Algorithm
There's a lot of content there, so I can't read through every one. Thus, I need an automated way to get it. Luckily, there's a python package called PRAW that makes it easier to mine data on reddit. For the mining then, I used the following algorithm:
- Get all posts for a day
- Filter those posts by [OC] posts as those are most likely to have specified the tools used
- Filter the comments of those posts by comments made by the post author since those are the ones that might specify the language
- Search those comments for the list of languages that seem most likely and track which ones are found
- Repeat for the next day in the list
I also did a few other things (track score of submission, track date of submission, etc.) for additional analyses. This algorithm isn't perfect in that it can miss situations where languages were specified and don't trip my filters (e.g., a typo would kill this) and many posts don't specify languages in the first place. It should work as a pretty good estimator though.
Most used language
First, we need to know how many posts identified languages used. I ran this against the last two years of data, and found that there were 7845 posts flagged [OC], and the algorithm above identified the language(s) used in 4189 of them.
Next, we can do a simple comparison of languages usages vs posts that specified languages, and that's where you get the post at the beginning (% = 100*(posts specifying this language/posts specifying any language)):
Note that the numbers above add up to >100% because some posts specified multiple languages (511 of the 4189 posts with identifiable languages).
And that's the original goal. It's clear that Excel wins by a landslide. I guess it makes sense because almost everyone can use Excel and it's really quick to get plots out. Python dominating MATLAB surprised me at first but makes sense in retrospect since MATLAB is not free and has fewer users (it's just really great for working with data).
That looks odd. We can't assume post scores have a normal distribution though, so another test is using medians:
That's a huge disparity between median and average. How weird is the distribution? A histogram with logarithmic bins yields:
That is much clearer to me. One interesting thing is that it spikes up in the 3 to 10 thousand score range, so I'm guessing that's when a post makes it to the front page maybe? An idea then is to look at the score distributions by language:
It's pretty clear from this that excel is more bottom heavy than some of the others. A huge number of posts with a score of 0 used it, and it has very few posts with extremely high scores, especially considering that it is the most popular language/tool for this. It looks like MATLAB and Adobe tools have the highest percentage of high-scoring posts, but they have so few samples it's hard to know. Among the popular languages/tools, Python and R appear to do best.
A final way to answer what languages/tools are most likely to yield a high score is to see what percentage of posts using the language/tool yield a score above 100:
Next, we can do a simple comparison of languages usages vs posts that specified languages, and that's where you get the post at the beginning (% = 100*(posts specifying this language/posts specifying any language)):
Note that the numbers above add up to >100% because some posts specified multiple languages (511 of the 4189 posts with identifiable languages).
And that's the original goal. It's clear that Excel wins by a landslide. I guess it makes sense because almost everyone can use Excel and it's really quick to get plots out. Python dominating MATLAB surprised me at first but makes sense in retrospect since MATLAB is not free and has fewer users (it's just really great for working with data).
Most valuable language
To make it interesting, I wanted to see if any languages predicted more success on reddit. I tried doing that a few different ways. A simple one is to get the average score per post per language:That looks odd. We can't assume post scores have a normal distribution though, so another test is using medians:
That is much clearer to me. One interesting thing is that it spikes up in the 3 to 10 thousand score range, so I'm guessing that's when a post makes it to the front page maybe? An idea then is to look at the score distributions by language:
It's pretty clear from this that excel is more bottom heavy than some of the others. A huge number of posts with a score of 0 used it, and it has very few posts with extremely high scores, especially considering that it is the most popular language/tool for this. It looks like MATLAB and Adobe tools have the highest percentage of high-scoring posts, but they have so few samples it's hard to know. Among the popular languages/tools, Python and R appear to do best.
A final way to answer what languages/tools are most likely to yield a high score is to see what percentage of posts using the language/tool yield a score above 100:
This just reinforces the takeaways from the histograms (it's basically the same information in a different form) and I'll stop there...
Probable biases in this data
I would guess that the following occurred to some degree:
- some languages are probably more prone to typos...e.g., maybe a lot of people typed 'Tablaeu instead of 'Tableau'...if that's the case, those languages would be undercounted by my crude algorithm
- a lot of OC posts don't specify the language(s) used and there might be a bias there...I wouldn't be shocked for example if a larger percentage of those actually used Excel or Tableau than something like MATLAB
- a lot of people probably specify something like 'plotly' as the tool used that would make the actual language used ambiguous even though it definitely wasn't excel in that case
- I personally submit a lot of posts using MATLAB. I think roughly 10% of the MATLAB posts are mine, and I usually submit low-quality posts that get very few upvotes (I don't think I've ever broken a score of 100 on this subreddit). Thus, I have personally hurt MATLAB's performance.
I'll think about more robust ways to catch all of these and might do this again at some point in the future. As a note, I did the data gathering and plotting in Python, but Excel doesn't have as many top posts so I redid all of the plots using Excel in hopes of breaking the trend.
My code for scanning the posts can be found here: https://github.com/rhamner/dataisbeautiful_languageFrequency
My code for scanning the posts can be found here: https://github.com/rhamner/dataisbeautiful_languageFrequency
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