Setting individual axis limits with facet_wrap and scales = “free” in ggplot2

135
votes

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:

enter image description here

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 need that done for the predicted vs. residual plot since the ranges are completely different.

I tried adding something like this for both scale_x_continous and scale_y_continuous:

min_xy <- min(min(plot$pred), min(plot$value))
max_xy <- max(max(plot$pred), max(plot$value))

p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free")
p <- p + scale_x_continuous(limits = c(min_xy, max_xy))
p <- p + scale_y_continuous(limits = c(min_xy, max_xy))

print(p)

But that picks up the min() of the residual values.

enter image description here

One last idea I had is to store the value of the minimum act and pred variables before melting, and then add them to the melted data frame in order to dictate in which facet they appear:

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

min_xy <- min(min(results$act), min(results$pred))
max_xy <- max(max(results$act), max(results$pred))

plot <- melt(results, id.vars = "pred")

plot <- rbind(plot, data.frame(pred = c(min_xy, max_xy),
  variable = c("act", "act"), value = c(max_xy, min_xy)))

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 does what I want, with the exception that the points show up, too:

enter image description here

Any suggestions for doing something like this?

I saw this idea to add geom_blank(), but I'm not sure how to specify the aes() bit and have it work properly, or what the geom_point() equivalent is to the histogram use of aes(y = max(..count..)).

Here's data to play with (my actual, predicted, and residual values prior to melting):

> dput(results)
structure(list(act = c(52.81, 44.46, 54.5866666666667, 36.2333333333333, 
53.2266666666667, 41.7233333333333, 35.2966666666667, 30.6833333333333, 
39.25, 35.8866666666667, 25.1, 29.0466666666667, 23.2766666666667, 
56.3866666666667, 42.92, 41.57, 27.92, 23.16, 38.0166666666667, 
61.8966666666667, 37.41, 41.6333333333333, 35.9466666666667, 
48.9933333333333, 30.5666666666667, 32.08, 40.3633333333333, 
53.2266666666667, 64.6066666666667, 38.5366666666667, 41.7233333333333, 
25.78, 33.4066666666667, 27.8033333333333, 39.3266666666667, 
48.9933333333333, 25.2433333333333, 32.67, 55.17, 42.92, 54.5866666666667, 
23.16, 64.6066666666667, 40.7966666666667, 39.0166666666667, 
41.6333333333333, 35.8866666666667, 25.1, 23.2766666666667, 44.46, 
34.2166666666667, 40.8033333333333, 24.5766666666667, 35.73, 
61.8966666666667, 62.1833333333333, 74.6466666666667, 39.4366666666667, 
36.6, 27.1333333333333), pred = c(52.8675013282404, 42.7682474758679, 
49.0048248585123, 35.5238560262515, 48.7942868566949, 41.5750416040131, 
33.9548164913007, 29.9787449128663, 37.6443975781139, 36.7196211666685, 
27.6043278172077, 27.0615724310721, 31.2073056885252, 55.0886903524179, 
43.0895814712768, 43.0895814712768, 32.3549865881578, 26.2428426737583, 
36.6926037128343, 56.7987490221996, 45.0370788180147, 41.8231642271826, 
38.3297859332601, 49.5343916620086, 30.8535641206809, 29.0117492750411, 
36.9767968381391, 49.0826677983065, 54.4678549541069, 35.5059204731218, 
41.5333417555995, 27.6069075391361, 31.2404889715121, 27.8920960978598, 
37.8505531149324, 49.2616631533957, 30.366837650159, 31.1623492639066, 
55.0456078770405, 42.772538591063, 49.2419293590535, 26.1963523976241, 
54.4080781796616, 44.9796700541254, 34.6996927469131, 41.6227713664027, 
36.8449646519306, 27.5318686661673, 31.6641793552795, 42.8198894266632, 
40.5769177148146, 40.5769177148146, 29.3807781312816, 36.8579132935989, 
55.5617033901752, 55.8097119335638, 55.1041728261666, 43.6094641699075, 
37.0674887276681, 27.3876960746536), resid = c(-0.0575013282403773, 
1.69175252413213, 5.58184180815435, 0.709477307081826, 4.43237980997177, 
0.148291729320228, 1.34185017536599, 0.704588420467079, 1.60560242188613, 
-0.832954500001826, -2.50432781720766, 1.98509423559461, -7.93063902185855, 
1.29797631424874, -0.169581471276786, -1.51958147127679, -4.43498658815778, 
-3.08284267375831, 1.32406295383237, 5.09791764446704, -7.62707881801468, 
-0.189830893849219, -2.38311926659339, -0.541058328675241, -0.286897454014273, 
3.06825072495888, 3.38653649519422, 4.14399886836018, 10.1388117125598, 
3.03074619354486, 0.189991577733821, -1.82690753913609, 2.16617769515461, 
-0.088762764526507, 1.47611355173427, -0.268329820062384, -5.12350431682565, 
1.5076507360934, 0.124392122959534, 0.147461408936991, 5.34473730761318, 
-3.03635239762411, 10.1985884870051, -4.18300338745873, 4.31697391975358, 
0.0105619669306023, -0.958297985263961, -2.43186866616734, -8.38751268861282, 
1.64011057333683, -6.36025104814794, 0.226415618518729, -4.80411146461488, 
-1.1279132935989, 6.33496327649151, 6.37362139976954, 19.5424938405001, 
-4.17279750324084, -0.467488727668119, -0.254362741320246)), .Names = c("act", 
"pred", "resid"), row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 39L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 
52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L
), class = "data.frame")
3
rggplot2facet
Just curious - why not plot actual and residual in the same graph? – Ricardo Saporta
I would create the plots separately and then use grid.arrange. – joran
@RicardoSaporta Is there a google image you could link to as an example? Are you suggesting, using the post-melted data, that I would just do ggplot(plot, aes(x = pred, y = value)) + geom_point() with no facetting? Wouldn't that really shrink the scale of the residuals to make it hard to detect non-randomness/skew? – Hendy
My other comment is that facetting is less code... I just had to melt, then plot and facet by the variable value created by melt(). Then again, I suppose I could store these in a list created by lapply to plot various combinations. Thanks for the input. If you want to create a grid solution, I can accept the answer, though if that's the route we take, this might as well be a duplicate of the other grid-based solutions. – Hendy
@joran and i find I'm routinely advising people not to use grid.arrange which almost invariably messes up the layout. I wish gtable's longstanding bugs were addressed. – baptiste

3 Answers

129
votes

Here's some code with a dummy geom_blank layer,

range_act <- range(range(results$act), range(results$pred))

d <- reshape2::melt(results, id.vars = "pred")

dummy <- data.frame(pred = range_act, value = range_act,
                    variable = "act", stringsAsFactors=FALSE)

ggplot(d, aes(x = pred, y = value)) +
  facet_wrap(~variable, scales = "free") +
  geom_point(size = 2.5) + 
  geom_blank(data=dummy) + 
  theme_bw()

enter image description here

33
votes

I am not sure I understand what you want, but based on what I understood

the x scale seems to be the same, it is the y scale that is not the same, and that is because you specified scales ="free"

you can specify scales = "free_x" to only allow x to be free (in this case it is the same as pred has the same range by definition)

p <- ggplot(plot, aes(x = pred, y = value)) + geom_point(size = 2.5) + theme_bw()
p <- p + facet_wrap(~variable, scales = "free_x")

worked for me, see the picture

enter image description here

I think you were making it too difficult - I do seem to remember one time defining the limits based on a formula with min and max and if faceted I think it used only those values, but I can't find the code

9
votes

You can also specify the range with the coord_cartesian command to set the y-axis range that you want, an like in the previous post use scales = free_x 

p <- ggplot(plot, aes(x = pred, y = value)) +
     geom_point(size = 2.5) +
     theme_bw()+
     coord_cartesian(ylim = c(-20, 80))
p <- p + facet_wrap(~variable, scales = "free_x")
p

enter image description here