How to plot similarity of two datasets? I'm performing some simulations, and at the end I get a CSV file with three columns. One column holds the values for the x-axis, which was also input to the simulation and theoretical calculations, second one holds theoretically expected values, and the other column holds the values obtained by the simulation. I was planning to plot something like this:



But that does not look good in my case, as the values in y-axis normally keep doubling, and the values for the x-axis exponentially increase, so most of the points end up getting collected at the lower left part, near the intersection of x-axis and y-axis of the plot. Therefore, I need a different way to plot my data, which will be more visually appealing and inform how close the simulation results are to the theoretical expected ones. For example, some of my values can be seen below (and they keep increasing in such a way):

x         = [2, 4, 8, 16, 32, 64] # partially removed for brevity
expected  = [47.9995, 95.9783, 191.9127, 383.9708, 767.8831] # partially removed for brevity 
simulated = [48, 96, 191.8, 383.8, 767.4] # partially removed for brevity


What is a good way to plot such a data that doubles in the y-axis and exponentially increases on the x-axis all the time, and to view how similar the two datasets actually are?
Here is an example of r lattice xyplot using log scale on the x axis and the difference of your two measures I(expected - simulated)

df &lt;- data.frame(
x         = c(2, 4, 8, 16, 32, 64),
expected  = c(47.9995, 95.9783, 
191.9127, 383.9708, 767.8831, 
1457.2771),
simulated = c(48, 96, 191.8, 383.8, 
767.4, 1458.1228))
xy &lt;- xyplot(I(expected - simulated) ~ x ,   
auto.key=TRUE,
data =  df ,    type=c("p","g"),
scales=list(x=list(log = 10) ),   
ylab="difference expected - simulated", xlab="x", main="Simulation Results" )
print (xy)


Note, that I added a 6th result to your sample data,that was missing.