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README.md
In gginnards: Explore the Innards of 'ggplot2' Objects
gginnards 
Purpose
Package ‘gginnards’ (Explore the innards of ‘ggplot2’) is a set of
extensions to R package ‘ggplot2’. Two geometries and two statistics are
specially useful when learning how to write ‘ggplot2’ extensions and
when debugging newly defined statistics and geometries.
Objects of class "gg", or unrendered ggplot objects, are a list-like
structure containing data and “instructions” to construct a plot and its
layers. By editing this structure it is possible to modify the plot that
will be rendered. Functions are provided for the manipulation of layers
within ggobjects. These can be useful in the case extensions to
‘ggplot2’ that define functions that construct and return whole gg
objects. In such cases in can be useful to programatically edit these
gg objects to insert plot layers or change their order.
As the variables returned in data by many statistics depend on input,
some of these tools can be also useful in everyday plotting with
‘gplot2’ as a way of diagnosing problems or getting information that is
missing from their documentation or only available at runtime.
Stability
Because of its nature, this package does not always play safe and nice
even if it tries to. No exported or internal function or object should
be imported or used in other packages, simply because they may change at
any time (as in version 0.2.0). Although I intend to maintain the
package, I will not attempt to actively keep it compatible with anything
but the current version of ‘ggplot2’, although it will frequently also
work with some earlier versions. Please, see the blog post Playing on
the same team as your
dependecy
by Thomas Lin Pedersen.
Alternatives
The debug statistics and geoms are defined in ‘gginnards’ using the same
approach as other ‘ggplot2’ extensions, without any significant
assumptions about the internals of ‘ggplot2’. Thus, they are unlikely to
be affected by minor updates to ‘ggplot2’. The layer manipulation
functions rely on the internal structure of "gg" objects, but mostly
on aspects unlikely to change frequently.
Package ‘gginnards’ does not provide a deep view into the structure of
"gg" objects or the steps involved in building these objects and
rendering them into graphical objects. Package
‘ggtrace’ by June Choe provides
tools for a much deeper and detailed exploration and edition of ggplots,
but it is much more dependent on the undocumented internals of ‘ggplot2’
than ‘gginnards’. (Note: there exists another package named
ggtrace for a different purpose.
None of them are yet available through CRAN.)
History
This package was born when several functions were extracted from package
‘ggpmisc’ and packaged on their own.
Geometries
Geometries geom_debug_panel() and geom_debug_droup() by default
print the data object received as input by the draw functi to the
console adding a plot layer that produces no graphic output. As they
take as arguments summary functions, they allows flexibility in how
data objects are displayed. geom_debug_panel() and
geom_debug_droup() are useful at any time when one needs to check what
variables are returned by statistics. Many statistics are well
documented and always return the same variables. For other statistics
even if well documented the returned variables in data vary depending
on grouping and/or the arguments passed to them, in which case this
geometry can also be useful when debugging scripts.
Statistics
Statistics stat_debug_panel() and stat_debug_droup() echo their data
input to the R console aim at easing debugging during development of new
geoms and statistics. They will also help those learning how ggplot
grouping, panels and layers work. As the debug geometries, they take
as arguments summary functions allowing flexibility in how data
objects are displayed.
For debugging, the value returned by these stats is usually not
interesting, and by default, it is a copy of the data object received
as input. This can be changed by passing a function as argument to
fun.data. This could be useful to produce data summaries to be plotted
as a layer. This may even work as a tool useful to build ad-hoc
statistics with rudimentary built-in debugging.
Manipulation of layers
A set of functions easy the manipulation of layers in ggplot objects,
allowing deletion of any existing layer, insertion of new layers at any
position, and reordering of the existing layers.
Manipulation of embedded data
A function to drop unused variables from the data object embedded in
gg and ggplot objects serves as an additional example of a
manipulation that may be useful when dealing with very large datasets.
Companion functions are defined to explore the embedded data object.
Examples
library(gginnards)
#> Loading required package: ggplot2
We print to the R console data as seen as input by geometries and
statistics.
ggplot(mtcars, aes(cyl, mpg, color = mpg)) +
geom_point() +
geom_debug_panel()
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'data' (head):"
#> colour x y PANEL group
#> 1 #30648F 6 21.0 1 -1
#> 2 #30648F 6 21.0 1 -1
#> 3 #356E9D 4 22.8 1 -1
#> 4 #316692 6 21.4 1 -1
#> 5 #29577E 8 18.7 1 -1
#> 6 #275379 6 18.1 1 -1
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'params' (summary):"
#> Length Class Mode
#> x 11 ViewScale environment
#> x.sec 11 ViewScale environment
#> x.range 2 -none- numeric
#> y 11 ViewScale environment
#> y.sec 11 ViewScale environment
#> y.range 2 -none- numeric
#> guides 4 Guides environment
We print to the R console colnames(data).
ggplot(mtcars, aes(cyl, mpg, color = mpg)) +
geom_point() +
geom_debug_panel(dbgfun.data = colnames, dbgfun.params = NULL)
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'data' (anonymous function):"
#> [1] "colour" "x" "y" "PANEL" "group"
We print to the R console data as returned by stat_summary() and
seen as input by geometries.
ggplot(mtcars, aes(cyl, mpg, colour = factor(cyl))) +
stat_summary(fun.data = "mean_cl_boot") +
stat_summary(fun.data = "mean_cl_boot", geom = "debug_panel")
#> [1] "PANEL 1; group(s) 1, 2, 3; 'draw_function()' input 'data' (head):"
#> colour x group y ymin ymax PANEL flipped_aes orientation
#> 1 #F8766D 4 1 26.66364 24.26341 29.21841 1 FALSE NA
#> 2 #00BA38 6 2 19.74286 18.78571 20.65714 1 FALSE NA
#> 3 #619CFF 8 3 15.10000 13.74268 16.37875 1 FALSE NA
We print to the R console data as seen as input by statistics that
use a panel function.
ggplot(mtcars, aes(cyl, mpg, colour = factor(cyl))) +
stat_debug_panel()
#> [1] "PANEL 1; group(s) 1, 2, 3; 'compute_panel()' input 'data' (head):"
#> x y colour PANEL group
#> 1 6 21.0 6 1 2
#> 2 6 21.0 6 1 2
#> 3 4 22.8 4 1 1
#> 4 6 21.4 6 1 2
#> 5 8 18.7 8 1 3
#> 6 6 18.1 6 1 2
We build object p of class gg (a ggplot). We query the number of
layers and the position of layers by the class of the ggproto object.
p <-
ggplot(mtcars, aes(cyl, mpg)) +
geom_point(size = 3) +
stat_summary(fun.data = "mean_cl_boot", color = "red", size = 2)
num_layers(p)
#> [1] 2
which_layers(p, "GeomPoint")
#> [1] 1
which_layers(p, "StatSummary")
#> [1] 2
p
move_layers(p, "GeomPoint", position = "top")
Installation
Installation of the most recent stable version from CRAN (sources, Mac
and Win binaries):
install.packages("gginnards")
Installation of the current unstable version from R-Universe CRAN-like
repository (binaries for Mac, Win, Webassembly, and Linux, as well as
sources available):
install.packages('gginnards',
repos = c('https://aphalo.r-universe.dev',
'https://cloud.r-project.org'))
Installation of the current unstable version from GitHub (from sources):
# install.packages("devtools")
devtools::install_github("aphalo/gginnards")
Documentation
HTML documentation is available at
(https://docs.r4photobiology.info/gginnards/), including two
vignettes.
News about updates are regularly posted at
(https://www.r4photobiology.info/).
Contributing
Please report bugs and request new features at
(https://github.com/aphalo/gginnards/issues). Pull requests are
welcome at (https://github.com/aphalo/gginnards).
Citation
If you use this package to produce scientific or commercial
publications, please cite according to:
citation("gginnards")
#> To cite package 'gginnards' in publications use:
#>
#> Aphalo P (2024). _gginnards: Explore the Innards of 'ggplot2'
#> Objects_. R package version 0.2.0,
#> https://github.com/aphalo/gginnards,
#> <https://www.r4photobiology.info>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {gginnards: Explore the Innards of 'ggplot2' Objects},
#> author = {Pedro J. Aphalo},
#> year = {2024},
#> note = {R package version 0.2.0, https://github.com/aphalo/gginnards},
#> url = {https://www.r4photobiology.info},
#> }
License
© 2016-2024 Pedro J. Aphalo (pedro.aphalo@helsinki.fi). Released under
the GPL, version 2 or greater. This software carries no warranty of any
kind.
Try the gginnards package in your browser
Any scripts or data that you put into this service are public.
gginnards documentation built on May 29, 2024, 2:08 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
gginnards
Purpose
Package ‘gginnards’ (Explore the innards of ‘ggplot2’) is a set of extensions to R package ‘ggplot2’. Two geometries and two statistics are specially useful when learning how to write ‘ggplot2’ extensions and when debugging newly defined statistics and geometries.
Objects of class "gg", or unrendered ggplot objects, are a list-like
structure containing data and “instructions” to construct a plot and its
layers. By editing this structure it is possible to modify the plot that
will be rendered. Functions are provided for the manipulation of layers
within ggobjects. These can be useful in the case extensions to
‘ggplot2’ that define functions that construct and return whole gg
objects. In such cases in can be useful to programatically edit these
gg objects to insert plot layers or change their order.
As the variables returned in data by many statistics depend on input,
some of these tools can be also useful in everyday plotting with
‘gplot2’ as a way of diagnosing problems or getting information that is
missing from their documentation or only available at runtime.
Stability
Because of its nature, this package does not always play safe and nice even if it tries to. No exported or internal function or object should be imported or used in other packages, simply because they may change at any time (as in version 0.2.0). Although I intend to maintain the package, I will not attempt to actively keep it compatible with anything but the current version of ‘ggplot2’, although it will frequently also work with some earlier versions. Please, see the blog post Playing on the same team as your dependecy by Thomas Lin Pedersen.
Alternatives
The debug statistics and geoms are defined in ‘gginnards’ using the same
approach as other ‘ggplot2’ extensions, without any significant
assumptions about the internals of ‘ggplot2’. Thus, they are unlikely to
be affected by minor updates to ‘ggplot2’. The layer manipulation
functions rely on the internal structure of "gg" objects, but mostly
on aspects unlikely to change frequently.
Package ‘gginnards’ does not provide a deep view into the structure of
"gg" objects or the steps involved in building these objects and
rendering them into graphical objects. Package
‘ggtrace’ by June Choe provides
tools for a much deeper and detailed exploration and edition of ggplots,
but it is much more dependent on the undocumented internals of ‘ggplot2’
than ‘gginnards’. (Note: there exists another package named
ggtrace for a different purpose.
None of them are yet available through CRAN.)
History
This package was born when several functions were extracted from package ‘ggpmisc’ and packaged on their own.
Geometries
Geometries geom_debug_panel() and geom_debug_droup() by default
print the data object received as input by the draw functi to the
console adding a plot layer that produces no graphic output. As they
take as arguments summary functions, they allows flexibility in how
data objects are displayed. geom_debug_panel() and
geom_debug_droup() are useful at any time when one needs to check what
variables are returned by statistics. Many statistics are well
documented and always return the same variables. For other statistics
even if well documented the returned variables in data vary depending
on grouping and/or the arguments passed to them, in which case this
geometry can also be useful when debugging scripts.
Statistics
Statistics stat_debug_panel() and stat_debug_droup() echo their data
input to the R console aim at easing debugging during development of new
geoms and statistics. They will also help those learning how ggplot
grouping, panels and layers work. As the debug geometries, they take
as arguments summary functions allowing flexibility in how data
objects are displayed.
For debugging, the value returned by these stats is usually not
interesting, and by default, it is a copy of the data object received
as input. This can be changed by passing a function as argument to
fun.data. This could be useful to produce data summaries to be plotted
as a layer. This may even work as a tool useful to build ad-hoc
statistics with rudimentary built-in debugging.
Manipulation of layers
A set of functions easy the manipulation of layers in ggplot objects, allowing deletion of any existing layer, insertion of new layers at any position, and reordering of the existing layers.
Manipulation of embedded data
A function to drop unused variables from the data object embedded in
gg and ggplot objects serves as an additional example of a
manipulation that may be useful when dealing with very large datasets.
Companion functions are defined to explore the embedded data object.
Examples
library(gginnards)
#> Loading required package: ggplot2
We print to the R console data as seen as input by geometries and
statistics.
ggplot(mtcars, aes(cyl, mpg, color = mpg)) +
geom_point() +
geom_debug_panel()
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'data' (head):"
#> colour x y PANEL group
#> 1 #30648F 6 21.0 1 -1
#> 2 #30648F 6 21.0 1 -1
#> 3 #356E9D 4 22.8 1 -1
#> 4 #316692 6 21.4 1 -1
#> 5 #29577E 8 18.7 1 -1
#> 6 #275379 6 18.1 1 -1
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'params' (summary):"
#> Length Class Mode
#> x 11 ViewScale environment
#> x.sec 11 ViewScale environment
#> x.range 2 -none- numeric
#> y 11 ViewScale environment
#> y.sec 11 ViewScale environment
#> y.range 2 -none- numeric
#> guides 4 Guides environment
We print to the R console colnames(data).
ggplot(mtcars, aes(cyl, mpg, color = mpg)) +
geom_point() +
geom_debug_panel(dbgfun.data = colnames, dbgfun.params = NULL)
#> [1] "PANEL 1; group(s) -1; 'draw_panel()' input 'data' (anonymous function):"
#> [1] "colour" "x" "y" "PANEL" "group"
We print to the R console data as returned by stat_summary() and
seen as input by geometries.
ggplot(mtcars, aes(cyl, mpg, colour = factor(cyl))) +
stat_summary(fun.data = "mean_cl_boot") +
stat_summary(fun.data = "mean_cl_boot", geom = "debug_panel")
#> [1] "PANEL 1; group(s) 1, 2, 3; 'draw_function()' input 'data' (head):"
#> colour x group y ymin ymax PANEL flipped_aes orientation
#> 1 #F8766D 4 1 26.66364 24.26341 29.21841 1 FALSE NA
#> 2 #00BA38 6 2 19.74286 18.78571 20.65714 1 FALSE NA
#> 3 #619CFF 8 3 15.10000 13.74268 16.37875 1 FALSE NA
We print to the R console data as seen as input by statistics that
use a panel function.
ggplot(mtcars, aes(cyl, mpg, colour = factor(cyl))) +
stat_debug_panel()
#> [1] "PANEL 1; group(s) 1, 2, 3; 'compute_panel()' input 'data' (head):"
#> x y colour PANEL group
#> 1 6 21.0 6 1 2
#> 2 6 21.0 6 1 2
#> 3 4 22.8 4 1 1
#> 4 6 21.4 6 1 2
#> 5 8 18.7 8 1 3
#> 6 6 18.1 6 1 2
We build object p of class gg (a ggplot). We query the number of
layers and the position of layers by the class of the ggproto object.
p <-
ggplot(mtcars, aes(cyl, mpg)) +
geom_point(size = 3) +
stat_summary(fun.data = "mean_cl_boot", color = "red", size = 2)
num_layers(p)
#> [1] 2
which_layers(p, "GeomPoint")
#> [1] 1
which_layers(p, "StatSummary")
#> [1] 2
p
move_layers(p, "GeomPoint", position = "top")
Installation
Installation of the most recent stable version from CRAN (sources, Mac and Win binaries):
install.packages("gginnards")
Installation of the current unstable version from R-Universe CRAN-like repository (binaries for Mac, Win, Webassembly, and Linux, as well as sources available):
install.packages('gginnards',
repos = c('https://aphalo.r-universe.dev',
'https://cloud.r-project.org'))
Installation of the current unstable version from GitHub (from sources):
# install.packages("devtools")
devtools::install_github("aphalo/gginnards")
Documentation
HTML documentation is available at (https://docs.r4photobiology.info/gginnards/), including two vignettes.
News about updates are regularly posted at (https://www.r4photobiology.info/).
Contributing
Please report bugs and request new features at (https://github.com/aphalo/gginnards/issues). Pull requests are welcome at (https://github.com/aphalo/gginnards).
Citation
If you use this package to produce scientific or commercial publications, please cite according to:
citation("gginnards")
#> To cite package 'gginnards' in publications use:
#>
#> Aphalo P (2024). _gginnards: Explore the Innards of 'ggplot2'
#> Objects_. R package version 0.2.0,
#> https://github.com/aphalo/gginnards,
#> <https://www.r4photobiology.info>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {gginnards: Explore the Innards of 'ggplot2' Objects},
#> author = {Pedro J. Aphalo},
#> year = {2024},
#> note = {R package version 0.2.0, https://github.com/aphalo/gginnards},
#> url = {https://www.r4photobiology.info},
#> }
License
© 2016-2024 Pedro J. Aphalo (pedro.aphalo@helsinki.fi). Released under the GPL, version 2 or greater. This software carries no warranty of any kind.
Try the gginnards package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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