What is the best practice of handling time series in R?

I am using R for some statistical analysis of time series. I have tried Googling around, but I can't seem to find any definitive answers. Can any one who knows more please point me in the right direction?

Example:

Let's say I want to do a linear regression of two time series. The time series contain daily data, but there might be gaps here and there so the time series are not regular. Naturally I only want to compare data points where both time series have data. This is what I do currently to read the csv files into a data frame:

library(zoo)
apples <- read.csv('/Data/apples.csv', as.is=TRUE)
oranges <- read.csv('/Data/oranges.csv', as.is=TRUE)
apples$date <- as.Date(apples$date, "%d/%m/%Y")
oranges$date <- as.Date(oranges$date, "%d/%m/%Y")
zapples <- zoo(apples$close,apples$date)
zoranges <- zoo(oranges$close,oranges$date)
zdata <- merge(zapples, zoranges, all=FALSE)
data <- as.data.frame(zdata)

Is there a slicker way of doing this?

Also, how can I slice the data, e.g., select the entries in data with dates within a certain period?