I took a two or three day on-site intro to R class that my employer put together. Perhaps it was not a great class, but as a seasoned software developer familiar with a number of imperative and functional languages I was baffled by R. 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. I know it's popular among statisticians and researchers, so I think a prerequsite must be a good fluency with statistics (I don't have that). Maybe it makes more sense if you think like a statistician. As a programmer I felt like nothing I learned about R contributed to developing an intuitive understanding of any of the rest of it.
R: Introduction to Data Science (2019)
61–70 of 139 posts
Re: R: Introduction to Data Science (2019)
#62In Google "data science" circa 2009 (although we didn't call it that), R was the weapon of choice.
I consider it a bad relic of the 70's. It doesn't have a "learning curve" -- it has a "learning straight line." Even when you're experienced and semi-competent at it, it's still difficult and surprising.
Re: R: Introduction to Data Science (2019)
#63I took a two or three day on-site intro to R class that my employer put together. Perhaps it was not a great class, but as a seasoned software developer familiar with a number of imperative and functional languages I was baffled by R. 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. I know it's popular a…
I think of R as a programming language designed by people who’d heard about programming languages but never actually used one before. It’s great for ad-hoc analysis without having to think about production systems.
Re: R: Introduction to Data Science (2019)
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Re: R: Introduction to Data Science (2019)
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Re: R: Introduction to Data Science (2019)
#66i'm an old R user, now migrated fully to python. 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. What we found was that R had alot of packages but most haven't been touched in years…
RStudio is a nice tool for making some quick graphs on data, descriptive analyisis and quickly exploring a dataset. Building some reports, or manipulating small datasets for beautiful graphs.
For anything else, we use Python.
Re: R: Introduction to Data Science (2019)
#67Earlier quoted context omitted.
A few of the main issues I see, as a R user who built his company on python - when we wanted to build a web app that processes data, it was a lot more straightforward to build both in python, so we can process data within the web servers instead of having to manage multiple stages of infrastructure and different languages. There's no Django for R. - R will often do something instead of explicitly failing. This is the…
> one of a hundred packages was updated There's renv that addresses that point already: https://rstudio.github.io/renv/articles/renv.html > There's no Django for R. Nowadays you can integrate R with WebR (WASM) in a web app: https://docs.r-wasm.org/webr/latest/
A lighterweight alternative to renv is to use Posit Public Package Manage (https://packagemanager.posit.co/) with a pinned date. That doesn't help if you're installing packages from a mix of places, but if you're only using CRAN packages it lets you get everything as of a fixed date.
And of course on the web side you have shiny (https://shiny.posit.co), which now also comes in a python flavour.
Re: R: Introduction to Data Science (2019)
#68Earlier quoted context omitted.
A few of the main issues I see, as a R user who built his company on python - when we wanted to build a web app that processes data, it was a lot more straightforward to build both in python, so we can process data within the web servers instead of having to manage multiple stages of infrastructure and different languages. There's no Django for R. - R will often do something instead of explicitly failing. This is the…
> R will often do something instead of explicitly failing. I mentioned exception handling above, but this is more specifically the problem. I think it's a hard problem to solve, because the behaviour of older libraries is so varied. I have sometimes thought that something like a try catch wrapper which pattern matched or tested the value returned would be useful.
I have noodled on this problem a bit in https://github.com/hadley/strict, which I'm contemplating bringing back to life over the coming year. It's certainly very difficult to cover 100% of all possible problems, but I suspect we can get good coverage of the most common failure points (specifically around recycling and coercion) with a decent amount of work.
Re: R: Introduction to Data Science (2019)
#69i'm an old R user, now migrated fully to python. 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. What we found was that R had alot of packages but most haven't been touched in years…
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Re: R: Introduction to Data Science (2019)
#70Earlier quoted context omitted.
OK, since you're here! (this all prefaced with a massive thank you for tidyverse, without which R is very crusty). I love R for interactive work and quick analyses, but I'm currently trying to integrate various bits of R code into a large document-building pipeline and wishing I could use Python for it: - Exception handling and error processing seem a pain in R. Maybe I'm doing it wrong, but if feels like a mess and…
Amen to that. Can I add the following: - 5 different ways to do wide to long and long to wide over the years even in the tidyverse. - A lot of dependencies to connect to DBs and difficult programs. Rstudio/Posit does have some premium libraries but they should be made free and bundled with the tidyverse to really promote the ecosystem. - Shiny support to save interactive charts and tables. This is a massive problem f…
What are the premium packages you're talking about? As far as I know all of our R packages are 100% open source.
I'd love to hear more why you're using webshot etc to talk screenshots of your shiny app. A more typical workflow would be to generate a separate HTML/PDF with quarto/RMarkdown.