I'm having trouble parsing the SO axis as well. Python (145K tags) and PHP (300K tags) are right next to each other around 90. R (19K tags) and Delphi (17K tags) are just slightly below around 80.
1) The "analysis" is just as broken as it was in September (the last time it was posted here).
The "popularity" of most of those languages is being grossly distorted when sogrady converts the "# of Tags" and "# of Projects" data to rankings.
The range in rank value for the stackoverflow tags was from 1 to 56, but the range in "# of Tags" that rank is based upon was from 0 to 82,923 and the data was so skewed that only 11 of 56 languages had above average "# of Tags".
Haskell was well below average for "# of Tags" and Java was well above average for "# of Tags" --
(The story was the same for the github "# of Projects" rank numbers.)
2) Which gives rise to this kind of bad-math "analysis" --
"Go jumping from #32 in 2010 to #30 today, a number that sounds modest but means that in that time it has improved more in popularity than Scala or Haskell and as much as Java, at least from a rankings standpoint (obviously growth becomes more difficult the more popular the language becomes)."
3) 90% of Tags were for just 10 languages.
50% of Tags were for just 3 languages.
The cumulative bottom 1% of Tags were for 31 different languages (including Haskell and Go).
the numbers for both axes are their respective rankings, not actual tag counts. their positioning, therefore, isn't directly proportional to the actual tag volume, but how they rank relative to one another. if we had actual numbers on the github side rather than just the rankings, we'd account for this by introducing a logarithmic scale, but we're constrained by what the data we have access to.
