Extract data from a ggplot
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 with the factors using a nested ddply function and facet wrap
I now need to make a data frame that contains the summarized data used to generate the ggplot above.
Sector2 Family Year Length
BUN Acroporidae 2010 332.1300496
BUN Poritidae 2011 141.1467966
BUN Acroporidae 2012 127.479
BUN Acroporidae 2013 142.5940556
MUR Faviidae 2010 304.0405
MUR Faviidae 2011 423.152
MUR Pocilloporidae 2012 576.0295
MUR Poritidae 2013 123.8936667
NTH Faviidae 2010 60.494
NTH Faviidae 2011 27.427
NTH Pocilloporidae 2012 270.475
NTH Poritidae 2013 363.4635
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
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]]
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.