R: Introduction to Data Science (2019)

Post by disgruntledphd2 » Sat, Mar 02, 2024, 3:31 PM UTC

klmr wrote: post #30

zhdc1 wrote: post #16

Earlier quoted context omitted.

I’ve transitioned a lot of my work over to Julia, but R is still the most intuitive language I’ve used for scripting out data collection, cleaning, aggregation, and analysis cases. The ecosystem is simply better. The folks who maintain CRAN do a fantastic job. I can’t remember the last time a library incompatibility led to a show stopper. This is a weekly occurrence in Python.

> I can’t remember the last time a library incompatibility led to a show stopper. Oh, it’s very common unless you basically only use much better (and ‘rig’ and PPM also help in small but important ways). But it’s still dire. At work we are facing these issues every other week on some code base.

Post by disgruntledphd2 » Sat, Mar 02, 2024, 3:34 PM UTC

ekianjo wrote: post #17

chollida1 wrote: post #3

i'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…

tidymodels is miles ahead the toys you have in python for traditional machine learning. of course Python is much better in other areas but that is a big reason to use R, together with the super powerful tidyverse syntax. and package management is much, much more reliable in R than in python.

Post by AndyMcConachie » Sat, Mar 02, 2024, 3:40 PM UTC

Post by iainctduncan » Sat, Mar 02, 2024, 3:45 PM UTC

laylower wrote: post #45

benreesman wrote: post #40

I'm looking at R seriously for the first time. I've got a decade in with Python numeric computing, and I'm interested in Julia and all of the cutting-edge stuff. I've only dabbled with R until now, and I haven't researched it enough to know if rumors of it's inevitable demise have any substance. There are a lot of interesting math problems other than training gigantic neural networks on NVIDIA gear, and I've got some…

I love R. You could do it R. But a lot of the derivations and Math Finance stuff you can and should be able to do in C/C++. R packages mostly depend on those as well for heavy duty calcs. So, if I wanted to dabble I'd easily use R and if I was in the quant developer world I'd be doing C/C++

Post by getoffmycase » Sat, Mar 02, 2024, 4:31 PM UTC

klmr wrote: post #30

zhdc1 wrote: post #16

Earlier quoted context omitted.

I’ve transitioned a lot of my work over to Julia, but R is still the most intuitive language I’ve used for scripting out data collection, cleaning, aggregation, and analysis cases. The ecosystem is simply better. The folks who maintain CRAN do a fantastic job. I can’t remember the last time a library incompatibility led to a show stopper. This is a weekly occurrence in Python.

> I can’t remember the last time a library incompatibility led to a show stopper. Oh, it’s very common unless you basically only use much better (and ‘rig’ and PPM also help in small but important ways). But it’s still dire. At work we are facing these issues every other week on some code base.