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Why Clinical Laboratorians Should Embrace the R Programming Language

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Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#21

Post by [deleted] »

nabla9 wrote: post #17
tomrod wrote: post #10

Earlier quoted context omitted.

R packages are the wild west as far as code quality goes. In one corner you have Hadley Wickam producing phenomenal efforts like tidyverse and ggplot. In the other corner you have a herd of feral cats. Python gets scrutiny but most packages are on github and feedback can be received. Although, you should read the source code regardless. EDIT: And I re-emphasize -- never trust the source code, even if the company you…

There are different qualities. Code quality and quality of the functionality. In R you more packages that do what you expect (mathematically) but the implementation is inelegant and slow. Written by someone who knows exactly what they need and what the package should do, but has difficulty of writing it down. In python you many well implemented neat packages where the code is well implemented and performs well, but i…

[deleted]

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#22

Post by teruakohatu »

z77dj3kl wrote: post #19
ekianjo wrote: post #18

Earlier quoted context omitted.

R has much better syntax with tidyverse for data wrangling and even up to models with tidymodels and all. Python in comparison is hard to read.

What... Have you seen the piping syntax in tidyverse? It's incomprehensible unless you put in a lot of effort to understand all that's going on.

What is incomprehensible about it?

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#24

Post by z77dj3kl »

kubb wrote: post #7

My impression is that people outside of maths and statistics are more likely to choose Python than R, because they're able to get started with it more easily. Conventional programmers seem to be somewhat reluctant to learn R's syntax and adjust their programming model. Non-programmer types think in maths even less so they like the python "straightforwardness".

R is used almost exclusively in stats. Most in maths use python, c++ (there's a surprising amount of hpc code, e.g. pde solvers and other stuff floating around in c++), matlab, etc.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#25

Post by todd8 »

hichamino wrote: post #12

I am a clinical laboratorian and i find this article very useful .. Thank you for sharing. Can you help me to explore this field ?

Tilman M. Davies’s book, The Book of R: A First Course in Programming and Statistics is a good place to start. See [1].

[1] https://www.amazon.com/Book-First-Course-Programming-Statist...

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#26

Post by _Wintermute »

I basically worked as an R troubleshooter in a Pharmaceutical company, and honestly I wish python or Julia would take its place. There's so many instances when R would return a nonsense answer rather than fail, but you wouldn't realise until you did a deep dive of someone's code.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#27

Post by hermitcrab »

R, for all its strengths, seems to have quite a learning curve and a lot of syntax to remember. The other approach is to use a GUI based enviroment such as Easy Data Transform, Alteryx or Knime. These are never going to be quite as flexible as a language-based approach, but they are a lot easier to get started with - especially for people with no programming background.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#28

Post by tomrod »

nabla9 wrote: post #17
tomrod wrote: post #10

Earlier quoted context omitted.

R packages are the wild west as far as code quality goes. In one corner you have Hadley Wickam producing phenomenal efforts like tidyverse and ggplot. In the other corner you have a herd of feral cats. Python gets scrutiny but most packages are on github and feedback can be received. Although, you should read the source code regardless. EDIT: And I re-emphasize -- never trust the source code, even if the company you…

There are different qualities. Code quality and quality of the functionality. In R you more packages that do what you expect (mathematically) but the implementation is inelegant and slow. Written by someone who knows exactly what they need and what the package should do, but has difficulty of writing it down. In python you many well implemented neat packages where the code is well implemented and performs well, but i…

> In R you more packages that do what you expect (mathematically)

I disagree with this point explicitly. Many packages are not only poorly written programmatically and systemically, they also produce bad results in common cases and fail silently. This has been discussed well before on our very own YCombinator.

https://news.ycombinator.com/item?id=17308554

> is not exactly doing what user need or skips important features because they are conceptually difficult.

This is why classes are cool. You can extend them, modify them, and so on. Perhaps we should teach this more to fellow data scientists.

https://docs.python.org/3/tutorial/classes.html

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#29

Post by Maxion »

akg_67 wrote: post #14
refactor_master wrote: post #11

To me, R seems more like a cobbled together ecosystem of automation within statistics, rather than an actual language. Compared with Python the language ergonomics of R are confusing and inconsistent. I guess momentum and establishment is also a feature in itself though I’ve personally never felt that, one of the selling points, the esoteric statistics packages at the edge would be of any use to me. The use I’ve seen…

Actually, it most probably reverse. Python, numpy, pandas, etc are cobbled together with duct tape to do what R does elegantly. There is no consistency with Python ecosystem.

Agree. One thing is very clear in the R vs. Python debate is that a lot of programmers seem to know either or, not both.

They are different tools for different purposes.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#30

Post by Maxion »

teruakohatu wrote: post #3

I would say you could replace R with Python or Julia and do just fine. Anything must be better than SPSS. That said, R had come a long way in recent years and is enjoyable to use. It is very complete as far as statistics go.

It depends on what you're doing, there's a lot of genomics related packages for R that do not exist in python.
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