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Julio, You mention that p1 and p2 have the same x-axis, but the reordering you do based on mean does not make them the same. <code>p1</code>'s axis goes "one --> two --> three" while <code>p2</code>'s axis goes "two --> one --> three". Is this intentional? Regardless, <code>ggplot</code> offers a few other solutions to combine these plots into one, namely <code>colour</code> and <code>faceting</code> (which you may have already tried?). The first step to either of these is to <code>melt</code> your data.frame to long format. We will identify the id variable "Type" and <code>melt</code> assumes the rest of the columns are to be <code>melted</code>. <pre><code>test.m <- melt(test, id.var = "Type") </code></pre> A quick check of the structure of the new object indicates most everything is in line, except the levels for type are a bit out of whack: <pre><code>> str(test.m) 'data.frame': 12 obs. of 3 variables: $ Type : Factor w/ 3 levels "One","Three",..: 1 3 1 1 2 2 1 3 1 1 ... $ variable: Factor w/ 2 levels "RatingA","RatingB": 1 1 1 1 1 1 2 2 2 2 ... $ value : int 3 5 5 7 4 8 36 53 57 74 ... </code></pre> So let's rearrage the levels: <pre><code>test.m$Type <- factor(test.m$Type, c("One", "Three", "Two"), c("One", "Two", "Three")) </code></pre> Now for the plotting. With colour: <pre><code>ggplot(test.m, aes(x = Type, y = value, group = variable, colour = variable)) + stat_summary(fun.y = "mean", geom = "point") </code></pre> or with facets: <pre><code>ggplot(test.m, aes(x = Type, y = value, group = variable)) + stat_summary(fun.y = "mean", geom = "point") + facet_grid(variable ~ ., scales = "free") </code></pre> Note I used the <code>scales = "free"</code> argument in the faceting so that each plot has its' own scale. Simply remove that argument if that's not the effect you want.
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