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I'm creating a facetted plot to view predicted vs. actual values side by side with a plot of predicted value vs. residuals. I'll be using shiny to help explore the results of modeling efforts using different training parameters. I train the model with 85% of the data, test on the remaining 15%, and repeat this 5 times, collecting actual/predicted values each time. After calculating the residuals, my data.frame looks like this: head(results) act pred resid 2 52.81000 52.86750 -0.05750133 3 44.46000 42.76825 1.69175252 4 54.58667 49.00482 5.58184181 5 36.23333 35.52386 0.70947731 6 53.22667 48.79429 4.43237981 7 41.72333 41.57504 0.14829173 What I want: Side by side plot of pred vs. act and pred vs. resid The x/y range/limits for pred vs. act to be the same, ideally from min(min(results$act), min(results$pred)) to max(max(results$act), max(results$pred)) The x/y range/limits for pred vs. resid not to be affected by what I do to the actual vs. predicted plot. Plotting for x over only the predicted values and y over only the residual range is fine. In order to view both plots side by side, I melt the data: library(reshape2) plot <- melt(results, id.vars = "pred") Now plot: library(ggplot2) p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw() p <- p + facet_wrap(~variable, scales = "free") print(p) That's pretty close to what I want: What I'd like is for the x and y ranges for actual vs. predicted to be the same, but I'm not sure how to specify that, and I don't</e

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