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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

#51

Post by 41b696ef1113 »

vixen99 wrote: post #48
_Wintermute wrote: post #26

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.

So the question is, who was writing the code and why were they so evidently incompetent? Easy for anyone to pick up a bit of R and start working with it. Thus it's hardly surprising to find the situation you describe. Why weren't these folk put through a rigorous course before being let loose in a pharmaceutical company of all places? Hardly their fault unless they exaggerated their skills.

My goto resource on this would be aRrgh which goes through some of the many rough edges of the language. Silent failures and data type castings can bite even those experienced in the language.

R is a data analysis DSL that also happens to be a full programming language.

[0] http://arrgh.tim-smith.us/

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#52

Post by disgruntledphd2 »

tomrod wrote: post #43
disgruntledphd2 wrote: post #40

Earlier quoted context omitted.

> This is why classes are cool You may be unaware, but there are vast similarities between the object system of R and Python, mostly due to their common inheritance from the Art of the Metaobject protocol. They look very different (generic functions vs classes), but they are equally extensible. The trouble with R's systems is that there's three of them, and people use whichever works without really understanding any…

Aware, yes. The point I made before was pointing out that you can implement what you want in Python relatively easy. That R has a wild west set of systems is exactly my point.

I don't really get what you mean.

You said, in the context of ensuring conceptually difficult parts of a model/method were implemented:

> 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.

I pointed out that both R and Python have similar object systems.

> ou can implement what you want in Python relatively easy.

I don't get why this is necessarily true, but I might be missing something. Can you clarify?

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#53

Post by wheelinsupial »

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 ?

What do you currently use for data analysis?

I’ve taken courses on statistical computing in R and statistical computing in SAS in my statistics degree. We were always told that SAS is the standard for anything health care, pharmaceutical, or where regulation and publication comes into play.

Anecdotally, my friends who did PhDs in biochem and immunology all used SAS for their data analysis.

Have I been misled or is this up to individual preference?

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#54

Post by clircle »

jarenmf wrote: post #50
clircle wrote: post #44

Reasons I prefer R to python: - Rmarkdown. I prefer a text document over a web notebook for exploratory research - The standard library is for statistics and data: dataframe, lm, anova, etc. are builtin - A huge range of probability distributions are built-in. I don't need to import extra libs to do simulations. - Between functional programming techniques and vectorization, I can write very clean and concise code - T…

Agreed, also R with vim is really a joy to work with (Nvim-R plugin) I can't replicate the experience with any other IDE. For example, I can define my own key bindings to show a certain summary statistic or a custom plot for the variable I'm at.

Thanks for this. I might give it Nvim-R a shot. I've been using Evil+ESS for a long time, but Emacs runs like a dog on Windows 10.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#55

Post by jhoechtl »

clircle wrote: post #44

Reasons I prefer R to python: - Rmarkdown. I prefer a text document over a web notebook for exploratory research - The standard library is for statistics and data: dataframe, lm, anova, etc. are builtin - A huge range of probability distributions are built-in. I don't need to import extra libs to do simulations. - Between functional programming techniques and vectorization, I can write very clean and concise code - T…

Does RStudio already work on Linux wayland? Last time it failed because of a QT-component but AFAIK the whole thing is transitioning to electron.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#56

Post by eigenspace »

_Wintermute wrote: post #26

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.

So many dynamic languages take this strange ethos of never wanting to throw an error and instead just guessing what the programmer meant and just doing something wacky instead of throwing an error. It's a real problem.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#57

Post by MohammedAShahid »

the_only_law wrote: post #45
z77dj3kl wrote: post #24

Earlier quoted context omitted.

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.

No more fortran I guess?

From what I've seen, there is actually still people using FORTRAN for Applied Math. I had several professors who use it for CFD.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#58

Post by fastaguy88 »

adimitrov wrote: post #8
toolslive wrote: post #6

> Unlike Excel and many other graphical user interface (GUI)-based programs, R’s reliance on text-based structure makes it straightforward to review at any time the commands used in a data processing pipeline to ensure that the correct steps were taken. > Furthermore, the ability to view the underlying commands facilitates transparency and reproducibility of analyses. The article seems to be targeted at people with z…

I disagree. As a software engineer, R is a nuisance, it's a terrible language, and I hate doing complicated things in it. But it's very powerful, it's exactly right for these use cases and its ecosystem is mindbogglingly huge. Also, it tends to be easier to grasp for folks who don't have prior programming knowledge (anecdotal, but I've seen people pick it up very quickly who struggled a lot with, say, Python. And Pyt…

But, to focus on the original article, Clinical Labs, where data analysis is literally the basis for life-and-death decisions, is not a sensible use case.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#59

Post by fastaguy88 »

vixen99 wrote: post #48
_Wintermute wrote: post #26

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.

So the question is, who was writing the code and why were they so evidently incompetent? Easy for anyone to pick up a bit of R and start working with it. Thus it's hardly surprising to find the situation you describe. Why weren't these folk put through a rigorous course before being let loose in a pharmaceutical company of all places? Hardly their fault unless they exaggerated their skills.

While your standards could be different from mine, I don’t think that all programmers who fail to write perfect code the first time are incompetent. Many competent programmers are not perfect, and rely on error messages and debuggers to produce correct code. Unfortunately, R does often fails to give the information required to find bugs.

Re: Why Clinical Laboratorians Should Embrace the R Programming Language

#60

Post by jhbadger »

Maxion wrote: post #30
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.

Exactly. Bioconductor is essential to genomics.
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