The downside is R is really slow...I have coded monte carlo simulations in c++ that take seconds that take R minutes.
This could not be more true. I've tried experimenting with the parallel processing packages for R but to no success.
The downside is R is really slow...I have coded monte carlo simulations in c++ that take seconds that take R minutes.
This is definitely a problem. There are some packages trying to solve this but the degree of success varies.To me the downside of R is the plotting - not interactive, so it's much harder to explore the data naturally.
What I might end up doing is feeding R through Python and matplotlib via RPy.This is definitely a problem. There are some packages trying to solve this but the degree of success varies.
What I might end up doing is feeding R through Python and matplotlib via RPy.
I've tried some of those packages and overall it's still pretty painful to get what you want, although there are some good parts.
I love R. I find it easier than Matlab (the syntax is more similar to c++ and it is easier to define and run functions) but not less powerful.
The downside is R is really slow...I have coded monte carlo simulations in c++ that take seconds that take R minutes.
Which of the two would you recommend more/what are the essential differences?I have used RPy and RPy2.
To me the downside of R is the plotting - not interactive, so it's much harder to explore the data naturally.
As far as speed, vectorize the code.
I really don't know what you are doing but, in my experience, R is never the problem.
I don't disagree that R could be slower than C++ but for numerical algorithms, you can usually make R run as fast as C++... and you can usually rewrite the slow pieces of the R programs in C or C++.
I've run the exact same time series functions on the same data in R and Matlab. It took <1 second in Matlab and 30 seconds in R.
if you can code something in bash that was written in R, it means that R was used for the wrong reasons. R strengths are far away from what bash can do.I'm working on a project, where I have to recode / impute categorical variables. On a 2046 x 11 test dataset, R took somewhere between 15 - 30 secs on average. I recoded that part in Bash and it gets the same job done in about a second. That said, R does have its strong points : easy EDA (exploratory data analysis) and plotting, very mature stats and machine learning packages, and it integrates with other major languages...