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i've used R for several large projects. it sucks in several ways for the projects i worked on: 1. it is extremely slow for numerics. slower than MATLAB. slower

by dododo 16y ago

i've used R for several large projects. it sucks in several ways for the projects i worked on: 1. it is extremely slow for numerics. slower than MATLAB. slower than python (with numpy). after talking to several stats people, it seems pretty much everyone ends up writing most of their code in C when using R. (in contrast, i didn't find this necessary in python or MATLAB for similar projects.) 2. its syntax is quite clunky. want to concatenate two strings? paste(string_a, string_b, sep=''). radford neal has a series of blog posts on R's design flaws: http://radfordneal.wordpress.com/2008/09/21/design-flaws-in-r-3-%E2%80%94-zero-subscripts/ http://radfordneal.wordpress.com/2008/09/21/design-flaws-in-... 3. uninformative error reporting. by default, the stack trace isn't printed. even if it is, often the errors don't really tell you what went wrong. i don't see any advantage to R over python. yield makes a nice replacement for the lazy evaluation of R.

revorad 16y ago
Regarding syntax, what I find most annoying is the lack of a standard way to do common things across different functions. For example, to ignore missing values in a dataset (denoted by NA), there are as many variants are there are functions: na.rm, na.omit, na.action etc. So there's different syntax for doing the same thing in different parts of the language.
- xtho 16y ago
  na.rm is a argument name, na.omit a function, na.action an attribute. They are not the same thing. Anyway, you're right with respect to the lack of standard way. One thing I personally find most annoying is function naming. Some functions use the dot convention (do.this), some use camel case (doThis), some use underscores (do_this) etc. And what is most annyoing: this is even true for novel functions that were just introduced in recent releases.
  - revorad 16y ago
    Thanks for the correction, but I think you made my point even clearer.
thesnark 16y ago
I agree with all of your points, yet I still find myself using R on a day to day basis instead of python. My reason for doing so is the large number of statistical packages that exist for R. I know that scipy exists, but it does not seem to have nearly the same coverage as CRAN + base libraries... Are there other python libraries I should look at?
- dododo 16y ago
  it's true that CRAN+Bioconductor have a lot of coverage of statistics. if you need to fit a particular kind of model and it's not using MCMC then perhaps it's the best you can do if someone has already written the code. we use sage (http://www.sagemath.org/ http://www.sagemath.org/) which includes a lot of the common python numerics: numpy, scipy, matplotlib, networkx. it provides nice interfaces to R and gp/pari. i also use mayavi2 for 3d plots (something i could never get R to do well under linux...) enthought have a lot of nice things for python and scientific computing. there's also pymc which i've not used (i just write the MCMC code directly).

by dododo 16y ago

revorad 16y ago

Regarding syntax, what I find most annoying is the lack of a standard way to do common things across different functions. For example, to ignore missing values in a dataset (denoted by NA), there are as many variants are there are functions: na.rm, na.omit, na.action etc. So there's different syntax for doing the same thing in different parts of the language.

xtho 16y ago
na.rm is a argument name, na.omit a function, na.action an attribute. They are not the same thing. Anyway, you're right with respect to the lack of standard way. One thing I personally find most annoying is function naming. Some functions use the dot convention (do.this), some use camel case (doThis), some use underscores (do_this) etc. And what is most annyoing: this is even true for novel functions that were just introduced in recent releases.
- revorad 16y ago
  Thanks for the correction, but I think you made my point even clearer.

thesnark 16y ago

I agree with all of your points, yet I still find myself using R on a day to day basis instead of python. My reason for doing so is the large number of statistical packages that exist for R. I know that scipy exists, but it does not seem to have nearly the same coverage as CRAN + base libraries... Are there other python libraries I should look at?

dododo 16y ago
it's true that CRAN+Bioconductor have a lot of coverage of statistics. if you need to fit a particular kind of model and it's not using MCMC then perhaps it's the best you can do if someone has already written the code. we use sage (http://www.sagemath.org/ http://www.sagemath.org/) which includes a lot of the common python numerics: numpy, scipy, matplotlib, networkx. it provides nice interfaces to R and gp/pari. i also use mayavi2 for 3d plots (something i could never get R to do well under linux...) enthought have a lot of nice things for python and scientific computing. there's also pymc which i've not used (i just write the MCMC code directly).