencies of light. If you want to understand it better, you might try the neat introduction in Chapter 35 ofThe Feynman Lectures on Physics (Vol. 1), read Charles Poynton’s  Color FAQ , or just peruse links from  the Wikipedia article on color .  For the purposes of this survey, we’re working inside the RGB space of the average monitor, so this data is useful for picking and naming screen colors. And really, if you’re reading this blog, odds are you probably—like me—spend more time looking at a monitor than at the outdoors anyway.

Miscellaneous

Lastly, here are some assorted things people came up with while labeling colors:

Thank you so much to relsqui for writing the survey frontend, and to everyone else who sacrificed their eyeballs for this project.  If you have ideas and want to analyze these results further, I’ve posted the raw data as an SQLite dump here (84 MB .tar.gz file). It’s been anonymized, with IPs, URLs, and emails removed.  I also have GeoIP information; if you’d like to do geocorrelation of some kind, I’ll be providing a version of the data with basic region-level lat/long information (limited to protect privacy) sometime in the next few days. Note: The ColorDB data is the main survey.  The SatOnly data is the supplementary survey covering only the RGB faces in the map, and was presented on a half-black half-white background.)

And, of course, if you do anything fun with this data, I’d love to see the results—let me know at [email protected].

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1,287 replies on “color survey results”.

Interesting fuchsia is correct spelling, as it is named after Leonardo Fuchs, but fuschia is a very accepted misspelling. Just type either in and see the results. Now, I will have to remember the name and add ia to the end. Nonton Film Gratis

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Carolina Planning

Angles from the cpj since 1974.

What XKCD Can Teach You About Planning

XKCD is a beloved, online nerd comic that primarily involves esoteric jokes about physics and math with a healthy dose of snark. But that’s not all these humble stick figures provide. They can also offer valuable insight and lessons into some of the finer complications of planning: from big-picture issues, to niche problems. Therefore, through careful review and study, you can use XKCD comics to become a better planner. Here’s a quick tour of some of the top applications of XKCD to the planning field.

From the master planning perspective, XKCD can help you plan an execute a project from start to finish by realizing the importance of big-picture thinking:

And providing helpful tips on time management and organization, such as the best ways to start a project:

To budget your time:

And to make a schedule:

XKCD can also help you address niche problems that come up in core course class work, as well as harder issues that you address in topical classes. For example, XKCD provides ample advice on how to create and present helpful maps.

It can help you set up your GIS project:

And teach you how to add that all important design flair:

So that you can present your work in a convincing way:

XKCD has advice for planning specializations as well. Particularly, it has a lot of information on transit problems.

Such as, how to set up roads for everyone’s use:

And prepare for the advances of the future:

XKCD can also assist with your studies by translating abstract concepts to ‘real-world’ situations, like supply and demand and the tragedy of the commons:

Or the real reasons for considering the best years for Census data:

And, finally, it can help you connect with the public. Whether that is explaining your most recent research project:

Or the most recent disaster:

About the Author: Nora Schwaller is a first-year Ph.D. student in the Department of City and Regional Planning, where she focuses on disaster recovery. Prior to UNC, she worked for an architecture firm in San Francisco. Outside of class, Nora enjoys long bike rides and short walks, delicious food with good people, and casually perusing information on the design history of contemporary video games and systems.

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XKCD and the xkcd package

XKCD is a webcomic of romance, sarcasm, math, and language created by Randall Munroe.

This package tries to give a satisfactory answer to the question How can we make xkcd style graphs in R? . The xkcd package provides a set of functions for plotting data in a XKCD style using ggplot2 .

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Some examples of Scatterplots and Bar Charts:

Scatterplots

The development page is here .

Software Development

Update: It turns out the cannon has a motorized base, and can make holes just fine using the barrel itself as a battering ram. But due to design constraints it won't work without a projectile loaded in, so we still need those drills.