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For those of you who us R still what is your use case? We found R has a really hard time integrating into data pipelines and was best used as a standalone tool by individuals, which doesn't really work in our particular professional setup where everyone works collaboratively together. About half our team can wrangle and plot as fast as we can think of ideas. It creates an incredibly tight cycle time between us having ideas and getting answers; sometimes many e. Before we used R, it would require someone jotting down things to investigate and reporting back in the next meeting.

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Wow, I really appreciate the reply. PHP used to be like that as well, but later versions look more usable for general scripting. Moto x3m Pool Party. No, it's not django, but it's a much richer framework than dash, and you can always bring your own HTML if what it generates for you isn't sufficient. Totally Science is a product founded by Zach Yadegari. On packaging. So I guess there isn't much you could have done in this case. I'm an old R user forced to mostly use python because that's what the team uses. How weird are we talking? Hey Hadley!! What python learning material you recommend focused on data science? Use suppressWarnings to silence misbehaving functions or withCallingHandlers to stop or handle specific conditions. As a programmer I felt like nothing I learned about R contributed to developing an intuitive understanding of any of the rest of it. You do any consulting on non-adjacent areas. Thanks for responding and your amazing work with the tidyverse.

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Then I can query, aggregate, or plot data sitting the meeting. State government resists change, especially role changes that don't match grants' or laws' wording. Re webshot my use case is: I have a heavily modified DT table in a shiny app. A lot of the stuff above was complaining about issues where Python is a lot worse than R, about non-issues or with a fundamental misunderstanding of the language. For anything else, we use Python. Formulas are extremely well-suited for data analysis but odd outside of that. It felt like a bunch of little functions that had been developed by different people with no consistent framework, and thrown together in some kind of big wrapper. Can you not just build your own code as a package and specify exact dependencies? What about R's language makes it better for Repl driven development? So our issues was partially our infrastructure tooling was going to python, but also we had a far easier time hiring people who were proficient in python and harder to find the same for R. Before we used R, it would require someone jotting down things to investigate and reporting back in the next meeting. The consistency of model specification across multiple libraries is really helpful base lm, lme4, brms etc.