R: Introduction to Data Science (2019)
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R: Introduction to Data Science (2019)
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Re: R: Introduction to Data Science (2019)
#2See also:
"R Programming for Data Science" https://leanpub.com/rprogramming
Re: R: Introduction to Data Science (2019)
#3i'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 and when you contact the owner you find they've often moved onto the python/pandas/scikit eco system
Re: R: Introduction to Data Science (2019)
#4i'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…
Basically with tidyverse, R can let you write less code and keep it readable: https://www.sumsar.net/blog/pandas-feels-clunky-when-coming-...
Can't speak to abandonment, but it seems a lot of recent devel is occurring inside the the tidyverse, which is deprecating a whole bunch of other stuff.
Re: R: Introduction to Data Science (2019)
#5i'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.
Re: R: Introduction to Data Science (2019)
#6i'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…
R is much better for REPL style development and functional programming.
Python could be so much better with some minor syntax extensions.
Re: R: Introduction to Data Science (2019)
#7i'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…
My main use case is making high quality visualizations for quick data exploration and sharing with a team. It is easy to guarantee that fonts are large enough, style is minimalist and clean, and filtering, transforming views or facets iteratively is only a couple characters change.
Re: R: Introduction to Data Science (2019)
#8i'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…
R is the better EDA language by far. Python has caught up a lot. Notebook diffs are now readable in git with the right tooling, that's huge.
The drum about not fitting into data pipelines... if you're literally using a bash pipe its true most R programmers have no idea how to do that. Otherwise, that is where Docker and k8s shine.
On packaging. R's package authority runs tests and ensures that all packages work with the latest version of their peers. The dependency heck is much less deep as a result.
We use R at my employer still because we put statistical data science into production. Our experts come to us comfortable with R. Reimplementation would be absurd.
Re: R: Introduction to Data Science (2019)
#9i'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…
Bayesian stats
Traditional stats
Very fast iteration for data exploration in REPL (vs code or R studio).
Prefer pipeline workflows (Tidyverse/maggrittr).
Prefer functional
Prefer array based.
Prefer 1-indexed arrays (yes there are some of us).
Re: R: Introduction to Data Science (2019)
#10i'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 think its more intuitive for statistical applications where Python is grossly under-represented. This includes things like the design and analysis of experiments but also lots of domain specific statistics and algorithms such as in bioinformatics, chemistry, and so on.
Typically those applications are not the sort of line-of-business enhancements ML in Python is more tuned to. I.e. recommender systems, NN models, and so on.