4 ms·

It's easier to dip your toes into, but it is a terrible language to learn software development with. It's such a hodge-podge of ill-thought-out and ill-fitting

by veddox 6y ago

It's easier to dip your toes into, but it is a terrible language to learn software development with. It's such a hodge-podge of ill-thought-out and ill-fitting components with random names and no overall sense of structure that you're never going to learn core CS concepts from it, let alone good programming practice.

disgruntledphd2 6y ago
That's not really its purpose though, right? R is "an environment for statistical computing and graphics". Note that it doesn't talk about software engineering at all. I agree with you that R has lots of rough edges, but please remember that it's a 90's era clone of a 70's era language (S) and a lot of those rough edges and corners are legacies from that time. I completely agree that the naming conventions (i.e. the absence of same) are super annoying, but again it's a tradeoff for the decades of statistical computing knowledge embedded in the system of R. I find your disdain for R a little annoying, and while I'm probably not a computer scientist, it was my first language and I learned about closures, higher order functions, OOP, testing and interacting with API's from it, and it definitely gave me insight into how computers worked. I'm sorry that you have to deal with horrible legacy R code, but that's no reason to throw the baby out with the bathwater.
- claytonjy 6y ago
  I really appreciate this sentiment as a data person who learned R first, now works in python, and helps non-engineer scientists write R programs for research. I've had great luck teaching scientists to think in a function-driven way, where functions are pure-as-possible and inputs are never mutated (which doesn't need to be discussed as R makes the opposite quite unnatural). I can't imagine teaching these same folks about python, custom classes, and why you need pd.DataFrame.copy() all over the place.
  - SJetKaran 6y ago
    ` and why you need pd.DataFrame.copy() all over the place.` Thanks for saying this! Also, in the past decade, the R data analysis/munging ecosystem has matured far more than the equivalent in Python. These days, writing (tabular) data transformation code in R is often far cleaner, clearer and less error prone than any other platform imo.
- veddox 6y ago
  > I learned about closures, higher order functions, OOP, testing and interacting with API's from it, and it definitely gave me insight into how computers worked. Well done, I'm honestly impressed :D > remember that it's a 90's era clone of a 70's era language That's not an excuse. Lisp is two decades older. > I find your disdain for R a little annoying ... I'm sorry that you have to deal with horrible legacy R code This has nothing to do with legacy code - I rarely have to touch that. (Scientific code is rarely reused...). R does offer a lot of great features, I'll grant you that. It has a huge library with great functions for doing statistical analysis and really neat graphics - exactly what it was designed for. As a biologist, I cannot imagine not using it. But, and this is a big but, as a programming language it is absolutely horrible. I've worked with half a dozen languages, and none of them are anywhere near as big a pain in the backside as R. Most of that has to do with the terrible incongruity of a plethora of mutually-incompatible data types. You know, you have one function that will only work on a data frame, but somehow your data ended up as a list; or you want strings or numbers but have factors; that kind of thing. In no other language do I have to do as much googling while coding, because even basic operations are so willy-nilly idiosyncratic in their details that I keep forgetting how to do them properly. The "tidyverse" and related packages do a lot to make the experience less painful, but pure R remains a linguistic nightmare.

by veddox 6y ago

disgruntledphd2 6y ago

That's not really its purpose though, right? R is "an environment for statistical computing and graphics". Note that it doesn't talk about software engineering at all. I agree with you that R has lots of rough edges, but please remember that it's a 90's era clone of a 70's era language (S) and a lot of those rough edges and corners are legacies from that time. I completely agree that the naming conventions (i.e. the absence of same) are super annoying, but again it's a tradeoff for the decades of statistical computing knowledge embedded in the system of R. I find your disdain for R a little annoying, and while I'm probably not a computer scientist, it was my first language and I learned about closures, higher order functions, OOP, testing and interacting with API's from it, and it definitely gave me insight into how computers worked. I'm sorry that you have to deal with horrible legacy R code, but that's no reason to throw the baby out with the bathwater.

claytonjy 6y ago
I really appreciate this sentiment as a data person who learned R first, now works in python, and helps non-engineer scientists write R programs for research. I've had great luck teaching scientists to think in a function-driven way, where functions are pure-as-possible and inputs are never mutated (which doesn't need to be discussed as R makes the opposite quite unnatural). I can't imagine teaching these same folks about python, custom classes, and why you need pd.DataFrame.copy() all over the place.
- SJetKaran 6y ago
  ` and why you need pd.DataFrame.copy() all over the place.` Thanks for saying this! Also, in the past decade, the R data analysis/munging ecosystem has matured far more than the equivalent in Python. These days, writing (tabular) data transformation code in R is often far cleaner, clearer and less error prone than any other platform imo.
veddox 6y ago
> I learned about closures, higher order functions, OOP, testing and interacting with API's from it, and it definitely gave me insight into how computers worked. Well done, I'm honestly impressed :D > remember that it's a 90's era clone of a 70's era language That's not an excuse. Lisp is two decades older. > I find your disdain for R a little annoying ... I'm sorry that you have to deal with horrible legacy R code This has nothing to do with legacy code - I rarely have to touch that. (Scientific code is rarely reused...). R does offer a lot of great features, I'll grant you that. It has a huge library with great functions for doing statistical analysis and really neat graphics - exactly what it was designed for. As a biologist, I cannot imagine not using it. But, and this is a big but, as a programming language it is absolutely horrible. I've worked with half a dozen languages, and none of them are anywhere near as big a pain in the backside as R. Most of that has to do with the terrible incongruity of a plethora of mutually-incompatible data types. You know, you have one function that will only work on a data frame, but somehow your data ended up as a list; or you want strings or numbers but have factors; that kind of thing. In no other language do I have to do as much googling while coding, because even basic operations are so willy-nilly idiosyncratic in their details that I keep forgetting how to do them properly. The "tidyverse" and related packages do a lot to make the experience less painful, but pure R remains a linguistic nightmare.