(This article was first published on

** uu kk**, and kindly contributed to

R-bloggers)
My programming experience progressed mostly along the lines of: C, C++, shell, Java, ~~Java~~, Ruby, Python, Java, ~~Java~~. Only recently have I started exploring the likes of Haskell, Erlang and R. Well that evolution bit me a little while back when I tried using an iterative approach in R where a vectorized solution would have been better.

I was dealing with a vector of timestamps that were formatted as ‘seconds since the epoch’ and what I wanted was to limit that vector to weekend timestamps only.

My naive approach was to construct a simple loop over the values and apply a function to each element. I was only dealing with about 20,000 elements but the time to do this was painfully slow – roughly 20 seconds – so I started investigating an apply-like approach. R provides several ways to do this depending on the input/output requirements: lapply, sapply, and vapply. All three resulted in behavior similar to the simple loop.

The function to test for weekend-ness is as follows:

is.weekend <- function(x) {

tm <- as.POSIXlt(x,origin="1970/01/01")

format(tm,"%a") %in% c("Sat","Sun")

}

I don’t know the specific details of date/time conversion in R but I was pretty sure that this was not the bottleneck. After a little searching I came upon a different approach. Instead of looping over each element I should have been passing the entire vector around to the functions. I believe that the apply functions take the vector as an argument but do the manual loop internally. However, R supports a more native approach to handling vectors: vectorized operations.

__Looping:__

use.sapply <- function(data) {

data[sapply(data$TIME,FUN=is.weekend),]

}

system.time(v <- use.sapply(csv.data))

user system elapsed

19.456 6.492 25.951

__Vectorized:__

use.vector <- function(data) {

data[is.weekend(data$TIME),]

}

system.time(v <- use.vector(csv.data))

user system elapsed

0.032 0.020 0.052

Duly noted.

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** uu kk**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...