3 ms·

I've had a similar experience from the opposite side. I've had quite a few years of experience in Python and had to work in R for an internship during my master

by BDPW 1y ago

I've had a similar experience from the opposite side. I've had quite a few years of experience in Python and had to work in R for an internship during my masters. My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with. Access to the underlying data was often opague and it was difficult to me at times to figure out what was happening under the hood. Does anyone here know any research areas still using R?

Tomte 1y ago
Everyone in statistics, and lots of people applying statistics in other disciplines (anthropology etc.).
j_bum 1y ago
In addition to stats, R is widely used in computational biology and bioinformatics domains. It’s also widely used in the biopharma industry for a variety of other purposes.
- mauritsd 1y ago
  IME (bioinformatics PhD in the netherlands a number of years ago) it's mostly still preferred in a (pre-)clinical context, not so much in academia itself
kgwgk 1y ago
> My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with. That's where I realised that the "modern" approach was taken in the article - which obviously I had not looked at.
pteetor 1y ago
R is used extensively in quant finance. The quant traders, portfolio managers, and risk managers with whom I work all use R.
vharuck 1y ago
As an R user, I get what you mean. If you need to do things that don't fit well in the "tidyverse" model, you have three options: 1. Wrap the complicated bits in functions, then force it into the tidyverse model by abusing summarize and mutate. 2. Use data.table. It's very adaptable and handles arbitrary multiline expressions (returning a data.table if the last expression returns a list, otherwise returning the object as-is). 3. Use base R. It's not as bad as people make it out to be. You'll need to learn it to anyway, if you want to do anything beyond the basics.
tyfon 1y ago
Not really research pr se, but it's used extensively in banking here in Norway for anything from statistical model development to basic analysis and reporting.
specproc 1y ago
Totally agree, I think this whole conversation is a first language thing. I learned pandas first, and found the whole R ecosystem to be a complete mess. So many different types of object, so many different syntaxes. The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R. Don't even get me started on docs and Stackoverflow for R. I much, and always will prefer Python. The one area I still go back to R is on proper survey work. I've looked for years and haven't found anything equivalent to the survey package for Python. I do like that R tends to start from the assumption that data is weighted. Fortunately I don't do surveys much anymore.
- larrled 1y ago
  Survey people use r already, or maybe stata, so there isn’t any need for a python package. It’s sort of trivial to implement a jackknife loop in python, if you really had to. A python survey package would not be pythonic.
  - specproc 1y ago
    Yeah, I've cludged together all sorts of stuff, just to avoid working in R. Terrible use of my time. The big issue isn't necessarily around jackknife etc. (as you say, pretty trivial and I think perhaps in statsmodels), but around regression weighting and ensuring compatibility with colleague's work. The R survey package, Stata and SPSS all support things like survey design in their regressions, python does not out of the box. Even simple things like weighted frequencies end up with some pretty awkward code in Python. I can imagine a pythonic survey package that extends pandas and statsmodels, but as you say, survey people use R and there's just not a scene for it. Past life for me anyway, surveys are not my bag.
- aydyn 1y ago
  > The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R Then you probably need to get new colleagues.
  - specproc 1y ago
goosedragons 1y ago
R is very heavily used in Statistics. It's also common in other sciences. I've worked a fair bit with biologists and that's what they're using too for data analysis and visualization.

by BDPW 1y ago

Tomte 1y ago

Everyone in statistics, and lots of people applying statistics in other disciplines (anthropology etc.).

j_bum 1y ago

In addition to stats, R is widely used in computational biology and bioinformatics domains. It’s also widely used in the biopharma industry for a variety of other purposes.

mauritsd 1y ago
IME (bioinformatics PhD in the netherlands a number of years ago) it's mostly still preferred in a (pre-)clinical context, not so much in academia itself

kgwgk 1y ago

> My impression was that it's pretty easy to do straightforward things like the examples described in the article. But when you have to do complicated or unusual things with your data I found it very frustrating to work with. That's where I realised that the "modern" approach was taken in the article - which obviously I had not looked at.

pteetor 1y ago

R is used extensively in quant finance. The quant traders, portfolio managers, and risk managers with whom I work all use R.

vharuck 1y ago

As an R user, I get what you mean. If you need to do things that don't fit well in the "tidyverse" model, you have three options: 1. Wrap the complicated bits in functions, then force it into the tidyverse model by abusing summarize and mutate. 2. Use data.table. It's very adaptable and handles arbitrary multiline expressions (returning a data.table if the last expression returns a list, otherwise returning the object as-is). 3. Use base R. It's not as bad as people make it out to be. You'll need to learn it to anyway, if you want to do anything beyond the basics.

tyfon 1y ago

Not really research pr se, but it's used extensively in banking here in Norway for anything from statistical model development to basic analysis and reporting.

specproc 1y ago

Totally agree, I think this whole conversation is a first language thing. I learned pandas first, and found the whole R ecosystem to be a complete mess. So many different types of object, so many different syntaxes. The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R. Don't even get me started on docs and Stackoverflow for R. I much, and always will prefer Python. The one area I still go back to R is on proper survey work. I've looked for years and haven't found anything equivalent to the survey package for Python. I do like that R tends to start from the assumption that data is weighted. Fortunately I don't do surveys much anymore.

larrled 1y ago
Survey people use r already, or maybe stata, so there isn’t any need for a python package. It’s sort of trivial to implement a jackknife loop in python, if you really had to. A python survey package would not be pythonic.
- specproc 1y ago
  Yeah, I've cludged together all sorts of stuff, just to avoid working in R. Terrible use of my time. The big issue isn't necessarily around jackknife etc. (as you say, pretty trivial and I think perhaps in statsmodels), but around regression weighting and ensuring compatibility with colleague's work. The R survey package, Stata and SPSS all support things like survey design in their regressions, python does not out of the box. Even simple things like weighted frequencies end up with some pretty awkward code in Python. I can imagine a pythonic survey package that extends pandas and statsmodels, but as you say, survey people use R and there's just not a scene for it. Past life for me anyway, surveys are not my bag.
aydyn 1y ago
> The tidyverse makes sense, and sure, is elegant, but if your colleagues are using base R Then you probably need to get new colleagues.
- specproc 1y ago

goosedragons 1y ago

R is very heavily used in Statistics. It's also common in other sciences. I've worked a fair bit with biologists and that's what they're using too for data analysis and visualization.