This is more or less standard ggplot2 problem. So I will only give an idea how to reproduce this graph. First get the data
dd<-structure(list(no.GREEN=c(1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L,1L,1L,2L,2L,3L,3L,4L,4L),no.RED=c(5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,5L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,7L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L,9L),Messagereceived=structure(c(2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L,2L,1L),.Label=c("blue","red"),class="factor"),Decisionasreceivercode=c(0L,1L,0L,1L,1L,0L,1L,0L,1L,1L,1L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,0L,0L,0L,1L,0L,1L,0L,0L,1L,0L,0L,1L,0L,1L,0L,0L,1L,0L,0L,1L,0L,1L,0L,0L,1L,1L,0L,1L,0L,1L,0L,0L,1L,1L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,1L,0L,0L,1L,1L,0L,0L,0L,1L,0L,0L,0L,1L,1L,1L,0L,1L,1L,0L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,1L,0L,1L,0L,0L,1L,0L,0L,1L,1L,0L,1L,1L,0L,1L,0L,1L,0L,1L,0L,1L,1L,0L,0L,1L,0L,1L,0L,1L,1L,0L,0L,1L,1L,1L,0L,1L,0L,0L,1L,1L,0L,1L,0L,0L,1L,0L,1L,0L,0L,1L,1L,1L,1L,1L,0L,1L,0L,1L,0L,1L,1L,1L,1L,1L,1L,1L,0L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,0L,1L,0L,1L,1L,0L,1L,0L,1L,1L,1L,1L,1L,0L,1L,0L,0L,0L,1L,0L,1L,0L,1L,0L,1L,1L,1L,0L,1L,0L,1L,0L,1L,1L,1L,1L,1L,1L,0L,1L,1L,0L,0L,1L,1L,0L,0L,1L,0L,1L,0L,0L,1L,0L,0L,0L,1L,0L,0L,0L,1L,0L,0L,1L,0L,1L,1L,1L,1L,0L,1L,0L),OptimalResponse=c(1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,0L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L)),.Names=c("no.GREEN","no.RED","Messagereceived","Decisionasreceivercode","OptimalResponse"),class="data.frame",row.names=c(NA,-328L))
Then transform it to get the desired statistics:
ddr<-recast(dd,no.GREEN+no.RED+Messagereceived~variable,fun.aggregate=mean,id.var=c("no.GREEN","no.RED","Messagereceived"))
And finaly plot it:
qplot(x=no.GREEN,y=Decisionasreceivercode,data=ddr,colour=no.RED,group=no.RED,geom="line")+facet_wrap(~Messagereceived,ncol=1)
The result will be the following:

I will leave the second graph and the cleaning up of the first one as an exercise :)
Update: There is alternative cleaner way to get the aggregation:
ddr<-aggregate(dd$Decision,by=as.list(dd[,1:3]),mean)
We lose the name of the variable this way, but save some time with not typing three names two times.