summarize data

data.R

data.last10.sum <- ddply(data.last10, c("genotype", "condition"), summarise,
                         fepsp.slope.percent.mean = mean(fepsp.slope.percent, na.rm=T),
                         fepsp.slope.percent.sd = sd(fepsp.slope.percent, na.rm=T))

Ns <- ddply(data, c("genotype", "condition"), summarise,
            N = sum(time == 0))
data.last10.sum$N <- cbind(Ns[,3])

data.last10.sum <- ddply(data.last10.sum, c("genotype", "condition"), transform,
                         fepsp.slope.percent.cv =
                         fepsp.slope.percent.sd / fepsp.slope.percent.mean)


data.last10.sum$fepsp.slope.percent.sem=data.last10.sum$fepsp.slope.percent.sd/sqrt(data.last10.sum$N)
data.last10.sum$UpperCI = data.last10.sum$fepsp.slope.percent.mean + data.last10.sum$fepsp.slope.percent.sem
data.last10.sum$LowerCI = data.last10.sum$fepsp.slope.percent.mean - data.last10.sum$fepsp.slope.percent.sem


print(data.last10.sum)

data.cis <-  ddply(data, c("time", "genotype", "condition"), summarise,
                   fepsp.slope.percent.mean = mean(fepsp.slope.percent, na.rm=T),
                   fepsp.slope.percent.sd = sd(fepsp.slope.percent, na.rm=T))
data.n <- ddply(data, c("time", "genotype", "condition"), summarise,
                fepsp.slope.percent.length = length(fepsp.slope.percent))
data.cis$SEM = data.cis$fepsp.slope.percent.sd/sqrt(data.n$fepsp.slope.percent.length)
data.cis$ymax = data.cis$fepsp.slope.percent.mean + data.cis$SEM
data.cis$ymin = data.cis$fepsp.slope.percent.mean - data.cis$SEM