Time series plots with ggplot

Hi,
I am newbie user of ggplot and would like some assistance in
implementing time series plots.

I'd like to know how the tsdiag plot can be made in ggplot?

It's not particularly easy because the code for tsdiag interweaves
computing statistics and then plotting them.  However, with a bit of
effort you can disentangle them and create the equivalent plots with
ggplot2.

# What does tsdiag look like?
fit <- arima(lh, c(1,0,0))
tsdiag(fit)

# Need to extract the calculations from the existing
# plotting code.  I started by looking at the contents of
# stats:::tsdiag.Arima

# Standardised residuals

stdres <- as.numeric(resid(fit) / sqrt(fit$sigma2))
qplot(seq_along(stdres), stdres, yend = 0, xend = seq_along(stdres),
geom="segment") +
  geom_hline(colour = "grey50")

# ACF

acf <- acf(resid(fit), plot = F)
acf_df <- data.frame(
  acf = acf$acf,
  lag = acf$lag
)
qplot(lag, acf, data = acf_df, yend = 0, xend = lag, geom="segment") +
  geom_hline() + geom_point()

# To figure out the confidence bands, we need to dig into the code
# for stats:::plot.acf.  Unfortunately this isn't available in its own
# method, so I'm not entirely sure I've extracted it correctly
ci <- 0.95
clim <- qnorm((1 + ci)/2) / sqrt(acf$n.used)

qplot(lag, acf, data = acf_df, yend = 0, xend = lag, geom="segment") +
  geom_hline(colour = "grey50") +
  geom_point() +
  geom_hline(yintercept = c(-clim, clim), colour = "darkblue")


# Finally the code for the Ljung-Box statistic
ljungbox <- function(i, rs) {
  Box.test(rs, i, type = "Ljung-Box")$p.value
}
acf_df$pvalue <- sapply(acf_df$lag, ljungbox, series = resid(fit))

qplot(lag, pvalue, data = acf_df) +
  geom_hline(colour = "grey50") +
  geom_hline(yintercept = 0.05, colour = "darkblue")

Regards,

Hadley

http://had.co.nz/