[HN Gopher] Fast Lane to Learning R
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       Fast Lane to Learning R
        
       Author : Tomte
       Score  : 10 points
       Date   : 2022-05-15 05:54 UTC (2 days ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | civilized wrote:
       | Base R? 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.
        
         | notafraudster wrote:
         | I agree that new R programmers should start with the tidyverse,
         | though actually the first element of the tidyverse I'd teach is
         | the pipe and that's now part of base R; the second element I'd
         | teach is using the readr stuff mainly to not have to worry
         | about stringsAsFactors and stringsAsFactors is now default off.
         | 
         | Still, I think ggplot is a better way of thinking about
         | plotting than base R's multiple and not very coherent plotting
         | systems, dplyr beats the pants off any kind of base tools for
         | manipulations, a lot of the tibble/pillar display stuff is
         | great, and personally I disagree with Norm and think functional
         | programming is as accessible as loops are to novice
         | programmers.
        
       | mechanical_bear wrote:
       | If I'm already productive in Python doing similar analysis, is
       | there a good reason to switch to R?
        
         | notafraudster wrote:
         | I write code in both daily. I don't think there is a burning
         | need to know both, but there are definitely tasks each is good
         | at. The RStudio IDE is really quite wonderful for interactive
         | stuff. The pipe operator (allowing left to right evaluation of
         | function chains) makes for extremely literate code. Most of the
         | basic statistical functions are substantially better than their
         | Python equivalents. Many of the ways in which you use Python
         | for data science stuff are just poor imitations of R (in
         | particular, pandas is a take on R's data frames that is imo not
         | as productive). In some conditions R can be faster [in others,
         | slower]. The R package ecosystem for more off the beaten path
         | statistical stuff is better than the Python package ecosystem
         | (if you're doing more intense ML and CNNs, the opposite is
         | true). If you're doing dashboards, Shiny is great. But in each
         | case, you can basically work in either language and you'll be
         | fine.
         | 
         | I think it makes more sense to think of R as replacing Stata,
         | SPSS, SAS, and to a lesser extent Matlab, rather than replacing
         | Python.
         | 
         | Julia is also fun.
        
         | nojito wrote:
         | Less code to get the same result. And r markdown is infinitely
         | better than anything in the python ecosystem
        
           | lmc wrote:
           | > And r markdown is infinitely better than anything in the
           | python ecosystem
           | 
           | You might want to check out Quarto [1], which i recently
           | discovered on here
           | 
           | [1] https://quarto.org/
        
         | mistrial9 wrote:
         | it seems the author has something to say about that:
         | https://github.com/matloff/R-vs.-Python-for-Data-Science
        
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