Factoids (and using R as a simple calculator)

Wikipedia defines factoid as “a questionable or spurious—unverified, incorrect, or fabricated—statement presented as a fact, but with no veracity.”

Last night I was enjoying a TV documentary series, The Story of Science, when I heard a startling factoid, namely:

If the “empty space” inside the atoms that make up people were removed, the entire human population would fit inside a sugar cube.

What the? Can we improve the veracity of this factoid?
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Why can’t PubMed or academic journals get the basics right?

A recent question at BioStar asked “Is the NAR database list available in a computer readable format?” The short answer is “no” and Pierre has done some excellent preliminary work to address the issue.

I’ve been working on a database and web application to check the associated URLs but quite frankly, this is tedious, a waste of everyone’s time and could be entirely avoided if the publishing industry did a better job. All that’s required is that either NAR or PubMed provide structured data – XML, Medline format, I don’t care what – containing a field that looks something like this:

URL    http://a.valid.url.goes.here

That way, we could all avoid writing regular expressions to detect URLs in abstracts. No wait – to detect broken URLs in abstracts. You would not believe how many of them look like this:

URL    http://www.amaze.ulb. ac.be/

Someone helpfully informed me via Twitter that this is “often a result of typesetting.” Thanks for that.

R 2.12 to 2.13 package upgrade

If you:

  • use Linux
  • have just upgraded your R installation from 2.12 to 2.13
  • installed some/all of your packages in your home area (e.g. ~/R/i486-pc-linux-gnu-library/2.12) and…
  • …are wondering why R can’t see them any more

just do this:

# at a shell prompt
cp -r ~/R/i486-pc-linux-gnu-library/2.12 ~/R/i486-pc-linux-gnu-library/2.13
# in R console
update.packages(checkBuilt=TRUE, ask=FALSE)
# back to the shell
rm -rf ~/R/i486-pc-linux-gnu-library/2.12

update: corrected a typo; of course you need “cp -r”

Fixing aberrant files using R and the shell: a case study

Once in a while, you embark on what looks like a simple computational procedure only to encounter frustration very early on. “I can’t even read my file into R!” you cry.

Step back, take a deep breath and take note of what the software is trying to tell you. Most times, you’ve just missed something very straightforward. Here’s an example.

Update: this post is not about how best to perform the task; it’s about how to cope with frustration. Please stop sending me your solutions :-)
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