R/render-plot.R
In YTLogos/shiny: Web Application Framework for R
Defines functions
renderPlot
getPrevPlotCoordmap
getGgplotCoordmap
find_panel_info
find_panel_info_api
find_panel_info_non_api
find_panel_ranges
Documented in
renderPlot
#' Plot Output
#'
#' Renders a reactive plot that is suitable for assigning to an \code{output}
#' slot.
#'
#' The corresponding HTML output tag should be \code{div} or \code{img} and have
#' the CSS class name \code{shiny-plot-output}.
#'
#' @section Interactive plots:
#'
#' With ggplot2 graphics, the code in \code{renderPlot} should return a ggplot
#' object; if instead the code prints the ggplot2 object with something like
#' \code{print(p)}, then the coordinates for interactive graphics will not be
#' properly scaled to the data space.
#'
#' See \code{\link{plotOutput}} for more information about interactive plots.
#'
#' @seealso For the corresponding client-side output function, and example
#' usage, see \code{\link{plotOutput}}. For more details on how the plots are
#' generated, and how to control the output, see \code{\link{plotPNG}}.
#'
#' @param expr An expression that generates a plot.
#' @param width,height The width/height of the rendered plot, in pixels; or
#' \code{'auto'} to use the \code{offsetWidth}/\code{offsetHeight} of the HTML
#' element that is bound to this plot. You can also pass in a function that
#' returns the width/height in pixels or \code{'auto'}; in the body of the
#' function you may reference reactive values and functions. When rendering an
#' inline plot, you must provide numeric values (in pixels) to both
#' \code{width} and \code{height}.
#' @param res Resolution of resulting plot, in pixels per inch. This value is
#' passed to \code{\link[grDevices]{png}}. Note that this affects the resolution of PNG
#' rendering in R; it won't change the actual ppi of the browser.
#' @param ... Arguments to be passed through to \code{\link[grDevices]{png}}.
#' These can be used to set the width, height, background color, etc.
#' @param env The environment in which to evaluate \code{expr}.
#' @param quoted Is \code{expr} a quoted expression (with \code{quote()})? This
#' is useful if you want to save an expression in a variable.
#' @param execOnResize If \code{FALSE} (the default), then when a plot is
#' resized, Shiny will \emph{replay} the plot drawing commands with
#' \code{\link[grDevices]{replayPlot}()} instead of re-executing \code{expr}.
#' This can result in faster plot redrawing, but there may be rare cases where
#' it is undesirable. If you encounter problems when resizing a plot, you can
#' have Shiny re-execute the code on resize by setting this to \code{TRUE}.
#' @param outputArgs A list of arguments to be passed through to the implicit
#' call to \code{\link{plotOutput}} when \code{renderPlot} is used in an
#' interactive R Markdown document.
#' @export
renderPlot <- function(expr, width='auto', height='auto', res=72, ...,
env=parent.frame(), quoted=FALSE,
execOnResize=FALSE, outputArgs=list()
) {
# This ..stacktraceon is matched by a ..stacktraceoff.. when plotFunc
# is called
installExprFunction(expr, "func", env, quoted, ..stacktraceon = TRUE)
args <- list(...)
if (is.function(width))
widthWrapper <- reactive({ width() })
else
widthWrapper <- function() { width }
if (is.function(height))
heightWrapper <- reactive({ height() })
else
heightWrapper <- function() { height }
# A modified version of print.ggplot which returns the built ggplot object
# as well as the gtable grob. This overrides the ggplot::print.ggplot
# method, but only within the context of renderPlot. The reason this needs
# to be a (pseudo) S3 method is so that, if an object has a class in
# addition to ggplot, and there's a print method for that class, that we
# won't override that method. https://github.com/rstudio/shiny/issues/841
print.ggplot <- function(x) {
grid::grid.newpage()
build <- ggplot2::ggplot_build(x)
gtable <- ggplot2::ggplot_gtable(build)
grid::grid.draw(gtable)
structure(list(
build = build,
gtable = gtable
), class = "ggplot_build_gtable")
}
getDims <- function() {
width <- widthWrapper()
height <- heightWrapper()
# Note that these are reactive calls. A change to the width and height
# will inherently cause a reactive plot to redraw (unless width and
# height were explicitly specified).
if (width == 'auto')
width <- session$clientData[[paste0('output_', outputName, '_width')]]
if (height == 'auto')
height <- session$clientData[[paste0('output_', outputName, '_height')]]
list(width = width, height = height)
}
# Vars to store session and output, so that they can be accessed from
# the plotObj() reactive.
session <- NULL
outputName <- NULL
# This function is the one that's returned from renderPlot(), and gets
# wrapped in an observer when the output value is assigned. The expression
# passed to renderPlot() is actually run in plotObj(); this function can only
# replay a plot if the width/height changes.
renderFunc <- function(shinysession, name, ...) {
session <<- shinysession
outputName <<- name
dims <- getDims()
if (is.null(dims$width) || is.null(dims$height) ||
dims$width <= 0 || dims$height <= 0) {
return(NULL)
}
# The reactive that runs the expr in renderPlot()
plotData <- plotObj()
img <- plotData$img
# If only the width/height have changed, simply replay the plot and make a
# new img.
if (dims$width != img$width || dims$height != img$height) {
pixelratio <- session$clientData$pixelratio %OR% 1
coordmap <- NULL
plotFunc <- function() {
..stacktraceon..(grDevices::replayPlot(plotData$recordedPlot))
# Coordmap must be recalculated after replaying plot, because pixel
# dimensions will have changed.
if (inherits(plotData$plotResult, "ggplot_build_gtable")) {
coordmap <<- getGgplotCoordmap(plotData$plotResult, pixelratio, res)
} else {
coordmap <<- getPrevPlotCoordmap(dims$width, dims$height)
}
}
outfile <- ..stacktraceoff..(
plotPNG(plotFunc, width = dims$width*pixelratio, height = dims$height*pixelratio,
res = res*pixelratio)
)
on.exit(unlink(outfile))
img <- dropNulls(list(
src = session$fileUrl(name, outfile, contentType='image/png'),
width = dims$width,
height = dims$height,
coordmap = coordmap,
# Get coordmap error message if present
error = attr(coordmap, "error", exact = TRUE)
))
}
img
}
plotObj <- reactive(label = "plotObj", {
if (execOnResize) {
dims <- getDims()
} else {
isolate({ dims <- getDims() })
}
if (is.null(dims$width) || is.null(dims$height) ||
dims$width <= 0 || dims$height <= 0) {
return(NULL)
}
# Resolution multiplier
pixelratio <- session$clientData$pixelratio %OR% 1
plotResult <- NULL
recordedPlot <- NULL
coordmap <- NULL
plotFunc <- function() {
success <-FALSE
tryCatch(
{
# This is necessary to enable displaylist recording
grDevices::dev.control(displaylist = "enable")
# Actually perform the plotting
result <- withVisible(func())
success <- TRUE
},
finally = {
if (!success) {
# If there was an error in making the plot, there's a good chance
# it's "Error in plot.new: figure margins too large". We need to
# take a reactive dependency on the width and height, so that the
# user's plotting code will re-execute when the plot is resized,
# instead of just replaying the previous plot (which errored).
getDims()
}
}
)
if (result$visible) {
# Use capture.output to squelch printing to the actual console; we
# are only interested in plot output
utils::capture.output({
# This ..stacktraceon.. negates the ..stacktraceoff.. that wraps
# the call to plotFunc. The value needs to be printed just in case
# it's an object that requires printing to generate plot output,
# similar to ggplot2. But for base graphics, it would already have
# been rendered when func was called above, and the print should
# have no effect.
plotResult <<- ..stacktraceon..(print(result$value))
})
}
recordedPlot <<- grDevices::recordPlot()
if (inherits(plotResult, "ggplot_build_gtable")) {
coordmap <<- getGgplotCoordmap(plotResult, pixelratio, res)
} else {
coordmap <<- getPrevPlotCoordmap(dims$width, dims$height)
}
}
# This ..stacktraceoff.. is matched by the `func` function's
# wrapFunctionLabel(..stacktraceon=TRUE) call near the beginning of
# renderPlot, and by the ..stacktraceon.. in plotFunc where ggplot objects
# are printed
outfile <- ..stacktraceoff..(
do.call(plotPNG, c(plotFunc, width=dims$width*pixelratio,
height=dims$height*pixelratio, res=res*pixelratio, args))
)
on.exit(unlink(outfile))
list(
# img is the content that gets sent to the client.
img = dropNulls(list(
src = session$fileUrl(outputName, outfile, contentType='image/png'),
width = dims$width,
height = dims$height,
coordmap = coordmap,
# Get coordmap error message if present.
error = attr(coordmap, "error", exact = TRUE)
)),
# Returned value from expression in renderPlot() -- may be a printable
# object like ggplot2. Needed just in case we replayPlot and need to get
# a coordmap again.
plotResult = plotResult,
recordedPlot = recordedPlot
)
})
# If renderPlot isn't going to adapt to the height of the div, then the
# div needs to adapt to the height of renderPlot. By default, plotOutput
# sets the height to 400px, so to make it adapt we need to override it
# with NULL.
outputFunc <- plotOutput
if (!identical(height, 'auto')) formals(outputFunc)['height'] <- list(NULL)
markRenderFunction(outputFunc, renderFunc, outputArgs = outputArgs)
}
# The coordmap extraction functions below return something like the examples
# below. For base graphics:
# plot(mtcars$wt, mtcars$mpg)
# str(getPrevPlotCoordmap(400, 300))
# List of 1
# $ :List of 4
# ..$ domain :List of 4
# .. ..$ left : num 1.36
# .. ..$ right : num 5.58
# .. ..$ bottom: num 9.46
# .. ..$ top : num 34.8
# ..$ range :List of 4
# .. ..$ left : num 50.4
# .. ..$ right : num 373
# .. ..$ bottom: num 199
# .. ..$ top : num 79.6
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ mapping: Named list()
#
# For ggplot2, first you need to define the print.ggplot function from inside
# renderPlot, then use it to print the plot:
# print.ggplot <- function(x) {
# grid::grid.newpage()
#
# build <- ggplot2::ggplot_build(x)
#
# gtable <- ggplot2::ggplot_gtable(build)
# grid::grid.draw(gtable)
#
# structure(list(
# build = build,
# gtable = gtable
# ), class = "ggplot_build_gtable")
# }
#
# p <- print(ggplot(mtcars, aes(wt, mpg)) + geom_point())
# str(getGgplotCoordmap(p, 1, 72))
# List of 1
# $ :List of 10
# ..$ panel : int 1
# ..$ row : int 1
# ..$ col : int 1
# ..$ panel_vars: Named list()
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 2
# .. ..$ x: chr "wt"
# .. ..$ y: chr "mpg"
# ..$ range :List of 4
# .. ..$ left : num 40.8
# .. ..$ right : num 446
# .. ..$ bottom: num 263
# .. ..$ top : num 14.4
#
# With a faceted ggplot2 plot, the outer list contains two objects, each of
# which represents one panel. In this example, there is one panelvar, but there
# can be up to two of them.
# mtc <- mtcars
# mtc$am <- factor(mtc$am)
# p <- print(ggplot(mtc, aes(wt, mpg)) + geom_point() + facet_wrap(~ am))
# str(getGgplotCoordmap(p, 1, 72))
# List of 2
# $ :List of 10
# ..$ panel : int 1
# ..$ row : int 1
# ..$ col : int 1
# ..$ panel_vars:List of 1
# .. ..$ panelvar1: Factor w/ 2 levels "0","1": 1
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 3
# .. ..$ x : chr "wt"
# .. ..$ y : chr "mpg"
# .. ..$ panelvar1: chr "am"
# ..$ range :List of 4
# .. ..$ left : num 45.6
# .. ..$ right : num 317
# .. ..$ bottom: num 251
# .. ..$ top : num 35.7
# $ :List of 10
# ..$ panel : int 2
# ..$ row : int 1
# ..$ col : int 2
# ..$ panel_vars:List of 1
# .. ..$ panelvar1: Factor w/ 2 levels "0","1": 2
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 3
# .. ..$ x : chr "wt"
# .. ..$ y : chr "mpg"
# .. ..$ panelvar1: chr "am"
# ..$ range :List of 4
# .. ..$ left : num 322
# .. ..$ right : num 594
# .. ..$ bottom: num 251
# .. ..$ top : num 35.7
# Get a coordmap for the previous plot made with base graphics.
# Requires width and height of output image, in pixels.
# Must be called before the graphics device is closed.
getPrevPlotCoordmap <- function(width, height) {
usrCoords <- graphics::par('usr')
usrBounds <- usrCoords
if (graphics::par('xlog')) {
usrBounds[c(1,2)] <- 10 ^ usrBounds[c(1,2)]
}
if (graphics::par('ylog')) {
usrBounds[c(3,4)] <- 10 ^ usrBounds[c(3,4)]
}
# Wrapped in double list because other types of plots can have multiple panels.
list(list(
# Bounds of the plot area, in data space
domain = list(
left = usrCoords[1],
right = usrCoords[2],
bottom = usrCoords[3],
top = usrCoords[4]
),
# The bounds of the plot area, in DOM pixels
range = list(
left = graphics::grconvertX(usrBounds[1], 'user', 'nfc') * width,
right = graphics::grconvertX(usrBounds[2], 'user', 'nfc') * width,
bottom = (1-graphics::grconvertY(usrBounds[3], 'user', 'nfc')) * height - 1,
top = (1-graphics::grconvertY(usrBounds[4], 'user', 'nfc')) * height - 1
),
log = list(
x = if (graphics::par('xlog')) 10 else NULL,
y = if (graphics::par('ylog')) 10 else NULL
),
# We can't extract the original variable names from a base graphic.
# `mapping` is an empty _named_ list, so that it is converted to an object
# (not an array) in JSON.
mapping = list(x = NULL)[0]
))
}
# Given a ggplot_build_gtable object, return a coordmap for it.
getGgplotCoordmap <- function(p, pixelratio, res) {
if (!inherits(p, "ggplot_build_gtable"))
return(NULL)
tryCatch({
# Get info from built ggplot object
info <- find_panel_info(p$build)
# Get ranges from gtable - it's possible for this to return more elements than
# info, because it calculates positions even for panels that aren't present.
# This can happen with facet_wrap.
ranges <- find_panel_ranges(p$gtable, pixelratio, res)
for (i in seq_along(info)) {
info[[i]]$range <- ranges[[i]]
}
return(info)
}, error = function(e) {
# If there was an error extracting info from the ggplot object, just return
# a list with the error message.
return(structure(list(), error = e$message))
})
}
find_panel_info <- function(b) {
# Structure of ggplot objects changed after 2.1.0. After 2.2.1, there was a
# an API for extracting the necessary information.
ggplot_ver <- utils::packageVersion("ggplot2")
if (ggplot_ver > "2.2.1") {
find_panel_info_api(b)
} else if (ggplot_ver > "2.1.0") {
find_panel_info_non_api(b, ggplot_format = "new")
} else {
find_panel_info_non_api(b, ggplot_format = "old")
}
}
# This is for ggplot2>2.2.1, after an API was introduced for extracting
# information about the plot object.
find_panel_info_api <- function(b) {
# Workaround for check NOTE, until ggplot2 >2.2.1 is released
colon_colon <- `::`
# Given a built ggplot object, return x and y domains (data space coords) for
# each panel.
layout <- colon_colon("ggplot2", "summarise_layout")(b)
coord <- colon_colon("ggplot2", "summarise_coord")(b)
layers <- colon_colon("ggplot2", "summarise_layers")(b)
# Given x and y scale objects and a coord object, return a list that has
# the bases of log transformations for x and y, or NULL if it's not a
# log transform.
get_log_bases <- function(xscale, yscale, coord) {
# Given a transform object, find the log base; if the transform object is
# NULL, or if it's not a log transform, return NA.
get_log_base <- function(trans) {
if (!is.null(trans) && grepl("^log-", trans$name)) {
environment(trans$transform)$base
} else {
NA_real_
}
}
# First look for log base in scale, then coord; otherwise NULL.
list(
x = get_log_base(xscale$trans) %OR% coord$xlog %OR% NULL,
y = get_log_base(yscale$trans) %OR% coord$ylog %OR% NULL
)
}
# Given x/y min/max, and the x/y scale objects, create a list that
# represents the domain. Note that the x/y min/max should be taken from
# the layout summary table, not the scale objects.
get_domain <- function(xmin, xmax, ymin, ymax, xscale, yscale) {
is_reverse <- function(scale) {
identical(scale$trans$name, "reverse")
}
domain <- list(
left = xmin,
right = xmax,
bottom = ymin,
top = ymax
)
if (is_reverse(xscale)) {
domain$left <- -domain$left
domain$right <- -domain$right
}
if (is_reverse(yscale)) {
domain$top <- -domain$top
domain$bottom <- -domain$bottom
}
domain
}
# Rename the items in vars to have names like panelvar1, panelvar2.
rename_panel_vars <- function(vars) {
for (i in seq_along(vars)) {
names(vars)[i] <- paste0("panelvar", i)
}
vars
}
get_mappings <- function(layers, layout, coord) {
# For simplicity, we'll just use the mapping from the first layer of the
# ggplot object. The original uses quoted expressions; convert to
# character.
mapping <- layers$mapping[[1]]
# lapply'ing as.character results in unexpected behavior for expressions
# like `wt/2`; deparse handles it correctly.
mapping <- lapply(mapping, deparse)
# If either x or y is not present, give it a NULL entry.
mapping <- mergeVectors(list(x = NULL, y = NULL), mapping)
# The names (not values) of panel vars are the same across all panels,
# so just look at the first one. Also, the order of panel vars needs
# to be reversed.
vars <- rev(layout$vars[[1]])
for (i in seq_along(vars)) {
mapping[[paste0("panelvar", i)]] <- names(vars)[i]
}
if (isTRUE(coord$flip)) {
mapping[c("x", "y")] <- mapping[c("y", "x")]
}
mapping
}
# Mapping is constant across all panels, so get it here and reuse later.
mapping <- get_mappings(layers, layout, coord)
# If coord_flip is used, these need to be swapped
flip_xy <- function(layout) {
l <- layout
l$xscale <- layout$yscale
l$yscale <- layout$xscale
l$xmin <- layout$ymin
l$xmax <- layout$ymax
l$ymin <- layout$xmin
l$ymax <- layout$xmax
l
}
if (coord$flip) {
layout <- flip_xy(layout)
}
# Iterate over each row in the layout data frame
lapply(seq_len(nrow(layout)), function(i) {
# Slice out one row, use it as a list. The (former) list-cols are still
# in lists, so we need to unwrap them.
l <- as.list(layout[i, ])
l$vars <- l$vars[[1]]
l$xscale <- l$xscale[[1]]
l$yscale <- l$yscale[[1]]
list(
panel = as.numeric(l$panel),
row = l$row,
col = l$col,
# Rename panel vars. They must also be in reversed order.
panel_vars = rename_panel_vars(rev(l$vars)),
log = get_log_bases(l$xscale, l$yscale, coord),
domain = get_domain(l$xmin, l$xmax, l$ymin, l$ymax, l$xscale, l$yscale),
mapping = mapping
)
})
}
# This is for ggplot2<=2.2.1, before an API was introduced for extracting
# information about the plot object. The "old" format was used before 2.1.0.
# The "new" format was used after 2.1.0, up to 2.2.1. The reason these two
# formats are mixed together in a single function is historical, and it's not
# worthwhile to separate them at this point.
find_panel_info_non_api <- function(b, ggplot_format) {
# Given a single range object (representing the data domain) from a built
# ggplot object, return the domain.
find_panel_domain <- function(b, panel_num, scalex_num = 1, scaley_num = 1) {
if (ggplot_format == "new") {
range <- b$layout$panel_ranges[[panel_num]]
} else {
range <- b$panel$ranges[[panel_num]]
}
domain <- list(
left = range$x.range[1],
right = range$x.range[2],
bottom = range$y.range[1],
top = range$y.range[2]
)
# Check for reversed scales
if (ggplot_format == "new") {
xscale <- b$layout$panel_scales$x[[scalex_num]]
yscale <- b$layout$panel_scales$y[[scaley_num]]
} else {
xscale <- b$panel$x_scales[[scalex_num]]
yscale <- b$panel$y_scales[[scaley_num]]
}
if (!is.null(xscale$trans) && xscale$trans$name == "reverse") {
domain$left <- -domain$left
domain$right <- -domain$right
}
if (!is.null(yscale$trans) && yscale$trans$name == "reverse") {
domain$top <- -domain$top
domain$bottom <- -domain$bottom
}
domain
}
# Given built ggplot object, return object with the log base for x and y if
# there are log scales or coord transforms.
check_log_scales <- function(b, scalex_num = 1, scaley_num = 1) {
# Given a vector of transformation names like c("log-10", "identity"),
# return the first log base, like 10. If none are present, return NULL.
extract_log_base <- function(names) {
names <- names[grepl("^log-", names)]
if (length(names) == 0)
return(NULL)
names <- names[1]
as.numeric(sub("^log-", "", names))
}
# Look for log scales and log coord transforms. People shouldn't use both.
x_names <- character(0)
y_names <- character(0)
# Continuous scales have a trans; discrete ones don't
if (ggplot_format == "new") {
if (!is.null(b$layout$panel_scales$x[[scalex_num]]$trans))
x_names <- b$layout$panel_scales$x[[scalex_num]]$trans$name
if (!is.null(b$layout$panel_scales$y[[scaley_num]]$trans))
y_names <- b$layout$panel_scales$y[[scaley_num]]$trans$name
} else {
if (!is.null(b$panel$x_scales[[scalex_num]]$trans))
x_names <- b$panel$x_scales[[scalex_num]]$trans$name
if (!is.null(b$panel$y_scales[[scaley_num]]$trans))
y_names <- b$panel$y_scales[[scaley_num]]$trans$name
}
coords <- b$plot$coordinates
if (!is.null(coords$trans)) {
if (!is.null(coords$trans$x))
x_names <- c(x_names, coords$trans$x$name)
if (!is.null(coords$trans$y))
y_names <- c(y_names, coords$trans$y$name)
}
# Keep only scale/trans names that start with "log-"
x_names <- x_names[grepl("^log-", x_names)]
y_names <- y_names[grepl("^log-", y_names)]
# Extract the log base from the trans name -- a string like "log-10".
list(
x = extract_log_base(x_names),
y = extract_log_base(y_names)
)
}
# Given a built ggplot object, return a named list of variables mapped to x
# and y. This function will be called for each panel, but in practice the
# result is always the same across panels, so we'll cache the result.
mappings_cache <- NULL
find_plot_mappings <- function(b) {
if (!is.null(mappings_cache))
return(mappings_cache)
# lapply'ing as.character results in unexpected behavior for expressions
# like `wt/2`. This works better.
mappings <- as.list(as.character(b$plot$mapping))
# If x or y mapping is missing, look in each layer for mappings and return
# the first one.
missing_mappings <- setdiff(c("x", "y"), names(mappings))
if (length(missing_mappings) != 0) {
# Grab mappings for each layer
layer_mappings <- lapply(b$plot$layers, function(layer) {
lapply(layer$mapping, as.character)
})
# Get just the first x or y value in the combined list of plot and layer
# mappings.
mappings <- c(list(mappings), layer_mappings)
mappings <- Reduce(x = mappings, init = list(x = NULL, y = NULL),
function(init, m) {
if (is.null(init$x) && !is.null(m$x)) init$x <- m$x
if (is.null(init$y) && !is.null(m$y)) init$y <- m$y
init
}
)
}
# Look for CoordFlip
if (inherits(b$plot$coordinates, "CoordFlip")) {
mappings[c("x", "y")] <- mappings[c("y", "x")]
}
mappings_cache <<- mappings
mappings
}
if (ggplot_format == "new") {
layout <- b$layout$panel_layout
} else {
layout <- b$panel$layout
}
# Convert factor to numbers
layout$PANEL <- as.integer(as.character(layout$PANEL))
# Names of facets
facet_vars <- NULL
if (ggplot_format == "new") {
facet <- b$layout$facet
if (inherits(facet, "FacetGrid")) {
facet_vars <- vapply(c(facet$params$cols, facet$params$rows), as.character, character(1))
} else if (inherits(facet, "FacetWrap")) {
facet_vars <- vapply(facet$params$facets, as.character, character(1))
}
} else {
facet <- b$plot$facet
if (inherits(facet, "grid")) {
facet_vars <- vapply(c(facet$cols, facet$rows), as.character, character(1))
} else if (inherits(facet, "wrap")) {
facet_vars <- vapply(facet$facets, as.character, character(1))
}
}
# Iterate over each row in the layout data frame
lapply(seq_len(nrow(layout)), function(i) {
# Slice out one row
l <- layout[i, ]
scale_x <- l$SCALE_X
scale_y <- l$SCALE_Y
mapping <- find_plot_mappings(b)
# For each of the faceting variables, get the value of that variable in
# the current panel. Default to empty _named_ list so that it's sent as a
# JSON object, not array.
panel_vars <- list(a = NULL)[0]
for (i in seq_along(facet_vars)) {
var_name <- facet_vars[[i]]
vname <- paste0("panelvar", i)
mapping[[vname]] <- var_name
panel_vars[[vname]] <- l[[var_name]]
}
list(
panel = l$PANEL,
row = l$ROW,
col = l$COL,
panel_vars = panel_vars,
scale_x = scale_x,
scale_y = scale_x,
log = check_log_scales(b, scale_x, scale_y),
domain = find_panel_domain(b, l$PANEL, scale_x, scale_y),
mapping = mapping
)
})
}
# Given a gtable object, return the x and y ranges (in pixel dimensions)
find_panel_ranges <- function(g, pixelratio, res) {
# Given a vector of unit objects, return logical vector indicating which ones
# are "null" units. These units use the remaining available width/height --
# that is, the space not occupied by elements that have an absolute size.
is_null_unit <- function(x) {
# A vector of units can be either a list of individual units (a unit.list
# object), each with their own set of attributes, or an atomic vector with
# one set of attributes. ggplot2 switched from the former (in version
# 1.0.1) to the latter. We need to make sure that we get the correct
# result in both cases.
if (inherits(x, "unit.list")) {
# For ggplot2 <= 1.0.1
vapply(x, FUN.VALUE = logical(1), function(u) {
isTRUE(attr(u, "unit", exact = TRUE) == "null")
})
} else {
# For later versions of ggplot2
attr(x, "unit", exact = TRUE) == "null"
}
}
# Workaround for a bug in the quartz device. If you have a 400x400 image and
# run `convertWidth(unit(1, "npc"), "native")`, the result will depend on
# res setting of the device. If res=72, then it returns 400 (as expected),
# but if, e.g., res=96, it will return 300, which is incorrect.
devScaleFactor <- 1
if (grepl("quartz", names(grDevices::dev.cur()), fixed = TRUE)) {
devScaleFactor <- res / 72
}
# Convert a unit (or vector of units) to a numeric vector of pixel sizes
h_px <- function(x) {
devScaleFactor * grid::convertHeight(x, "native", valueOnly = TRUE)
}
w_px <- function(x) {
devScaleFactor * grid::convertWidth(x, "native", valueOnly = TRUE)
}
# Given a vector of relative sizes (in grid units), and a function for
# converting grid units to numeric pixels, return a list with: known pixel
# dimensions, scalable dimensions, and the overall space for the scalable
# objects.
find_size_info <- function(rel_sizes, unit_to_px) {
# Total pixels (in height or width)
total_px <- unit_to_px(grid::unit(1, "npc"))
# Calculate size of all panel(s) together. Panels (and only panels) have
# null size.
null_idx <- is_null_unit(rel_sizes)
# All the absolute heights. At this point, null heights are 0. We need to
# calculate them separately and add them in later.
px_sizes <- unit_to_px(rel_sizes)
# Mark the null heights as NA.
px_sizes[null_idx] <- NA_real_
# The plotting panels all are 'null' units.
null_sizes <- rep(NA_real_, length(rel_sizes))
null_sizes[null_idx] <- as.numeric(rel_sizes[null_idx])
# Total size allocated for panels is the total image size minus absolute
# (non-panel) elements.
panel_px_total <- total_px - sum(px_sizes, na.rm = TRUE)
# Size of a 1null unit
null_px <- abs(panel_px_total / sum(null_sizes, na.rm = TRUE))
# This returned list contains:
# * px_sizes: A vector of known pixel dimensions. The values that were
# null units will be assigned NA. The null units are ones that scale
# when the plotting area is resized.
# * null_sizes: A vector of the null units. All others will be assigned
# NA. The null units often are 1, but they may be any value, especially
# when using coord_fixed.
# * null_px: The size (in pixels) of a 1null unit.
# * null_px_scaled: The size (in pixels) of a 1null unit when scaled to
# fit a smaller dimension (used for plots with coord_fixed).
list(
px_sizes = abs(px_sizes),
null_sizes = null_sizes,
null_px = null_px,
null_px_scaled = null_px
)
}
# Given a size_info, return absolute pixel positions
size_info_to_px <- function(info) {
px_sizes <- info$px_sizes
null_idx <- !is.na(info$null_sizes)
px_sizes[null_idx] <- info$null_sizes[null_idx] * info$null_px_scaled
# If this direction is scaled down because of coord_fixed, we need to add an
# offset so that the pixel locations are centered.
offset <- (info$null_px - info$null_px_scaled) *
sum(info$null_sizes, na.rm = TRUE) / 2
# Get absolute pixel positions
cumsum(px_sizes) + offset
}
heights_info <- find_size_info(g$heights, h_px)
widths_info <- find_size_info(g$widths, w_px)
if (g$respect) {
# This is a plot with coord_fixed. The grid 'respect' option means to use
# the same pixel value for 1null, for width and height. We want the
# smaller of the two values -- that's what makes the plot fit in the
# viewport.
null_px_min <- min(heights_info$null_px, widths_info$null_px)
heights_info$null_px_scaled <- null_px_min
widths_info$null_px_scaled <- null_px_min
}
# Convert to absolute pixel positions
y_pos <- size_info_to_px(heights_info)
x_pos <- size_info_to_px(widths_info)
# Match up the pixel dimensions to panels
layout <- g$layout
# For panels:
# * For facet_wrap, they'll be named "panel-1", "panel-2", etc.
# * For no facet or facet_grid, they'll just be named "panel". For
# facet_grid, we need to re-order the layout table. Assume that panel
# numbers go from left to right, then next row.
# Assign a number to each panel, corresponding to PANEl in the built ggplot
# object.
layout <- layout[grepl("^panel", layout$name), ]
layout <- layout[order(layout$t, layout$l), ]
layout$panel <- seq_len(nrow(layout))
# When using a HiDPI client on a Linux server, the pixel
# dimensions are doubled, so we have to divide the dimensions by
# `pixelratio`. When a HiDPI client is used on a Mac server (with
# the quartz device), the pixel dimensions _aren't_ doubled, even though
# the image has double size. In the latter case we don't have to scale the
# numbers down.
pix_ratio <- 1
if (!grepl("^quartz", names(grDevices::dev.cur()))) {
pix_ratio <- pixelratio
}
# Return list of lists, where each inner list has left, right, top, bottom
# values for a panel
lapply(seq_len(nrow(layout)), function(i) {
p <- layout[i, , drop = FALSE]
list(
left = x_pos[p$l - 1] / pix_ratio,
right = x_pos[p$r] / pix_ratio,
bottom = y_pos[p$b] / pix_ratio,
top = y_pos[p$t - 1] / pix_ratio
)
})
}
YTLogos/shiny documentation built on May 7, 2019, 10:38 a.m.
R Package Documentation
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Defines functions renderPlot getPrevPlotCoordmap getGgplotCoordmap find_panel_info find_panel_info_api find_panel_info_non_api find_panel_ranges
Documented in renderPlot
#' Plot Output
#'
#' Renders a reactive plot that is suitable for assigning to an \code{output}
#' slot.
#'
#' The corresponding HTML output tag should be \code{div} or \code{img} and have
#' the CSS class name \code{shiny-plot-output}.
#'
#' @section Interactive plots:
#'
#' With ggplot2 graphics, the code in \code{renderPlot} should return a ggplot
#' object; if instead the code prints the ggplot2 object with something like
#' \code{print(p)}, then the coordinates for interactive graphics will not be
#' properly scaled to the data space.
#'
#' See \code{\link{plotOutput}} for more information about interactive plots.
#'
#' @seealso For the corresponding client-side output function, and example
#' usage, see \code{\link{plotOutput}}. For more details on how the plots are
#' generated, and how to control the output, see \code{\link{plotPNG}}.
#'
#' @param expr An expression that generates a plot.
#' @param width,height The width/height of the rendered plot, in pixels; or
#' \code{'auto'} to use the \code{offsetWidth}/\code{offsetHeight} of the HTML
#' element that is bound to this plot. You can also pass in a function that
#' returns the width/height in pixels or \code{'auto'}; in the body of the
#' function you may reference reactive values and functions. When rendering an
#' inline plot, you must provide numeric values (in pixels) to both
#' \code{width} and \code{height}.
#' @param res Resolution of resulting plot, in pixels per inch. This value is
#' passed to \code{\link[grDevices]{png}}. Note that this affects the resolution of PNG
#' rendering in R; it won't change the actual ppi of the browser.
#' @param ... Arguments to be passed through to \code{\link[grDevices]{png}}.
#' These can be used to set the width, height, background color, etc.
#' @param env The environment in which to evaluate \code{expr}.
#' @param quoted Is \code{expr} a quoted expression (with \code{quote()})? This
#' is useful if you want to save an expression in a variable.
#' @param execOnResize If \code{FALSE} (the default), then when a plot is
#' resized, Shiny will \emph{replay} the plot drawing commands with
#' \code{\link[grDevices]{replayPlot}()} instead of re-executing \code{expr}.
#' This can result in faster plot redrawing, but there may be rare cases where
#' it is undesirable. If you encounter problems when resizing a plot, you can
#' have Shiny re-execute the code on resize by setting this to \code{TRUE}.
#' @param outputArgs A list of arguments to be passed through to the implicit
#' call to \code{\link{plotOutput}} when \code{renderPlot} is used in an
#' interactive R Markdown document.
#' @export
renderPlot <- function(expr, width='auto', height='auto', res=72, ...,
env=parent.frame(), quoted=FALSE,
execOnResize=FALSE, outputArgs=list()
) {
# This ..stacktraceon is matched by a ..stacktraceoff.. when plotFunc
# is called
installExprFunction(expr, "func", env, quoted, ..stacktraceon = TRUE)
args <- list(...)
if (is.function(width))
widthWrapper <- reactive({ width() })
else
widthWrapper <- function() { width }
if (is.function(height))
heightWrapper <- reactive({ height() })
else
heightWrapper <- function() { height }
# A modified version of print.ggplot which returns the built ggplot object
# as well as the gtable grob. This overrides the ggplot::print.ggplot
# method, but only within the context of renderPlot. The reason this needs
# to be a (pseudo) S3 method is so that, if an object has a class in
# addition to ggplot, and there's a print method for that class, that we
# won't override that method. https://github.com/rstudio/shiny/issues/841
print.ggplot <- function(x) {
grid::grid.newpage()
build <- ggplot2::ggplot_build(x)
gtable <- ggplot2::ggplot_gtable(build)
grid::grid.draw(gtable)
structure(list(
build = build,
gtable = gtable
), class = "ggplot_build_gtable")
}
getDims <- function() {
width <- widthWrapper()
height <- heightWrapper()
# Note that these are reactive calls. A change to the width and height
# will inherently cause a reactive plot to redraw (unless width and
# height were explicitly specified).
if (width == 'auto')
width <- session$clientData[[paste0('output_', outputName, '_width')]]
if (height == 'auto')
height <- session$clientData[[paste0('output_', outputName, '_height')]]
list(width = width, height = height)
}
# Vars to store session and output, so that they can be accessed from
# the plotObj() reactive.
session <- NULL
outputName <- NULL
# This function is the one that's returned from renderPlot(), and gets
# wrapped in an observer when the output value is assigned. The expression
# passed to renderPlot() is actually run in plotObj(); this function can only
# replay a plot if the width/height changes.
renderFunc <- function(shinysession, name, ...) {
session <<- shinysession
outputName <<- name
dims <- getDims()
if (is.null(dims$width) || is.null(dims$height) ||
dims$width <= 0 || dims$height <= 0) {
return(NULL)
}
# The reactive that runs the expr in renderPlot()
plotData <- plotObj()
img <- plotData$img
# If only the width/height have changed, simply replay the plot and make a
# new img.
if (dims$width != img$width || dims$height != img$height) {
pixelratio <- session$clientData$pixelratio %OR% 1
coordmap <- NULL
plotFunc <- function() {
..stacktraceon..(grDevices::replayPlot(plotData$recordedPlot))
# Coordmap must be recalculated after replaying plot, because pixel
# dimensions will have changed.
if (inherits(plotData$plotResult, "ggplot_build_gtable")) {
coordmap <<- getGgplotCoordmap(plotData$plotResult, pixelratio, res)
} else {
coordmap <<- getPrevPlotCoordmap(dims$width, dims$height)
}
}
outfile <- ..stacktraceoff..(
plotPNG(plotFunc, width = dims$width*pixelratio, height = dims$height*pixelratio,
res = res*pixelratio)
)
on.exit(unlink(outfile))
img <- dropNulls(list(
src = session$fileUrl(name, outfile, contentType='image/png'),
width = dims$width,
height = dims$height,
coordmap = coordmap,
# Get coordmap error message if present
error = attr(coordmap, "error", exact = TRUE)
))
}
img
}
plotObj <- reactive(label = "plotObj", {
if (execOnResize) {
dims <- getDims()
} else {
isolate({ dims <- getDims() })
}
if (is.null(dims$width) || is.null(dims$height) ||
dims$width <= 0 || dims$height <= 0) {
return(NULL)
}
# Resolution multiplier
pixelratio <- session$clientData$pixelratio %OR% 1
plotResult <- NULL
recordedPlot <- NULL
coordmap <- NULL
plotFunc <- function() {
success <-FALSE
tryCatch(
{
# This is necessary to enable displaylist recording
grDevices::dev.control(displaylist = "enable")
# Actually perform the plotting
result <- withVisible(func())
success <- TRUE
},
finally = {
if (!success) {
# If there was an error in making the plot, there's a good chance
# it's "Error in plot.new: figure margins too large". We need to
# take a reactive dependency on the width and height, so that the
# user's plotting code will re-execute when the plot is resized,
# instead of just replaying the previous plot (which errored).
getDims()
}
}
)
if (result$visible) {
# Use capture.output to squelch printing to the actual console; we
# are only interested in plot output
utils::capture.output({
# This ..stacktraceon.. negates the ..stacktraceoff.. that wraps
# the call to plotFunc. The value needs to be printed just in case
# it's an object that requires printing to generate plot output,
# similar to ggplot2. But for base graphics, it would already have
# been rendered when func was called above, and the print should
# have no effect.
plotResult <<- ..stacktraceon..(print(result$value))
})
}
recordedPlot <<- grDevices::recordPlot()
if (inherits(plotResult, "ggplot_build_gtable")) {
coordmap <<- getGgplotCoordmap(plotResult, pixelratio, res)
} else {
coordmap <<- getPrevPlotCoordmap(dims$width, dims$height)
}
}
# This ..stacktraceoff.. is matched by the `func` function's
# wrapFunctionLabel(..stacktraceon=TRUE) call near the beginning of
# renderPlot, and by the ..stacktraceon.. in plotFunc where ggplot objects
# are printed
outfile <- ..stacktraceoff..(
do.call(plotPNG, c(plotFunc, width=dims$width*pixelratio,
height=dims$height*pixelratio, res=res*pixelratio, args))
)
on.exit(unlink(outfile))
list(
# img is the content that gets sent to the client.
img = dropNulls(list(
src = session$fileUrl(outputName, outfile, contentType='image/png'),
width = dims$width,
height = dims$height,
coordmap = coordmap,
# Get coordmap error message if present.
error = attr(coordmap, "error", exact = TRUE)
)),
# Returned value from expression in renderPlot() -- may be a printable
# object like ggplot2. Needed just in case we replayPlot and need to get
# a coordmap again.
plotResult = plotResult,
recordedPlot = recordedPlot
)
})
# If renderPlot isn't going to adapt to the height of the div, then the
# div needs to adapt to the height of renderPlot. By default, plotOutput
# sets the height to 400px, so to make it adapt we need to override it
# with NULL.
outputFunc <- plotOutput
if (!identical(height, 'auto')) formals(outputFunc)['height'] <- list(NULL)
markRenderFunction(outputFunc, renderFunc, outputArgs = outputArgs)
}
# The coordmap extraction functions below return something like the examples
# below. For base graphics:
# plot(mtcars$wt, mtcars$mpg)
# str(getPrevPlotCoordmap(400, 300))
# List of 1
# $ :List of 4
# ..$ domain :List of 4
# .. ..$ left : num 1.36
# .. ..$ right : num 5.58
# .. ..$ bottom: num 9.46
# .. ..$ top : num 34.8
# ..$ range :List of 4
# .. ..$ left : num 50.4
# .. ..$ right : num 373
# .. ..$ bottom: num 199
# .. ..$ top : num 79.6
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ mapping: Named list()
#
# For ggplot2, first you need to define the print.ggplot function from inside
# renderPlot, then use it to print the plot:
# print.ggplot <- function(x) {
# grid::grid.newpage()
#
# build <- ggplot2::ggplot_build(x)
#
# gtable <- ggplot2::ggplot_gtable(build)
# grid::grid.draw(gtable)
#
# structure(list(
# build = build,
# gtable = gtable
# ), class = "ggplot_build_gtable")
# }
#
# p <- print(ggplot(mtcars, aes(wt, mpg)) + geom_point())
# str(getGgplotCoordmap(p, 1, 72))
# List of 1
# $ :List of 10
# ..$ panel : int 1
# ..$ row : int 1
# ..$ col : int 1
# ..$ panel_vars: Named list()
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 2
# .. ..$ x: chr "wt"
# .. ..$ y: chr "mpg"
# ..$ range :List of 4
# .. ..$ left : num 40.8
# .. ..$ right : num 446
# .. ..$ bottom: num 263
# .. ..$ top : num 14.4
#
# With a faceted ggplot2 plot, the outer list contains two objects, each of
# which represents one panel. In this example, there is one panelvar, but there
# can be up to two of them.
# mtc <- mtcars
# mtc$am <- factor(mtc$am)
# p <- print(ggplot(mtc, aes(wt, mpg)) + geom_point() + facet_wrap(~ am))
# str(getGgplotCoordmap(p, 1, 72))
# List of 2
# $ :List of 10
# ..$ panel : int 1
# ..$ row : int 1
# ..$ col : int 1
# ..$ panel_vars:List of 1
# .. ..$ panelvar1: Factor w/ 2 levels "0","1": 1
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 3
# .. ..$ x : chr "wt"
# .. ..$ y : chr "mpg"
# .. ..$ panelvar1: chr "am"
# ..$ range :List of 4
# .. ..$ left : num 45.6
# .. ..$ right : num 317
# .. ..$ bottom: num 251
# .. ..$ top : num 35.7
# $ :List of 10
# ..$ panel : int 2
# ..$ row : int 1
# ..$ col : int 2
# ..$ panel_vars:List of 1
# .. ..$ panelvar1: Factor w/ 2 levels "0","1": 2
# ..$ log :List of 2
# .. ..$ x: NULL
# .. ..$ y: NULL
# ..$ domain :List of 4
# .. ..$ left : num 1.32
# .. ..$ right : num 5.62
# .. ..$ bottom: num 9.22
# .. ..$ top : num 35.1
# ..$ mapping :List of 3
# .. ..$ x : chr "wt"
# .. ..$ y : chr "mpg"
# .. ..$ panelvar1: chr "am"
# ..$ range :List of 4
# .. ..$ left : num 322
# .. ..$ right : num 594
# .. ..$ bottom: num 251
# .. ..$ top : num 35.7
# Get a coordmap for the previous plot made with base graphics.
# Requires width and height of output image, in pixels.
# Must be called before the graphics device is closed.
getPrevPlotCoordmap <- function(width, height) {
usrCoords <- graphics::par('usr')
usrBounds <- usrCoords
if (graphics::par('xlog')) {
usrBounds[c(1,2)] <- 10 ^ usrBounds[c(1,2)]
}
if (graphics::par('ylog')) {
usrBounds[c(3,4)] <- 10 ^ usrBounds[c(3,4)]
}
# Wrapped in double list because other types of plots can have multiple panels.
list(list(
# Bounds of the plot area, in data space
domain = list(
left = usrCoords[1],
right = usrCoords[2],
bottom = usrCoords[3],
top = usrCoords[4]
),
# The bounds of the plot area, in DOM pixels
range = list(
left = graphics::grconvertX(usrBounds[1], 'user', 'nfc') * width,
right = graphics::grconvertX(usrBounds[2], 'user', 'nfc') * width,
bottom = (1-graphics::grconvertY(usrBounds[3], 'user', 'nfc')) * height - 1,
top = (1-graphics::grconvertY(usrBounds[4], 'user', 'nfc')) * height - 1
),
log = list(
x = if (graphics::par('xlog')) 10 else NULL,
y = if (graphics::par('ylog')) 10 else NULL
),
# We can't extract the original variable names from a base graphic.
# `mapping` is an empty _named_ list, so that it is converted to an object
# (not an array) in JSON.
mapping = list(x = NULL)[0]
))
}
# Given a ggplot_build_gtable object, return a coordmap for it.
getGgplotCoordmap <- function(p, pixelratio, res) {
if (!inherits(p, "ggplot_build_gtable"))
return(NULL)
tryCatch({
# Get info from built ggplot object
info <- find_panel_info(p$build)
# Get ranges from gtable - it's possible for this to return more elements than
# info, because it calculates positions even for panels that aren't present.
# This can happen with facet_wrap.
ranges <- find_panel_ranges(p$gtable, pixelratio, res)
for (i in seq_along(info)) {
info[[i]]$range <- ranges[[i]]
}
return(info)
}, error = function(e) {
# If there was an error extracting info from the ggplot object, just return
# a list with the error message.
return(structure(list(), error = e$message))
})
}
find_panel_info <- function(b) {
# Structure of ggplot objects changed after 2.1.0. After 2.2.1, there was a
# an API for extracting the necessary information.
ggplot_ver <- utils::packageVersion("ggplot2")
if (ggplot_ver > "2.2.1") {
find_panel_info_api(b)
} else if (ggplot_ver > "2.1.0") {
find_panel_info_non_api(b, ggplot_format = "new")
} else {
find_panel_info_non_api(b, ggplot_format = "old")
}
}
# This is for ggplot2>2.2.1, after an API was introduced for extracting
# information about the plot object.
find_panel_info_api <- function(b) {
# Workaround for check NOTE, until ggplot2 >2.2.1 is released
colon_colon <- `::`
# Given a built ggplot object, return x and y domains (data space coords) for
# each panel.
layout <- colon_colon("ggplot2", "summarise_layout")(b)
coord <- colon_colon("ggplot2", "summarise_coord")(b)
layers <- colon_colon("ggplot2", "summarise_layers")(b)
# Given x and y scale objects and a coord object, return a list that has
# the bases of log transformations for x and y, or NULL if it's not a
# log transform.
get_log_bases <- function(xscale, yscale, coord) {
# Given a transform object, find the log base; if the transform object is
# NULL, or if it's not a log transform, return NA.
get_log_base <- function(trans) {
if (!is.null(trans) && grepl("^log-", trans$name)) {
environment(trans$transform)$base
} else {
NA_real_
}
}
# First look for log base in scale, then coord; otherwise NULL.
list(
x = get_log_base(xscale$trans) %OR% coord$xlog %OR% NULL,
y = get_log_base(yscale$trans) %OR% coord$ylog %OR% NULL
)
}
# Given x/y min/max, and the x/y scale objects, create a list that
# represents the domain. Note that the x/y min/max should be taken from
# the layout summary table, not the scale objects.
get_domain <- function(xmin, xmax, ymin, ymax, xscale, yscale) {
is_reverse <- function(scale) {
identical(scale$trans$name, "reverse")
}
domain <- list(
left = xmin,
right = xmax,
bottom = ymin,
top = ymax
)
if (is_reverse(xscale)) {
domain$left <- -domain$left
domain$right <- -domain$right
}
if (is_reverse(yscale)) {
domain$top <- -domain$top
domain$bottom <- -domain$bottom
}
domain
}
# Rename the items in vars to have names like panelvar1, panelvar2.
rename_panel_vars <- function(vars) {
for (i in seq_along(vars)) {
names(vars)[i] <- paste0("panelvar", i)
}
vars
}
get_mappings <- function(layers, layout, coord) {
# For simplicity, we'll just use the mapping from the first layer of the
# ggplot object. The original uses quoted expressions; convert to
# character.
mapping <- layers$mapping[[1]]
# lapply'ing as.character results in unexpected behavior for expressions
# like `wt/2`; deparse handles it correctly.
mapping <- lapply(mapping, deparse)
# If either x or y is not present, give it a NULL entry.
mapping <- mergeVectors(list(x = NULL, y = NULL), mapping)
# The names (not values) of panel vars are the same across all panels,
# so just look at the first one. Also, the order of panel vars needs
# to be reversed.
vars <- rev(layout$vars[[1]])
for (i in seq_along(vars)) {
mapping[[paste0("panelvar", i)]] <- names(vars)[i]
}
if (isTRUE(coord$flip)) {
mapping[c("x", "y")] <- mapping[c("y", "x")]
}
mapping
}
# Mapping is constant across all panels, so get it here and reuse later.
mapping <- get_mappings(layers, layout, coord)
# If coord_flip is used, these need to be swapped
flip_xy <- function(layout) {
l <- layout
l$xscale <- layout$yscale
l$yscale <- layout$xscale
l$xmin <- layout$ymin
l$xmax <- layout$ymax
l$ymin <- layout$xmin
l$ymax <- layout$xmax
l
}
if (coord$flip) {
layout <- flip_xy(layout)
}
# Iterate over each row in the layout data frame
lapply(seq_len(nrow(layout)), function(i) {
# Slice out one row, use it as a list. The (former) list-cols are still
# in lists, so we need to unwrap them.
l <- as.list(layout[i, ])
l$vars <- l$vars[[1]]
l$xscale <- l$xscale[[1]]
l$yscale <- l$yscale[[1]]
list(
panel = as.numeric(l$panel),
row = l$row,
col = l$col,
# Rename panel vars. They must also be in reversed order.
panel_vars = rename_panel_vars(rev(l$vars)),
log = get_log_bases(l$xscale, l$yscale, coord),
domain = get_domain(l$xmin, l$xmax, l$ymin, l$ymax, l$xscale, l$yscale),
mapping = mapping
)
})
}
# This is for ggplot2<=2.2.1, before an API was introduced for extracting
# information about the plot object. The "old" format was used before 2.1.0.
# The "new" format was used after 2.1.0, up to 2.2.1. The reason these two
# formats are mixed together in a single function is historical, and it's not
# worthwhile to separate them at this point.
find_panel_info_non_api <- function(b, ggplot_format) {
# Given a single range object (representing the data domain) from a built
# ggplot object, return the domain.
find_panel_domain <- function(b, panel_num, scalex_num = 1, scaley_num = 1) {
if (ggplot_format == "new") {
range <- b$layout$panel_ranges[[panel_num]]
} else {
range <- b$panel$ranges[[panel_num]]
}
domain <- list(
left = range$x.range[1],
right = range$x.range[2],
bottom = range$y.range[1],
top = range$y.range[2]
)
# Check for reversed scales
if (ggplot_format == "new") {
xscale <- b$layout$panel_scales$x[[scalex_num]]
yscale <- b$layout$panel_scales$y[[scaley_num]]
} else {
xscale <- b$panel$x_scales[[scalex_num]]
yscale <- b$panel$y_scales[[scaley_num]]
}
if (!is.null(xscale$trans) && xscale$trans$name == "reverse") {
domain$left <- -domain$left
domain$right <- -domain$right
}
if (!is.null(yscale$trans) && yscale$trans$name == "reverse") {
domain$top <- -domain$top
domain$bottom <- -domain$bottom
}
domain
}
# Given built ggplot object, return object with the log base for x and y if
# there are log scales or coord transforms.
check_log_scales <- function(b, scalex_num = 1, scaley_num = 1) {
# Given a vector of transformation names like c("log-10", "identity"),
# return the first log base, like 10. If none are present, return NULL.
extract_log_base <- function(names) {
names <- names[grepl("^log-", names)]
if (length(names) == 0)
return(NULL)
names <- names[1]
as.numeric(sub("^log-", "", names))
}
# Look for log scales and log coord transforms. People shouldn't use both.
x_names <- character(0)
y_names <- character(0)
# Continuous scales have a trans; discrete ones don't
if (ggplot_format == "new") {
if (!is.null(b$layout$panel_scales$x[[scalex_num]]$trans))
x_names <- b$layout$panel_scales$x[[scalex_num]]$trans$name
if (!is.null(b$layout$panel_scales$y[[scaley_num]]$trans))
y_names <- b$layout$panel_scales$y[[scaley_num]]$trans$name
} else {
if (!is.null(b$panel$x_scales[[scalex_num]]$trans))
x_names <- b$panel$x_scales[[scalex_num]]$trans$name
if (!is.null(b$panel$y_scales[[scaley_num]]$trans))
y_names <- b$panel$y_scales[[scaley_num]]$trans$name
}
coords <- b$plot$coordinates
if (!is.null(coords$trans)) {
if (!is.null(coords$trans$x))
x_names <- c(x_names, coords$trans$x$name)
if (!is.null(coords$trans$y))
y_names <- c(y_names, coords$trans$y$name)
}
# Keep only scale/trans names that start with "log-"
x_names <- x_names[grepl("^log-", x_names)]
y_names <- y_names[grepl("^log-", y_names)]
# Extract the log base from the trans name -- a string like "log-10".
list(
x = extract_log_base(x_names),
y = extract_log_base(y_names)
)
}
# Given a built ggplot object, return a named list of variables mapped to x
# and y. This function will be called for each panel, but in practice the
# result is always the same across panels, so we'll cache the result.
mappings_cache <- NULL
find_plot_mappings <- function(b) {
if (!is.null(mappings_cache))
return(mappings_cache)
# lapply'ing as.character results in unexpected behavior for expressions
# like `wt/2`. This works better.
mappings <- as.list(as.character(b$plot$mapping))
# If x or y mapping is missing, look in each layer for mappings and return
# the first one.
missing_mappings <- setdiff(c("x", "y"), names(mappings))
if (length(missing_mappings) != 0) {
# Grab mappings for each layer
layer_mappings <- lapply(b$plot$layers, function(layer) {
lapply(layer$mapping, as.character)
})
# Get just the first x or y value in the combined list of plot and layer
# mappings.
mappings <- c(list(mappings), layer_mappings)
mappings <- Reduce(x = mappings, init = list(x = NULL, y = NULL),
function(init, m) {
if (is.null(init$x) && !is.null(m$x)) init$x <- m$x
if (is.null(init$y) && !is.null(m$y)) init$y <- m$y
init
}
)
}
# Look for CoordFlip
if (inherits(b$plot$coordinates, "CoordFlip")) {
mappings[c("x", "y")] <- mappings[c("y", "x")]
}
mappings_cache <<- mappings
mappings
}
if (ggplot_format == "new") {
layout <- b$layout$panel_layout
} else {
layout <- b$panel$layout
}
# Convert factor to numbers
layout$PANEL <- as.integer(as.character(layout$PANEL))
# Names of facets
facet_vars <- NULL
if (ggplot_format == "new") {
facet <- b$layout$facet
if (inherits(facet, "FacetGrid")) {
facet_vars <- vapply(c(facet$params$cols, facet$params$rows), as.character, character(1))
} else if (inherits(facet, "FacetWrap")) {
facet_vars <- vapply(facet$params$facets, as.character, character(1))
}
} else {
facet <- b$plot$facet
if (inherits(facet, "grid")) {
facet_vars <- vapply(c(facet$cols, facet$rows), as.character, character(1))
} else if (inherits(facet, "wrap")) {
facet_vars <- vapply(facet$facets, as.character, character(1))
}
}
# Iterate over each row in the layout data frame
lapply(seq_len(nrow(layout)), function(i) {
# Slice out one row
l <- layout[i, ]
scale_x <- l$SCALE_X
scale_y <- l$SCALE_Y
mapping <- find_plot_mappings(b)
# For each of the faceting variables, get the value of that variable in
# the current panel. Default to empty _named_ list so that it's sent as a
# JSON object, not array.
panel_vars <- list(a = NULL)[0]
for (i in seq_along(facet_vars)) {
var_name <- facet_vars[[i]]
vname <- paste0("panelvar", i)
mapping[[vname]] <- var_name
panel_vars[[vname]] <- l[[var_name]]
}
list(
panel = l$PANEL,
row = l$ROW,
col = l$COL,
panel_vars = panel_vars,
scale_x = scale_x,
scale_y = scale_x,
log = check_log_scales(b, scale_x, scale_y),
domain = find_panel_domain(b, l$PANEL, scale_x, scale_y),
mapping = mapping
)
})
}
# Given a gtable object, return the x and y ranges (in pixel dimensions)
find_panel_ranges <- function(g, pixelratio, res) {
# Given a vector of unit objects, return logical vector indicating which ones
# are "null" units. These units use the remaining available width/height --
# that is, the space not occupied by elements that have an absolute size.
is_null_unit <- function(x) {
# A vector of units can be either a list of individual units (a unit.list
# object), each with their own set of attributes, or an atomic vector with
# one set of attributes. ggplot2 switched from the former (in version
# 1.0.1) to the latter. We need to make sure that we get the correct
# result in both cases.
if (inherits(x, "unit.list")) {
# For ggplot2 <= 1.0.1
vapply(x, FUN.VALUE = logical(1), function(u) {
isTRUE(attr(u, "unit", exact = TRUE) == "null")
})
} else {
# For later versions of ggplot2
attr(x, "unit", exact = TRUE) == "null"
}
}
# Workaround for a bug in the quartz device. If you have a 400x400 image and
# run `convertWidth(unit(1, "npc"), "native")`, the result will depend on
# res setting of the device. If res=72, then it returns 400 (as expected),
# but if, e.g., res=96, it will return 300, which is incorrect.
devScaleFactor <- 1
if (grepl("quartz", names(grDevices::dev.cur()), fixed = TRUE)) {
devScaleFactor <- res / 72
}
# Convert a unit (or vector of units) to a numeric vector of pixel sizes
h_px <- function(x) {
devScaleFactor * grid::convertHeight(x, "native", valueOnly = TRUE)
}
w_px <- function(x) {
devScaleFactor * grid::convertWidth(x, "native", valueOnly = TRUE)
}
# Given a vector of relative sizes (in grid units), and a function for
# converting grid units to numeric pixels, return a list with: known pixel
# dimensions, scalable dimensions, and the overall space for the scalable
# objects.
find_size_info <- function(rel_sizes, unit_to_px) {
# Total pixels (in height or width)
total_px <- unit_to_px(grid::unit(1, "npc"))
# Calculate size of all panel(s) together. Panels (and only panels) have
# null size.
null_idx <- is_null_unit(rel_sizes)
# All the absolute heights. At this point, null heights are 0. We need to
# calculate them separately and add them in later.
px_sizes <- unit_to_px(rel_sizes)
# Mark the null heights as NA.
px_sizes[null_idx] <- NA_real_
# The plotting panels all are 'null' units.
null_sizes <- rep(NA_real_, length(rel_sizes))
null_sizes[null_idx] <- as.numeric(rel_sizes[null_idx])
# Total size allocated for panels is the total image size minus absolute
# (non-panel) elements.
panel_px_total <- total_px - sum(px_sizes, na.rm = TRUE)
# Size of a 1null unit
null_px <- abs(panel_px_total / sum(null_sizes, na.rm = TRUE))
# This returned list contains:
# * px_sizes: A vector of known pixel dimensions. The values that were
# null units will be assigned NA. The null units are ones that scale
# when the plotting area is resized.
# * null_sizes: A vector of the null units. All others will be assigned
# NA. The null units often are 1, but they may be any value, especially
# when using coord_fixed.
# * null_px: The size (in pixels) of a 1null unit.
# * null_px_scaled: The size (in pixels) of a 1null unit when scaled to
# fit a smaller dimension (used for plots with coord_fixed).
list(
px_sizes = abs(px_sizes),
null_sizes = null_sizes,
null_px = null_px,
null_px_scaled = null_px
)
}
# Given a size_info, return absolute pixel positions
size_info_to_px <- function(info) {
px_sizes <- info$px_sizes
null_idx <- !is.na(info$null_sizes)
px_sizes[null_idx] <- info$null_sizes[null_idx] * info$null_px_scaled
# If this direction is scaled down because of coord_fixed, we need to add an
# offset so that the pixel locations are centered.
offset <- (info$null_px - info$null_px_scaled) *
sum(info$null_sizes, na.rm = TRUE) / 2
# Get absolute pixel positions
cumsum(px_sizes) + offset
}
heights_info <- find_size_info(g$heights, h_px)
widths_info <- find_size_info(g$widths, w_px)
if (g$respect) {
# This is a plot with coord_fixed. The grid 'respect' option means to use
# the same pixel value for 1null, for width and height. We want the
# smaller of the two values -- that's what makes the plot fit in the
# viewport.
null_px_min <- min(heights_info$null_px, widths_info$null_px)
heights_info$null_px_scaled <- null_px_min
widths_info$null_px_scaled <- null_px_min
}
# Convert to absolute pixel positions
y_pos <- size_info_to_px(heights_info)
x_pos <- size_info_to_px(widths_info)
# Match up the pixel dimensions to panels
layout <- g$layout
# For panels:
# * For facet_wrap, they'll be named "panel-1", "panel-2", etc.
# * For no facet or facet_grid, they'll just be named "panel". For
# facet_grid, we need to re-order the layout table. Assume that panel
# numbers go from left to right, then next row.
# Assign a number to each panel, corresponding to PANEl in the built ggplot
# object.
layout <- layout[grepl("^panel", layout$name), ]
layout <- layout[order(layout$t, layout$l), ]
layout$panel <- seq_len(nrow(layout))
# When using a HiDPI client on a Linux server, the pixel
# dimensions are doubled, so we have to divide the dimensions by
# `pixelratio`. When a HiDPI client is used on a Mac server (with
# the quartz device), the pixel dimensions _aren't_ doubled, even though
# the image has double size. In the latter case we don't have to scale the
# numbers down.
pix_ratio <- 1
if (!grepl("^quartz", names(grDevices::dev.cur()))) {
pix_ratio <- pixelratio
}
# Return list of lists, where each inner list has left, right, top, bottom
# values for a panel
lapply(seq_len(nrow(layout)), function(i) {
p <- layout[i, , drop = FALSE]
list(
left = x_pos[p$l - 1] / pix_ratio,
right = x_pos[p$r] / pix_ratio,
bottom = y_pos[p$b] / pix_ratio,
top = y_pos[p$t - 1] / pix_ratio
)
})
}
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