I was playing around with extracting data from ggplot graphs, which I have done previously using ggplot2_build(). This time it was getting some of the modelling results that are plotted using geom_smooth(). Take the quick example below.

library(ggplot2)
p = ggplot(cars, aes(x = speed, y = dist)) + 
  geom_point() + 
  geom_smooth(method = "loess")
p

The plot has two layers and so ggplot_build(p)$data will return a list of two data frames. If we are interested in the results from geom_smooth() we need the second one as geom_smooth() is the second layer.

head(ggplot_build(p)$data[[2]], n = 10)

          x         y        ymin     ymax       se flipped_aes PANEL group
1  4.000000  5.893628 -14.0214308 25.80869 9.885466       FALSE     1    -1
2  4.265823  6.369796 -12.1702114 24.90980 9.202917       FALSE     1    -1
3  4.531646  6.867702 -10.3935937 24.12900 8.568188       FALSE     1    -1
4  4.797468  7.387181  -8.6930267 23.46739 7.981917       FALSE     1    -1
5  5.063291  7.928070  -7.0698299 22.92597 7.444680       FALSE     1    -1
6  5.329114  8.490205  -5.5250240 22.50543 6.956900       FALSE     1    -1
7  5.594937  9.073423  -4.0591073 22.20595 6.518745       FALSE     1    -1
8  5.860759  9.677561  -2.6717841 22.02691 6.129986       FALSE     1    -1
9  6.126582 10.302454  -1.3616644 21.96657 5.789853       FALSE     1    -1
10 6.392405 10.947940  -0.1259761 22.02186 5.496887       FALSE     1    -1
    colour   fill linewidth linetype weight alpha
1  #3366FF grey60         1        1      1   0.4
2  #3366FF grey60         1        1      1   0.4
3  #3366FF grey60         1        1      1   0.4
4  #3366FF grey60         1        1      1   0.4
5  #3366FF grey60         1        1      1   0.4
6  #3366FF grey60         1        1      1   0.4
7  #3366FF grey60         1        1      1   0.4
8  #3366FF grey60         1        1      1   0.4
9  #3366FF grey60         1        1      1   0.4
10 #3366FF grey60         1        1      1   0.4

The y values are the results from the loess model. So far, so good but I wanted to check if these values were the same as the results from direct calculation. Using base R functions give us the following by default.

loess_results = predict(loess(dist~speed,cars), cars$speed)
head(loess_results)

[1]  5.893628  5.893628 12.499786 12.499786 15.281082 18.446568

These results don’t match but a quick look at the documentation shows that geom_smooth() defaults to using n = 80 points and these are in equal steps from the minimum to the maximum values of speed. Providing the same sequence of speed values

# recalculate the values
loess_results = predict(loess(dist~speed,cars), 
                        seq(min(cars$speed), max(cars$speed), length.out = 80))

produces the same results as geom_smooth(), so we don’t need to do both. We can double check just to be sure.

# are they the same as those from ggplot?
identical(loess_results, ggplot_build(p)$data[[2]]$y)

[1] TRUE