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layer_before_stat(), layer_after_stat(), layer_before_geom(), and layer_after_scale() are helper functions that return a snapshot of a layer's data in the internals.

Usage

layer_before_stat(plot, i = 1L, ..., error = FALSE, verbose = TRUE)

layer_after_stat(plot, i = 1L, ..., error = FALSE, verbose = TRUE)

layer_before_geom(plot, i = 1L, ..., error = FALSE, verbose = TRUE)

layer_after_scale(plot, i = 1L, ..., error = FALSE, verbose = TRUE)

Arguments

plot

A ggplot object. If missing, defaults to ggplot2::last_plot().

i

Index of the layer to inspect. Defaults to 1L.

...

Unused.

error

If TRUE, returns the layer data early if available before the point of error.

verbose

If TRUE, prints the corresponding ggtrace code and re-prints the error if it exists.

Value

A dataframe

Examples

library(ggplot2)
p1 <- ggplot(mpg, aes(displ, class)) +
  geom_boxplot(outlier.shape = NA) +
  geom_text(
    aes(
      label = after_stat(xmax),
      x = stage(displ, after_stat = xmax)
    ),
    stat = "boxplot", hjust = -0.5
  )
p1


# Before Stat snapshot of first layer's data
layer_before_stat()
#>  Executed `ggtrace_inspect_args(last_plot(), ggplot2:::Layer$compute_statistic)$data`
#> # A tibble: 234 × 4
#>        x y          PANEL group
#>    <dbl> <mppd_dsc> <fct> <int>
#>  1   1.8 2          1         2
#>  2   1.8 2          1         2
#>  3   2   2          1         2
#>  4   2   2          1         2
#>  5   2.8 2          1         2
#>  6   2.8 2          1         2
#>  7   3.1 2          1         2
#>  8   1.8 2          1         2
#>  9   1.8 2          1         2
#> 10   2   2          1         2
#> # ℹ 224 more rows

# After Stat snapshot of first layer's data
layer_after_stat()
#>  Executed `ggtrace_inspect_return(last_plot(), ggplot2:::Layer$compute_statistic)`
#> # A tibble: 7 × 14
#>    xmin xlower xmiddle xupper  xmax outliers  notchupper notchlower     y width
#>   <dbl>  <dbl>   <dbl>  <dbl> <dbl> <list>         <dbl>      <dbl> <dbl> <dbl>
#> 1   5.7    5.7    6.2    6.2    6.2 <dbl [1]>       6.55       5.85     1  0.75
#> 2   1.8    2      2.2    2.8    3.3 <dbl [0]>       2.38       2.02     2  0.75
#> 3   1.8    2.4    2.8    3.5    4.2 <dbl [1]>       3.07       2.53     3  0.75
#> 4   3      3.3    3.3    3.8    4   <dbl [1]>       3.54       3.06     4  0.75
#> 5   2.7    3.9    4.7    4.7    5.9 <dbl [0]>       4.92       4.48     5  0.75
#> 6   1.6    1.9    2.2    3.25   4.6 <dbl [1]>       2.56       1.84     6  0.75
#> 7   2.5    4      4.65   5.3    6.5 <dbl [0]>       4.91       4.39     7  0.75
#> # ℹ 4 more variables: relvarwidth <dbl>, flipped_aes <lgl>, PANEL <fct>,
#> #   group <int>

# First and second layer's data are identical for those two stages
identical(layer_before_stat(), layer_before_stat(i = 2))
#>  Executed `ggtrace_inspect_args(last_plot(), ggplot2:::Layer$compute_statistic)$data`
#>  Executed `ggtrace_inspect_args(last_plot(), ggplot2:::Layer$compute_statistic, cond = 2L)$data`
#> [1] TRUE
identical(layer_after_stat(), layer_after_stat(i = 2))
#>  Executed `ggtrace_inspect_return(last_plot(), ggplot2:::Layer$compute_statistic)`
#>  Executed `ggtrace_inspect_return(last_plot(), ggplot2:::Layer$compute_statistic, cond = 2L)`
#> [1] TRUE

# `after_stat()` mappings add new columns to the second layer's data
# by the time the geom receives the data in the Before Geom stage
library(dplyr)
layer_before_geom(i = 2)
#>  Executed `ggtrace_inspect_args(last_plot(), ggplot2:::Layer$compute_geom_1, cond = 2L)$data`
#> # A tibble: 7 × 16
#>       x label  xmin xlower xmiddle xupper  xmax outliers  notchupper notchlower
#>   <dbl> <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl> <list>         <dbl>      <dbl>
#> 1   6.2   6.2   5.7    5.7    6.2    6.2    6.2 <dbl [1]>       6.55       5.85
#> 2   3.3   3.3   1.8    2      2.2    2.8    3.3 <dbl [0]>       2.38       2.02
#> 3   4.2   4.2   1.8    2.4    2.8    3.5    4.2 <dbl [1]>       3.07       2.53
#> 4   4     4     3      3.3    3.3    3.8    4   <dbl [1]>       3.54       3.06
#> 5   5.9   5.9   2.7    3.9    4.7    4.7    5.9 <dbl [0]>       4.92       4.48
#> 6   4.6   4.6   1.6    1.9    2.2    3.25   4.6 <dbl [1]>       2.56       1.84
#> 7   6.5   6.5   2.5    4      4.65   5.3    6.5 <dbl [0]>       4.91       4.39
#> # ℹ 6 more variables: y <dbl>, width <dbl>, relvarwidth <dbl>,
#> #   flipped_aes <lgl>, PANEL <fct>, group <int>

# After Scale data reflects `after_scale()` mappings
p2 <- ggplot(mpg, aes(as.factor(cyl), hwy, color = as.factor(cyl))) +
  theme(legend.position = 0)
p2a <- p2 +
  geom_boxplot(aes(fill = as.factor(cyl)))
p2b <- p2 +
  geom_boxplot(aes(fill = after_scale(alpha(color, .6))))

library(patchwork)
p2a + p2b


layer_after_scale(p2a, verbose = FALSE)$fill
#> [1] "#F8766D" "#7CAE00" "#00BFC4" "#C77CFF"
layer_after_scale(p2b, verbose = FALSE)$fill
#> [1] "#F8766D99" "#7CAE0099" "#00BFC499" "#C77CFF99"
alpha( layer_after_scale(p2a, verbose = FALSE)$fill, .6 )
#> [1] "#F8766D99" "#7CAE0099" "#00BFC499" "#C77CFF99"