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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. The unique qualities of R that allow this are that it's so easy to use, extremely reliable for package installation problems occur approximately never , and the tidyverse makes it incredible easy to translate ideas into code, not only in its broad, easy to understand and powerful vocabulary, but in there being little 'nesting' required; instead working left to right and top to bottom via the magrittr pipe - i.

Totally science io

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Personally only issues for me with integrating R is making renv play nice in multistage docker builds.

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Totally science io

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Parkour Race. The values NA vs Null vs integer 0 are all used as standins for real thrown errors and knowing which one to check for after calling a function can be tough. The option to stop on warnings isn't useful because older packages seem to abuse warnings as messages. I stopped using it in , when I began to learn how to code. I found that I need to have my other pipeline software built in the same stage as my R env setup building specific version from archive, system dependencies, then r package dependencies via renv reply. SomeoneFromCA 7 hours ago root parent next [—]. Just import star and you're golden. But the fact that there's no Django for R means shiny's a dead-end for a production web app. I'm an old R user forced to mostly use python because that's what the team uses. If its OTC I won't be able to help anyway, if its standard then I can at least try to point you in the right direction. You might also be well served by adopting some more data validation tooling, e. It's about saving CPU time more than user time.

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Prefer 1-indexed arrays yes there are some of us. SomeoneFromCA 7 hours ago root parent next [—]. Also Erlang with the actor model, and Go with its goroutines for network services. If you have to use R, use the tidyverse. I quit R a while ago - before data science became a thing - and switched to Julia for such tasks. It is de facto. Most people I know who want to dive deeper end up learning Python anyway. FNAF 3. I had thought this book was redone by someone in Python. It creates an incredibly tight cycle time between us having ideas and getting answers; sometimes many e. Experience the latest games here We have update new Block the Pig Games, Eggy car, endless truck and more. Don't know how common this is though. It simply means that R was designed with a particular application in mind.