4 ms·

I do wonder if there isn't enough computer science / software engineering that is being taught as part of data science. People I've worked with that used R and

I do wonder if there isn't enough computer science / software engineering that is being taught as part of data science. People I've worked with that used R and manged data / did analysis didn't really seem too concerned with long term maintenance. Secondary observation, these same people were the first to preach for the AI coding gospel.

mjhay 4mo ago
Bingo. The typical data scientist has a masters or PhD in a non-CS quantitative field, and has had exactly zero CS or software eng classes. It’s a shame, because once you get over some of the idiosyncrasies, R is a really powerful and flexible functional language.
- flopsamjetsam 4mo ago
  I was a programmer, and moved over to biology recently. I was very "wtf is R" when I started, but slowly its strengths are coming through. I found the combination of the REPL plus the IDE, and the language syntax, somehow give me the ability to play around a lot more than I expected. Unlike others, I find tidyverse (especially ggplot2 and dplyr, which reminds me a lot of pandas) to be quite intuitive.
mr_toad 4mo ago
> People I've worked with that used R and manged data / did analysis didn't really seem too concerned with long term maintenance. Unless you’re the poor schmuck who is given the task of running the code written by the previous analyst, who has probably already left the company. Often it’s easier to just throw something together from scratch and then look for a new job, perpetuating the problem.
dofm 4mo ago
One of the things that always reassures me about LLMs is that as well as being trained on languages with reasonably well-designed grammars, they will also have seen lots of examples of good practice in their training set. Two things that make me wonder if they can possibly turn out good quality R. Perhaps a true test of AGI will be when you ask it to write an application in R and it refuses for fear of what people might think.
ngriffiths 4mo ago
At my job I switch between writing analysis code for research projects and writing code for apps. The difference in mindset is so dramatic. In the same way that good software has consistent names and interfaces that are ~useless when you just need the code to run once, research code has its own requirements that are ~useless in software. It's honestly a big challenge to switch back and forth. So I think it just reflects the main skillset of the people who use it (caring is not enough).

mjhay 4mo ago

Bingo. The typical data scientist has a masters or PhD in a non-CS quantitative field, and has had exactly zero CS or software eng classes. It’s a shame, because once you get over some of the idiosyncrasies, R is a really powerful and flexible functional language.

flopsamjetsam 4mo ago
I was a programmer, and moved over to biology recently. I was very "wtf is R" when I started, but slowly its strengths are coming through. I found the combination of the REPL plus the IDE, and the language syntax, somehow give me the ability to play around a lot more than I expected. Unlike others, I find tidyverse (especially ggplot2 and dplyr, which reminds me a lot of pandas) to be quite intuitive.

mr_toad 4mo ago

> People I've worked with that used R and manged data / did analysis didn't really seem too concerned with long term maintenance. Unless you’re the poor schmuck who is given the task of running the code written by the previous analyst, who has probably already left the company. Often it’s easier to just throw something together from scratch and then look for a new job, perpetuating the problem.

dofm 4mo ago

One of the things that always reassures me about LLMs is that as well as being trained on languages with reasonably well-designed grammars, they will also have seen lots of examples of good practice in their training set. Two things that make me wonder if they can possibly turn out good quality R. Perhaps a true test of AGI will be when you ask it to write an application in R and it refuses for fear of what people might think.

ngriffiths 4mo ago

At my job I switch between writing analysis code for research projects and writing code for apps. The difference in mindset is so dramatic. In the same way that good software has consistent names and interfaces that are ~useless when you just need the code to run once, research code has its own requirements that are ~useless in software. It's honestly a big challenge to switch back and forth. So I think it just reflects the main skillset of the people who use it (caring is not enough).