Extract data from a ggplot
While the other answers get you close, if you are looking for the actual data that was passed to ggplot(), you can use:
ggplot_build(p)$plot$data
require(tidyverse)
p <- ggplot(mtcars,aes(mpg))+geom_histogram()+
facet_wrap(~cyl)+geom_vline(data=data.frame(x=c(20,30)),aes(xintercept=x))
pg <- ggplot_build(p)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pg$plot$data
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-04 by the reprex package (v0.2.1)
While that isn't useful for an un-modified data frame, if you are piping through a series of mutate()'s or summarize()'s before you get to the ggplot, this can be useful after the fact to show the data.
layer_data is designed precisely for this :
layer_data(p, 1)
It will give you the data of the first layer, same as ggplot_build(p)$data[[1]].
Its source code is indeed precisely:
function (plot, i = 1L) ggplot_build(plot)$data[[i]]
To get values actually plotted you can use function ggplot_build() where argument is your plot.
p <- ggplot(mtcars,aes(mpg))+geom_histogram()+
facet_wrap(~cyl)+geom_vline(data=data.frame(x=c(20,30)),aes(xintercept=x))
pg <- ggplot_build(p)
This will make list and one of sublists is named data. This sublist contains dataframe with values used in plot, for example, for histrogramm it contains y values (the same as count). If you use facets then column PANEL shows in which facet values are used. If there are more than one geom_ in your plot then data will contains dataframes for each - in my example there is one dataframe for histogramm and another for vlines.
head(pg$data[[1]])
y count x ndensity ncount density PANEL group ymin ymax
1 0 0 9.791667 0 0 0 1 1 0 0
2 0 0 10.575000 0 0 0 1 1 0 0
3 0 0 11.358333 0 0 0 1 1 0 0
4 0 0 12.141667 0 0 0 1 1 0 0
5 0 0 12.925000 0 0 0 1 1 0 0
6 0 0 13.708333 0 0 0 1 1 0 0
xmin xmax
1 9.40000 10.18333
2 10.18333 10.96667
3 10.96667 11.75000
4 11.75000 12.53333
5 12.53333 13.31667
6 13.31667 14.10000
head(pg$data[[2]])
xintercept PANEL group xend x
1 20 1 1 20 20
2 30 1 1 30 30
3 20 2 2 20 20
4 30 2 2 30 30
5 20 3 3 20 20
6 30 3 3 30 30