Combining 2 plots of different data (boxplot and points)

Question

I'd like to plot two graphs ontop of each other like in this post.

Experimental data: I have continuous variable displaying the angle of wind on a given day in a list called expt$iso_xs[,8], I then have the wind speed corresponding to that angle in expt$iso_xs[,2].

df<-data.frame(expt$iso.xs)

head(expt$iso.xs)
         [,1]     [,2]     [,3]      [,4]      [,5] [,6] [,7]   [,8]
 736105.4 16.62729 2.183740  7.234774 0.9791632 4.01 4.20 238.62
 736105.4 18.96705 2.489668  7.036234 0.9640366 3.82 4.00 243.14
 736105.5 20.52089 2.687636 10.355394 1.3698454 4.99 5.14 247.02
 736105.5 19.94449 2.611556 10.306912 1.3655301 4.85 5.12 249.57
 736105.5 19.43309 2.551787 11.098302 1.4646251 4.83 5.12 243.89
 736105.5 20.48259 2.689075 11.928011 1.5710530 4.89 5.09 254.23

Which looks like this:

Simulation data: I have a data.frame z that contains predictions for a subset of the above angles (0-90º).

head(z,15)
   Tracer angle treatment bigangle
  71.101     0         S      150
  71.101     0         S      150
  71.105     15         S      165
  71.105     15         S      165
  71.098     30         S      180
  71.098     45         S      195
  71.114     60         S      210
  71.114     80         S      230
  71.110     90         S      240

Plotting it using bigangle as factor and Tracer as :

ggplot() +
  geom_boxplot(data=z, aes(y = (3600/Tracer/93.241), x = factor(bigangle)),outlier.shape = NA,outlier.colour = NA)+
  coord_cartesian(ylim=c(0, 1))+
  labs(x = "Angle", y = "Normalised ACh" )+
  scale_x_discrete(labels=seq(0,360,10))+
  theme_classic()

looks like this:

I'd like to superimpose the boxplot ontop of the portion of red points (between 150º and 240º) but the following doesn't work:

ggplot() +
      geom_boxplot(data=z, aes(y = (3600/Tracer/93.241), x = factor(bigangle)),outlier.shape = NA,outlier.colour = NA)+
      geom_point(data=df, aes(y = X2/45, x = X8),color="red")+
      coord_cartesian(ylim=c(0, 1))+
      labs(x = "Angle", y = "Normalised ACh" )+
      scale_x_discrete(labels=seq(0,360,10))+
      theme_classic()

Any thoughts would be much appreciated, Cheers

Answer 1

I think your only problem is trying to specify a discrete x scale for continuous data. That and you need a group for your boxplot geom.

As an illustrative example:

mt = mtcars
mt$wt_bin = cut(mt$wt, breaks = c(1, 3, 4.5, 6))
ggplot(mt, aes(x = wt, y = mpg)) +
    geom_point() +
    geom_boxplot(aes(group = wt_bin, x = wt), alpha = 0.4)

As the geom_boxplot help says:

You can also use boxplots with continuous x, as long as you supply a grouping variable. cut_width is particularly useful

The example in the help shows this code:

ggplot(diamonds, aes(carat, price)) +
  geom_boxplot(aes(group = cut_width(carat, 0.25)))

You can, of course, add a geom_point layer (though in the diamonds data there are too many points for that to be a nice plot).

For your scale, don't use a discrete scale unless you have factors on the axis. You probably want scale_x_continuous(breaks = seq(0, 360, 10)).

Different data sets can be used in the usual way, with the data argument. Continuing the previous example but using different data for the geom_point layer:

similar_to_mt = data.frame(wt = runif(100, 1, 6), mpg = rnorm(100, 20, 4))
ggplot(mt, aes(x = wt, y = mpg)) +
    geom_point(data = similar_to_mt) +
    geom_boxplot(data = mt, aes(group = wt_bin, x = wt), alpha = 0.4)