Split/apply and plyr with a large data set, and measuring the time it takes to execute a function. See ?system.time
winddata <- read.csv("ballarat.csv", header = TRUE, sep = ",")
winddata$Date <- as.POSIXct(winddata$Time, format = "%d/%m/%Y %H:%M", tz = "Australia/Melbourne")
# Create a unique indicator for each day of the 12 years
winddata$day_index <- paste(format(winddata$Date, "%Y"), format(winddata$Date,
"%j"), sep = "_")
# then do the usual split-apply thing to loop over each day.
# in the simplest case, just using base-R functions you can do something
# like this: I'm limiting the data to the first 200 samples, as it will take
# a while to calculate them all
dim(winddata)
## [1] 192998 5
# using 'system.time' to measure the elapsed time to execute the function
# limited to 20000 samples!
system.time(my.summary <- sapply(split(winddata[1:20000, ], winddata$day_index[1:20000]),
function(x) {
# put your actual function here that returns your desired results
return(c(my.mean = mean(x$windspeed), my.sd = sd(x$windspeed)))
}))
## user system elapsed
## 25.172 0.188 25.463
To make split/apply easier, use “plyr”
library(plyr)
# again, only using the first 20000 samples
# but now using "plyr""
system.time(my.summary <- ddply(winddata[1:20000, ],
.(day_index),
#.parallel = TRUE, # activate in case you've got a parallel backend installed and configured
.progress = "text", # in case it takes longer, a progress bar can be useful
function(x) {
return(data.frame(my.mean = mean(x$windspeed),
my.sd = sd(x$windspeed)))
}))
##
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## user system elapsed
## 1.536 0.004 1.545