To compute an ACF or PACF, use the forecast package.

Install via Tools > Install Packages… menu.

library("forecast")

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: timeDate

Loading required package: methods

This is forecast 7.3 

n <- 10000; time <- 1:n
wn <- rt(n, df=3)
Acf(wn)

ma1 <- filter(wn, c(1, 0.25), method="convolution", sides=1)
ma2 <- filter(wn, c(1, 0.1, 0.25), method="convolution", sides=1)
par(mfrow=c(1,2))
Acf(ma1); Acf(ma2)

ar1 <- filter(wn, 0.5, method="recursive")
ar2 <- filter(wn, c(0.4, -0.2), method="recursive")
par(mfrow=c(1,2))
Acf(ar1); Acf(ar2)

Pacf(wn)

par(mfrow=c(1,2))
Pacf(ar1)
Pacf(ar2)

par(mfrow=c(1,2))
Pacf(ma1)
Pacf(ma2)

par(mfrow=c(1,2))
Acf(wn); Acf(diff(wn))

# Amazon.com Stock Price, Daily, May 16, 1997 - Present
# Adjusted Closing Price.
# Source: http://finance.yahoo.com/q?s=amzn
url <- "http://ptrckprry.com/course/forecasting/data/amazon.csv"
data <- read.csv(url)
date <- as.Date(data$date)
time <- 1:length(date)
AMZN <- data$amazon

plot(date, AMZN, type="l", col=2)

log.AMZN <- log(AMZN)
plot(date, log.AMZN, type="l", col=2)

par(mfrow=c(1,2))
Acf(log.AMZN, lag.max=100)
Pacf(log.AMZN, lag.max=100)

diff.log.AMZN <- c(NA, diff(log.AMZN))
plot(date, diff.log.AMZN, type="l", col=2)

par(mfrow=c(1,2))
Acf(diff.log.AMZN)
Pacf(diff.log.AMZN)

diff2.log.AMZN <- c(NA, diff(diff.log.AMZN))
plot(date, diff2.log.AMZN, type="l", col=2)

par(mfrow=c(1,2))
Acf(diff2.log.AMZN)
Pacf(diff2.log.AMZN)