Why Clinical Laboratorians Should Embrace the R Programming Language
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Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#2This is more of a sales pitch for the general concept of programming rather than R in particular. R is as good a choice as any...
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#3I 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.
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#4I 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.
I think that R libraries have edge over Python and Julia in quality and quantity.
If you like to write lots of code, Python might be better. But if you use it in clinical research, R has probably better packages for whatever you need.
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#5I 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.
One’s who come from programming background prefer Python. The one’s who come from scientific or statistics background prefer R.
IME, for production Python rules and for data exploration, graphics, stats and adhoc analysis R rules.
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#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.
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#7My 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".
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#8> 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 Python is the only language/ecosystem that comes close to R.)
So, yeah, a lot of languages could be used for the use cases in TFA, but R is uniquely suited, weaknesses notwithstanding.
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#9There are some specific advantages that R has over other languages in this context. One is that you can use (almost)* a single source document to produce docx, pdf, and interactive html output.
* I can't quite get htmlwidgets and docx formats to work together in bookdown without using separate commands for the interactive tables (DT and flextable).
Re: Why Clinical Laboratorians Should Embrace the R Programming Language
#10I 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.
I think that R libraries have edge over Python and Julia in quality and quantity. If you like to write lots of code, Python might be better. But if you use it in clinical research, R has probably better packages for whatever you need.
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 work with indemnifies. One commercial product we used in particular had an insidious bug in one of the new time series packages that got corrected with in later versions, but we never would have found it if model testing requirements didn't also require implementing in R or Python. Since the package didn't exist for Python, and we wrote it ourselves, we found the performance issues.