# load libraries
library(lattice) # nice looking plots
library(plyr) # advanced aggregation functions

# generate 100 random obs
set.seed(1)
x <- rnorm(100)

# generate treatment labels
treatment <- rep(letters[1:5], each=4)

# generate depth labels
depth <- rep(c('0-10', '10-20'), 50)

# combine into a single dataframe
d <- data.frame(x, treatment, depth)

# check out the dataframe:
str(d)
head(d)


# visually check data with box-whisker plot
bwplot(x ~ treatment:depth, data=d, scales=list(y=list(tick.number=10, cex=0.75), x=list(rot=45, cex=0.75)), ylab='Measured Variable', xlab='Treatment / Group')


# calculate median by treatment and depth
aggregate(d$x, by=list(d$treatment, d$depth), median)

# Group.1 is the treatment
# Group.2 is the depth
# x is the median
   Group.1 Group.2            x
1        a    0-10  0.382152173
2        b    0-10  0.347044867
3        c    0-10  0.384062345
4        d    0-10  0.499198983
5        e    0-10 -0.191705870
6        a   10-20 -0.005618922
7        b   10-20  0.066331780
8        c   10-20  0.328471014
9        d   10-20 -0.049369325
10       e   10-20 -0.097184000


# another approach using ddply()
# compute a summary by treatment X depth
# returning the result as a nice data frame
ddply(d, .(treatment, depth), function(i) summary(i$x))

# result looks like this:
   treatment depth    Min.  1st Qu.    Median      Mean 3rd Qu.   Max.
1          a  0-10 -0.8356 -0.46760  0.382200  0.376500  0.8635 2.4020
2          b  0-10 -1.8050 -0.55550  0.347000  0.006561  0.5673 1.0630
3          c  0-10 -0.5425 -0.04595  0.384100  0.371000  0.5507 1.5120
4          d  0-10 -1.3770 -0.38070  0.499200  0.339300  1.1520 1.5870
5          e  0-10 -1.2770 -0.43100 -0.191700 -0.115700  0.4459 1.1000
6          a 10-20 -1.9890 -0.22380 -0.005619 -0.079500  0.4634 1.5950
7          b 10-20 -1.4710 -0.60670  0.066330  0.013510  0.6370 1.4660
8          c 10-20 -0.7099 -0.25470  0.328500  0.360600  0.7653 2.1730
9          d 10-20 -2.2150 -0.80430 -0.049370 -0.126100  0.4917 1.9800
10         e 10-20 -1.0440 -0.54830 -0.097180 -0.057270  0.4457 0.9438