Extract data from a ggplot

Question

I have made a plot using ggplot2 geom_histogram from a data frame. See sample below and link to the ggplot histogram Need to label each geom_vline

Answer 1

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

Answer 2

If you need just data it seems 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]]

Answer 3

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.