grouse

library(lattice)

## load data
connection <- url("http://bengarland.com/r/sample_data.txt")
data <- read.table(connection, header = TRUE)

## process data
data.pruned <- subset(data, select = c(-Rep, -Factor1, -SubFactor))

data.by <- with(data, list(Factor1=Factor1, SubFactor=SubFactor))
data.mean <- aggregate(data.pruned, data.by, mean)

stack.SubFactor <- function(subfactor) {
  stacked <- stack(subset(data.mean.bob, SubFactor == subfactor))
  cbind(stacked, SubFactor = subfactor)
}

data.mean.bob <- subset(data.mean, Factor1 == "BOB")
data.mean.bob.stacked <- do.call(rbind, lapply(levels(data.mean.bob$SubFactor),
                                               stack.SubFactor))

data$Time <- c(0, 30, 40, 50)[data$Rep]
data.bob.p <- subset(data, Factor1 == "BOB" & SubFactor == "P")

## graph 1
barchart(Measurement1 ~ SubFactor | Factor1, data.mean,
         main = "Mean of Measurement1")

## graph 2
xyplot(values ~ ind, data.mean.bob.stacked, group = SubFactor, type = "b")

## graph 3
barchart(Measurement1 + Measurement2 + Measurement3 + Measurement4
         + Measurement5 ~ SubFactor | Factor1, data.mean,
         auto.key = TRUE)

## graph 4
# assuming that rep A is T1, rep B is T2, etc.
xyplot(Measurement1 + Measurement2 + Measurement3 + Measurement4
       + Measurement5 ~ Time, data.bob.p, type = "b", auto.key = TRUE)