TidyverseSkeptic

I just don't get these to be honest -- besides the fact that author missed simple things like `df.groupby('var',as_index=False)`, isn't this obviously arbitrary "this is easier my way" complaints? (I did R before all the chaining stuff was popular, and I wouldn't stuff everything into a single command like that even now. It isn't like you get lazy evaluation or any special data processing magic.) So I get people love chaining and tidyverse, good for you, I don't. But at least I can acknowledge that my way (or this way) people have different preferences and one is not intrinsically easier. Norm Matloff has a blog where he essentially just argues the opposite of all the tidyverse stuff, https://github.com/matloff/TidyverseSkeptic, but it is the same idea in reverse to me (one is not obviously easier to learn than the other IMO).

Base R only? tapply? Ick, no thanks. New R programmers should learn tidyverse https://www.tidyverse.org/learn/, not just base R. There are a lot of R programmers (including the author of the OP, apparently https://github.com/matloff/TidyverseSkeptic) who are used to the old way of doing things and allergic to tidyverse. But the base-R only, anti-Tidyverse attitude is going the way of COBOL. I have worked full time in R for many years and it is no contest. I agree with the author that one must learn vectors and subscripts, but that is not an excuse to shove dplyr under the rug. It is an easier way to do lots of things.

> For a new R user it seems so much simpler: > 1. run "library(dplyr)" 2. Google "how to XYZ in dplyr" 3. ??? 4. Profit I beg to differ here. There’s much to be said for using data.table and base R instead of the tidyverse. This article is worth a read in my view: https://github.com/matloff/TidyverseSkeptic

IMO the main contribution of RStudio is not RStudio itself (the IDE) but their general support of the R language. I think that this dynamic scene where R is still an evolving language is the true value. I don't actually use RStudio itself (hurray for vim). Tidyverse is the obvious example of their contributions, but I don't mean just tidyverse (actually, there are some things that I don't like that much, like tibbles) [1]. I think a language like R really needs this kind of support due to its mantra: for statisticians, by statisticians. By that I mean that you should be able to get insights by sometimes only writing a handful of lines of code. The ease of use of R, good libraries, nice graphs, and so forth is why I happily code in both R and Python. [1] It seems like here is another thread about some objections to tidyverse: https://githu