171–180 of 299 posts
Wow I'm surprised to see ExtJS on a list of "most used" anything. I mean don't get me wrong it's great if you want to prototype something quick that uses data but for a great UX / real application it's dreadful to use IMO.
You might be reading the chart wrong. Its position basically means "most overrepresented on weekdays compared to its own weekend volume"
Microsoft should really take note of that. That's a huge tick on their woefully uncool meter. Developers, developers, developers, developers don't want to use Microsoft gear unless they're being paid, it would seem.
Everyone is pointing out potential problems with the methodology, but really this matches up pretty well with experience. SQL, MS Office stuff, boring things like logging, and testing all show up as being about work. Haskell is actually infamous for having an evangelical following and limited real-world uses. And, yeah, your C-like languages work well for both work and side-projects. This all makes perfect sense. The…
That may have been true 15 or more years ago but is not the case any more. I've been using Haskell almost exclusively for the last 7 years for everything from financial analysis to web back ends to, most recently, web front ends.
I was kinda surprised that Go (golang) wasn't up in the list
I realized one factor that might play a role. I came primarily from a C++ background, and back then, stackoverflow and google were paramount and used daily.
Now that I'm primarily using Go, I almost always get my answers from godoc.org (i.e., the documentation of packages), from source code, from the specification, and from trying things on playground. I don't remember using stackoverflow for Go programming questions in years, if ever.
I wonder if that's a property of Go language (great docusmentation, easily readable source, referenceable spec and viability of answering questions via playground experiments) that cause this, and if it applies to other people too.
The funnel shape of the scatter plot immediately reminded me of an article on the insensitivity to sample size pitfall [0], which points out that you'll expect entities with smaller sample sizes to show up more often in the extremes because of the higher variance. Looks like the tags with the biggest differences exemplify this pretty well. [0]- http://dataremixed.com/2015/01/avoiding-data-pitfalls-part-2...
Is it really a 'sample', if they are reporting on the entirety of their data for a given period? Is the question interpreted as extending to those not on stackoverflow, or is it a complete census of the 'population' of their data?
Suppose there were no difference between the usage of each language, and people just program on the weekends vs weekdays with some probability independent of language. Then, if a language has lots of users, it will likely have close to the average weekend/weekday proportion. The fewer users the language has, the more likely that it has an uneven weekend/weekday proportion just by chance. And if you plot the weekend/weekday proportions vs. the number of users, you expect a funnel shape just like the one in the article.
Therefore, the plot in the article - by itself - provides no evidence that there is any difference between the usage of different programming languages.
The funnel shape of the scatter plot immediately reminded me of an article on the insensitivity to sample size pitfall [0], which points out that you'll expect entities with smaller sample sizes to show up more often in the extremes because of the higher variance. Looks like the tags with the biggest differences exemplify this pretty well. [0]- http://dataremixed.com/2015/01/avoiding-data-pitfalls-part-2...
a Also, they likely have a low-end cutoff (notice their x axis starts at 10^4. If you do the same to the above plot, you get even closer to that exact shape. Try:
plot(a + b, log(a/b), xlim=quantile(a + b, probs=c(0.2, 1)))
I surprised there was no mention in the blog post or the comments so far about the homework factor. It isn't just personal side projects that people are working on over the weekend. I am betting the relative percentage of CS students on the site is also much higher on the weekend. Tags like assembly, pointers, algorithm, recursion, class, and math are all rather vague. Those topics are all discussed at length in CS c…
Yup, seeing the top 10 list made me immediately think of homework assignments. In the real world, programmers don't take their questions with "recursion" or "pointers" very often. Nor is Assembly a common language for side projects. This definitely seems like a case of students simply being relatively overrepresented on weekends.
Earlier quoted context omitted.
I can't tell, I know many developers today who are on the new shiny js thing, golang, elixir, scalar, rust train, but they never got really good in their first language be it Java, python, or php. They use linux or OSX. But they don't know awk/sed/bash, give em a very simple problem, they would write 1000 line of OOP, united test code when a few line of scripts would solve it. I know many programmers, who graduated w…
> They use linux or OSX. But they don't know awk/sed/bash Am I in the minority of developers then? I don't know awk and sed and I'm honestly not very interested in learning them. But bash I definitely agree with. I still use a cheatsheet to get the syntax right because it's weird as hell imo.
curl https://raw.githubusercontent.com/kedarmhaswade/cities/master/area-codes.csv | \
awk -F, '{print $1}' \
> ~/area_codes.txt
The funnel shape of the scatter plot immediately reminded me of an article on the insensitivity to sample size pitfall [0], which points out that you'll expect entities with smaller sample sizes to show up more often in the extremes because of the higher variance. Looks like the tags with the biggest differences exemplify this pretty well. [0]- http://dataremixed.com/2015/01/avoiding-data-pitfalls-part-2...
I think it's just that they are plotting sum vs ratio of two random variables. Try this (in R): a Also, they likely have a low-end cutoff (notice their x axis starts at 10^4. If you do the same to the above plot, you get even closer to that exact shape. Try: plot(a + b, log(a/b), xlim=quantile(a + b, probs=c(0.2, 1)))
plot(a + b, log(a/b), xlim=c(1, 2), log="x")
Earlier quoted context omitted.
I think it's just that they are plotting sum vs ratio of two random variables. Try this (in R): a Also, they likely have a low-end cutoff (notice their x axis starts at 10^4. If you do the same to the above plot, you get even closer to that exact shape. Try: plot(a + b, log(a/b), xlim=quantile(a + b, probs=c(0.2, 1)))
Note also their x axis is on a log scale, which makes the edges linear instead of curved. E.g.: plot(a + b, log(a/b), xlim=c(1, 2), log="x")