There are many things you can do to improve speed in R. Byte compiling
is just one of them.

This chapter in Hadley Wickham's excellent Advanced R book covers both
profiling and byte compiling.

http://adv-r.had.co.nz/Profiling.html

I've gotten some stunning improvements in speed through profiling and
careful thought: 3 days to 3 seconds, even.

Sarah

On Thu, Jun 21, 2018 at 6:28 AM, akshay kulkarni <akshay...@hotmail.com> wrote:
> dear members,
>                             I a Day Trader based in INDIA. I use R for my 
> research. I have a function ygusa(snlq,snlcqn) which takes 208 stocks and 
> returns 4 best stocks for the next day(snlq is the list of 208 stocks and 
> snlcqn is their names). However, the execution time is around 2 hrs, making 
> it hard for me.
>
> I recently read in the internet that you can precompile the code in R to make 
> it run faster. Also that you can enable JIT(just in time compilation) from 
> your R session automatically. I came to know that R 3.4.x has JIT enabled in 
> it by default. Is it true? Is it also true that even after enabling JIT in R 
> 3.4.x, the first run of a function is not Byte compiled?
>
> So when I start my R session, download the data, and run ygusa(snlq,snlcqn), 
> it is not byte compiled and therefore it is very slow? Will including the 
> following lines in ygusa solve my problem:
>> require(compiler)
>> enableJIT(3)
>
> ?
>
> Also, instead of compiling the function ygusa every time I run it, can I 
> compile it once and store it, and run that compiled file instead of 
> ygusa(snlq,snlcqn)?
>
> Can you point me to some online resources that can help on this issue?
>
> very many thanks for your time and effort...
> Yours sincerely,
> AKSHAY M KULKARNI
>


-- 
Sarah Goslee
http://www.functionaldiversity.org

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