if you really want to use the mid point of each bin as a scaling factor:
d<-rgamma(100,5,1.5) # sample
z<-hist(d,plot=FALSE) # make histogram, i.e., divide into bins and count up
co<-z$counts # original counts of each bin
z$counts<-z$counts*z$mids # scaled by mids of the bin
plot(z, xlim=c(0,10),ylim=c(0,max(z$counts))) # plot scaled histogram
par(new=T)
plot(z$mids,co,col=2, xlim=c(0,10),ylim=c(0,max(z$counts))) # overplot original counts
instead, if you want to use the actual value of each sample point as a scaling factor:
d<-rgamma(100,5,1.5)
z<-hist(d,plot=FALSE)
co<-z$counts # original counts of each bin
z$counts<-aggregate(d,list(cut(d,z$breaks)),sum)$x # sum up the value of data in each bin
plot(z, xlim=c(0,10),ylim=c(0,max(z$counts))) # plot scaled histogram
par(new=T)
plot(z$mids,co,col=2, xlim=c(0,10),ylim=c(0,max(z$counts))) # overplot original counts