Hi,
i do have a dataframe representing data from a repeated experiment. PID
is a subject identifier, Time are timepoints in an experiment which was
repeated twice. For each subject and all three timepoints there are 2
sets of four values.
df <- data.frame(PID = c(rep("A", 12), rep("B", 12), rep("C", 12)),
Time = rep(c(0, 0, 0, 0, 30, 30, 30, 30, 60, 60, 60,
60), 3),
Dset = rep(c(1, 2),18),
Val1 = rnorm(36),
Val2 = rnorm(36),
Val3 = rnorm(36),
Val4 = rnorm(36))
You can plot the data nicely with x=Time and y=Val1 by grouping PID and
facetting for Dset.
p <- ggplot(df) +
geom_line(aes(x=Time,y=Val1,group=PID)) +
geom_point(aes(x=Time,y=Val1,colour=PID)) +
facet_grid(. ~ Ecc)
theme_set(theme_bw())
p
I would now like to normalize these data to the mean of the two values
at Time = 0 for each subject (so having plots in % of the mean Time=0
value rather than absolute values).
Any suggestions are welcome!
Herbert
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