Is it possible to jitter two ggplot geoms in the same way?

I ended up generating a uniform distribution to solve this problem.

I had to address the same underlying problem today. I create one plot, jittering the points, and then I create a second plot that essentially zooms in on a subsection of the first. It's dissonant and distracting if the points move around.

Following is a demo of the problem and my solution. I don't use ggplot for this plot, but the same concept applies. I make a uniform distribution, one value for each value I need to jitter. I add it to the source dataframe so that each time I take a subset, the jitter value corresponds to the same original data value.

data(airquality)
someDataset= airquality 
someDataset$color="black"
someDataset$color[someDataset$Month==8 & someDataset$Wind==9.7]="red"
## jitter gives different results each time it's run
for (fZoom in c(TRUE, FALSE)){
    if (fZoom) myAirQuality = someDataset[someDataset $Wind >7.5 & someDataset $Wind < 11.5,] 
    else myAirQuality = someDataset[someDataset $Wind >8.5 & someDataset $Wind < 10.5,]
    quartz("Using Jitter")
    plot(myAirQuality $Wind ~ jitter(myAirQuality $Month), col= myAirQuality$color)
    }

someDataset$MonthJit=runif(nrow(someDataset), min=-0.2, max=0.2)
for (fZoom in c(TRUE, FALSE)){
    if (fZoom) myAirQuality = someDataset[someDataset $Wind >7.5 & someDataset $Wind < 11.5,] 
    else myAirQuality = someDataset[someDataset $Wind >8.5 & someDataset $Wind < 10.5,]
    quartz("Using runif")
    plot(myAirQuality $Wind ~ c(myAirQuality $Month + myAirQuality $MonthJit), col= myAirQuality$color)
    }

This is a weakness in the current ggplot2 syntax - there's no way to work around it except to add the jitter yourself.

Or you could do something like this:

ggplot(baseball, aes(round(year,-1) + as.numeric(factor(lg)), sb, color = factor(lg))) +
  stat_summary(fun.data="mean_cl_normal") +
  stat_summary(fun.y=mean,geom="line") +
  coord_cartesian(ylim=c(0,40))

I think so, by setting the seed to be the same in the two instances:

p=ggplot(baseball,aes(x=round(year,-1),y=sb,color=factor(lg)))
myseed = 2010
set.seed(myseed)
p=p+stat_summary(fun.data="mean_cl_normal",
  position=position_jitter(width=3,height=0))+coord_cartesian(ylim=c(0,40))
set.seed(myseed)
p+stat_summary(fun.y=mean,geom="line",
           position=position_jitter(width=3,height=0))

This ensures that the random number generator is sent back to the same starting position as was used in the initial call. However I don't know how you could extract the random increments added to the values.