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Big Book of R
111–116 of 116 posts
Re: Big Book of R
#112R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
Life saver. I do not use the raw dataframe API, inconsistent and error prone.
Re: Big Book of R
#113Any Julians comment? Having seen Julia proposed as the nemesis of R (not python, that too political, non-lispy) > the creator of the R programming language, Ross Ihaka, who provided benchmarks demonstrating that Lisp’s optional type declaration and machine-code compiler allow for code that is 380 times faster than R and 150 times faster than Python (Would especially love an overview of the controversies in graphics/r…
Julia is what I mostly use. I used R in the past, but I was all the time puzzled from the documentation. It did not work for me. Sometimes I fire the REPL for some interpolation, but I limit myself to what I understand.
BTW I am a senior Java / Python developer
Re: Big Book of R
#114Been working 8 years with Rs data.table package in research and now after I changed to the private sector I have to use python and pandas. Pandas are so terrible compared to data.table it defies belief. Even tidyverse is better than pandas which is saying something. I miss it so much
Use it every single day. Absolutely fantastic tool.
Re: Big Book of R
#115R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
Why not mix R and Python in interactive analysis workflows: 1) Download positron: https://github.com/posit-dev/positron 2) Set up a quarto (.qmd) notebook 3) Set up R and Python code chunks in tour quarto document 4a) Use reticulate to spawn a Python session inside R and exchange objects beween both languages ( https://github.com/posit-dev/positron/pull/4603 ) 4b) Write a few helper functions that pass objects betwee…
Is this what tools like Nextflow or Snakemake aim to do? I don't know, and I'm genuinely curious, because I'm starting to work in bioinformatics and doing different parts of an analysis pipeline in R and Python seems common, and, necessary really if you want to use certain packages.
I'm wondering if I should devote time to learning Nextflow/Snakemake, or whether the solution that you outlined is "sufficient" (I say "sufficient" in quotes because of course, depends on the use case).
Re: Big Book of R
#116Earlier quoted context omitted.
what’s the story integrating R code into larger software systems (say, a saas product)? I’m sure part of Python’s success is sheer mindshare momentum from being a common computing denominator, but I’d guess the integration story is part of the margins. Your back end may well already be in python or have interop, reducing stack investment and systems tax.
It's getting a lot better, but R in production was something companies 10 years ago would say "so we figured out a way". The problem is pinning dependencies. So while an R analysis written using base R 20 or 30 years ago works fine, something using dplyr is probably really difficult to get up and running. At my old work we took a copy of CRAN when we started a new project and added dependencies from then. So instead…
Posit offers cran snapshots on their public 'package manager' instance. It's a great resource indeed. Via the URL you can ask to use as a mirror and get the snapshot you desire ;)