Unfortunately there are 56 other data science with R books, so what is the differentiating factor here?
R: Introduction to Data Science (2019)
21–30 of 139 posts
Re: R: Introduction to Data Science (2019)
#22i'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…
If you tell me what makes R hard to integrate into data pipelines I will do my best to fix it :)
This guy is the man to ask ^^^^^^
Re: R: Introduction to Data Science (2019)
#23i'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…
> We found R has a really hard time integrating into data pipelines and was best used as a standalone tool by individuals
In my org we have several 100% R teams (including mine) that have been developing and maintaining business-critical, data-intensive applications for a decade now. We don't find R difficult to integrate into data pipelines. We write our data pipelines in R, and we find it very efficient to do so. They talk to databases, APIs, command line tools, etc without issue.
Doing what we do in Python is unimaginable, especially if pandas is the tabular lingua franca in the team. I vehemently agree with this article on the clunkiness of pandas from a sister comment: https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-.... Compared to dplyr and the tidyverse, pandas very noticeably gets in your way rather than being a tool of thought. (For what it's worth, there are other teams in my org that use Python for entirely justified reasons, and they use polars these days, not pandas.)
If I had to complain about anything in R these days, it would be the increasing complexity and illegibility of error messages. Tidyverse tracebacks are often dozens or hundreds of lines. This is made much worse if you have a web app in the Shiny framework, as Shiny seems to mangle and garble what little useful information you can get (my kingdom for an error with a file name and line number). Even outside of advanced packages like Shiny, the reporting of error messages suffers from some clunkiness and irregularity.
As an expert user, I can usually squint at the error barrage and infer what is really going on, but it's probably quite confusing and off-putting to newer users.
Overall though, I'm not seeing any competition for R in our space. My fondest hope is that in the coming decades there arises a new, thoughtfully designed language with the Lispy flexibility of R, but also optional type safety and static analysis affordances. I'm not sure if that's even possible, but I hope the computer science geniuses figure out a way.
Re: R: Introduction to Data Science (2019)
#24i'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…
im somewhat of an armchair data scientist myself.
However - I'd love to learn your ways; specifically - what are your best recommendations for python over R?
Specifically, even though my R skills are weak - I think that RStudio is pretty darn amazing - what do you recommend over Rstudio?
I'd truly like to hear what a good toolbox looks like from your perspective these days (especially now this little GPT toddler is bonking into everything in my domain)
Re: R: Introduction to Data Science (2019)
#25i'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…
> We found R has a really hard time integrating into data pipelines and was best used as a standalone tool by individuals In my org we have several 100% R teams (including mine) that have been developing and maintaining business-critical, data-intensive applications for a decade now. We don't find R difficult to integrate into data pipelines. We write our data pipelines in R, and we find it very efficient to do so. T…
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Re: R: Introduction to Data Science (2019)
#26Earlier quoted context omitted.
R is much better for REPL style development and functional programming. Python could be so much better with some minor syntax extensions.
I find that with vscode and the immediate window I get a decent repl. What about R's language makes it better for Repl driven development?
If you haven’t used Emacs ESS it may be hard to explain what you’re missing with a true REPL, but if you had used it in the past and add tidyverse to it, you basically have super smooth interactive editing with the ability to quickly pool text from other REPLs, past notes or scripts. Contrary to the similar Python repl, you can easily pull and edit chains of multiple commands via R’s piping.
Re: R: Introduction to Data Science (2019)
#27Unfortunately there are 56 other data science with R books, so what is the differentiating factor here?
It's the Harvardx course
Re: R: Introduction to Data Science (2019)
#28i'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…
Exactly what you said, R is easy to get started for individuals in social science fields. Most people I know who want to dive deeper end up learning Python anyway.
This is true, I've found teaching r to get things done is very fast and readable fully achievable in a semester compared to teaching pandas (and also having to teach how to program in python)
Re: R: Introduction to Data Science (2019)
#29i'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…
I quit R a while ago - before data science became a thing - and switched to Julia for such tasks.
R has lots of stats packages, but it is too esoteric and specialised a language to be useful IMO.
Re: R: Introduction to Data Science (2019)
#30i'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…
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.