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The tidyverse packages are pretty great that is true, in particular dplyr was useful. I don't want to sound like I'm just someone against R for the sake of it.
by verelo 5y ago
The tidyverse packages are pretty great that is true, in particular dplyr was useful. I don't want to sound like I'm just someone against R for the sake of it. There were things I liked, but I just walked away from the project uncertain what the benefit was in using R. There's a solid chance i'm wrong.
- lanstin 5y agoThe advantage is you get tables, numbers and plots based on solids maths instead of implementing your own understanding of useful analyses. Depending on your skill and interests in implementing your own designs for stats this might be a plus or a minus. For me, I don’t want to spend my time redoing the tricky and subtle stuff, but I can learn the odd syntax and weirdly functional nature of it pretty reliably. If someone does a Golang version of CRAN I wouldn’t think of using R again but for now R wins the stats battle. And much of the community advice and so on about how to do things, outside of the base R vs tidyverse flame rift, are from informed statisticians not autodidact Python folks that think everything is easy. But if you do understand he maths solidly behind what you want, then R might not be the best tool for your use.