[HN Gopher] Fast Lane to Learning R ___________________________________________________________________ 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 ___________________________________________________________________ (page generated 2022-05-17 23:00 UTC)