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R: Introduction to Data Science (2019)

rafalab.dfci.harvard.edu

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Re: R: Introduction to Data Science (2019)

#11

Post by chollida1 »

medstrom wrote: post #4
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…

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.

I will agree that I left just as tidyverse was coming of age and I'm sometimes jealous i never got to use it.

What Hadley Wickham has done is very impressive.

Re: R: Introduction to Data Science (2019)

#12

Post by chollida1 »

ildjarn wrote: post #6
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…

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?

Re: R: Introduction to Data Science (2019)

#13

Post by transcriptase »

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…

Bioinformatics, particularly genetic mapping and population genomics. There’s an entire ecosystem of very mature tools actively maintained by labs to add analyses pertaining to advancements in the field, without breaking pipelines or silently changing the results of a given analysis from version to version.

Take something like adegenet, where the manual itself is approaching 200 pages:

https://cran.r-project.org/web/packages/adegenet/adegenet.pd...

Re: R: Introduction to Data Science (2019)

#14

Post by nomilk »

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…

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.g. 10-20+) of those cycles in a single meeting. Before we used R, it would require someone jotting down things to investigate and reporting back in the next meeting. But we can do ~80% of whatever people can think of on the spot (more involved research questions can take more time).

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.e. your code, for the most part, is like reading a page in a book.

Re: R: Introduction to Data Science (2019)

#15

Post by jslakro »

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…

What python learning material you recommend focused on data science?

Re: R: Introduction to Data Science (2019)

#16

Post by zhdc1 »

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…

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.

Re: R: Introduction to Data Science (2019)

#17

Post by ekianjo »

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.

Re: R: Introduction to Data Science (2019)

#18

Post by acc_297 »

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…

Pk/PD work for pharmaceutical data analysis I didn’t like using R at first but I’ve come to appreciate the speed that comes with months of experience.

It’s a language which feels like it has a lot of magical incantations you need to remember - the default namespace is much more crowded. Functions like sapply vs mapply are tricky to reason about from the documentation alone. 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.

But after using it for a few hundred hours to do data processing and statistical regression it’s hard to imagine python or Julia being faster to use. But in all honesty for the pharmaceutical industry it’s mostly momentum that keeps R on top same reason they use a lot of FORTRAN90.

Re: R: Introduction to Data Science (2019)

#19

Post by rcbdev »

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…

R's biggest moat in my opinion is its much saner package management system and lower propensity to curb stomp existing libraries and projects with breaking changes.

As a SWE I much rather inherit and maintain R services than Python services.

Re: R: Introduction to Data Science (2019)

#20

Post by hadley »

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…

If you tell me what makes R hard to integrate into data pipelines I will do my best to fix it :)
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