March 04, 2016 Code R pbapply tutorials
pbapply
is a lightweight R extension package
that adds progress bar to vectorized R functions (*apply).
The latest addition in version 1.2-0
is the timerProgressBar function which adds a text based
progress bar with timer that all started with
this pull request.
This package is the least scientifically sophisticated piece of software that I have worked on, but still it seems to be popular based on reverse dependencies and download statistics. The reason for the buzz is probably related to the packages solving a common frustration. The frustration stems in the fact that (1) vectorized functions do not provide any feedback about how long the process is going to take; and (2) there is no unified interface to progress bars.
Hadley Wickham’s plyr package came to the rescue. But to my taste that was an overkill. And honestly, what is the fun in using a package that someone else wrote? So I decided to integrate the available progress bar types in a single lightweight package, with options to manipulate the type and style.
Let us see an example from the package help pages:
library(pbapply) # load package
set.seed(1234) # for reproducibility
n <- 200 # sample size
x <- rnorm(n) # predictor
y <- rnorm(n, model.matrix(~x) %*% c(0,1), sd=0.5) # observations
d <- data.frame(y, x) # data
mod <- lm(y ~ x, d) # call to lm
ndat <- model.frame(mod)
B <- 100 # number of bootstrap samples
## bootstrap IDs
bid <- sapply(1:B, function(i) sample(nrow(ndat), nrow(ndat), TRUE))
## bootstrap function
fun <- function(z) {
if (missing(z))
z <- sample(nrow(ndat), nrow(ndat), TRUE)
coef(lm(mod$call$formula, data=ndat[z,]))
}
The function takes a resampling vector as argument (here we use
columns from the pre-defined bid matrix). When the argument is missing,
it generates the vector itself. This way we can use the same
function in different vectorized functions.
First let’s look at the standard *apply functions, printing out
system time for comparison.
system.time(res1 <- lapply(1:B, function(i) fun(bid[,i])))
## user system elapsed
## 0.123 0.008 0.095
system.time(res2 <- sapply(1:B, function(i) fun(bid[,i])))
## user system elapsed
## 0.095 0.000 0.096
system.time(res3 <- apply(bid, 2, fun))
## user system elapsed
## 0.097 0.002 0.099
system.time(res4 <- replicate(B, fun()))
## user system elapsed
## 0.091 0.001 0.092
Here is the pb*apply implementation, trying different types and
styles of progress bar. Available progress bar types are timer, text,
Windows (on Windows only), TclTk, or none.
## the default is the shiny new timer progress bar
op <- pboptions(type="timer")
system.time(res1pb <- pblapply(1:B, function(i) fun(bid[,i])))
## |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% ~00s
## user system elapsed
## 0.163 0.010 0.173
pboptions(op) # reset defaults
## text progress bar with percentages
pboptions(type="txt")
system.time(res2pb <- pbsapply(1:B, function(i) fun(bid[,i])))
## |++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
## user system elapsed
## 0.164 0.007 0.174
pboptions(op)
## alternative style with '=' as character
pboptions(type="txt", style=1, char="=")
system.time(res3pb <- pbapply(bid, 2, fun))
##==================================================
## user system elapsed
## 0.144 0.006 0.155
pboptions(op)
## now we use ':' isn't it nice?
pboptions(type="txt", char=":")
system.time(res4pb <- pbreplicate(B, fun()))
## |::::::::::::::::::::::::::::::::::::::::::::::::::| 100%
## user system elapsed
## 0.152 0.007 0.162
pboptions(op)
There is clearly an overhead when comparing system times. Which is not a surprise. More calculations take more time. The good news is that the overhead do not increase with the size of the problem, so it only takes an extra second or so.
Install the package from your nearest
CRAN mirror
by install.packages("pbapply") and
let me know any issues you might run into
on the GitHub development site.
UPDATE
Elapsed and remaining time is now shown with progress bar or throbber. Version 1.2-1 is now on CRAN.
I moved to Canada in 2008 to start a postdoctoral fellowship with Prof. Subhash Lele at the stats department of the University of Alberta. Subhash at the time just published a paper about a statistical technique called data cloning. Data cloning is a way to use Bayesian MCMC algorithms to do frequentist inference. Yes, you read that right.
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