JD Long JD Long - 1 year ago 96
R Question

Thinking in Vectors with R

I know that R works most efficiently with vectors and looping should be avoided. I am having a hard time teaching myself to actually write code this way. I would like some ideas on how to 'vectorize' my code. Here's an example of creating 10 years of sample data for 10,000 non unique combinations of state (

), plan1 (
) and plan2 (

starttime <- Sys.time()

while (i<10000) {
for (years in seq(1991,2000)) {
year <-c(year,years)
Sys.time() - starttime

This takes about 8 minutes to run on my laptop. I end up with 4 vectors, each with 100,000 values, as expected. How can I do this faster using vector functions?

As a side note, if I limit the above code to 1000 loops on i it only takes 2 seconds, but 10,000 takes 8 minutes. Any idea why?

Answer Source

Clearly I should have worked on this for another hour before I posted my question. It's so obvious in retrospect. :)

To use R's vector logic I took out the loop and replaced it with this:

st <-   sample(c(12,17,24),10000,prob=c(20,30,50),replace=TRUE)
p1 <-   sample(c(12,17,24),10000,prob=c(20,30,50),replace=TRUE)
p2 <-   sample(c(12,17,24),10000,prob=c(20,30,50),replace=TRUE)
year <- rep(1991:2000,1000)

I can now do 100,000 samples almost instantaneous. I knew that vectors were faster, but dang. I presume 100,000 loops would have taken over an hour using a loop and the vector approach takes <1 second. Just for kicks I made the vectors a million. It took ~2 seconds to complete. Since I must test to failure, I tried 10mm but ran out of memory on my 2GB laptop. I switched over to my Vista 64 desktop with 6GB ram and created vectors of length 10mm in 17 seconds. 100mm made things fall apart as one of the vectors was over 763mb which resulted in an allocation issue with R.

Vectors in R are amazingly fast to me. I guess that's why I am an economist and not a computer scientist.

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