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#' Cartesian coordinates.
#'
#' The Cartesian coordinate system is the most familiar, and common, type of
#' coordinate system. Setting limits on the coordinate system will zoom the
#' plot (like you're looking at it with a magnifying class), and will not
#' change the underlying data like setting limits on a scale will.
#'
#' @param xlim limits for the x axis
#' @param ylim limits for the y axis
#' @param wise If \code{TRUE} will wisely expand the actual range of the plot
#' a little, in the way that setting the limits on the scales does
#' @export
#' @examples
#' # There are two ways of zooming the plot display: with scales or
#' # with coordinate systems. They work in two rather different ways.
#'
#' (p <- qplot(disp, wt, data=mtcars) + geom_smooth())
#'
#' # Setting the limits on a scale will throw away all data that's not
#' # inside these limits. This is equivalent to plotting a subset of
#' # the original data
#' p + scale_x_continuous(limits = c(325, 500))
#'
#' # Setting the limits on the coordinate system performs a visual zoom
#' # the data is unchanged, and we just view a small portion of the original
#' # plot. See how the axis labels are the same as the original data, and
#' # the smooth continue past the points visible on this plot.
#' p + coord_cartesian(xlim = c(325, 500))
#'
#' # You can see the same thing with this 2d histogram
#' (d <- ggplot(diamonds, aes(carat, price)) +
#' stat_bin2d(bins = 25, colour="grey50"))
#'
#' # When zooming the scale, the we get 25 new bins that are the same
#' # size on the plot, but represent smaller regions of the data space
#' d + scale_x_continuous(limits = c(0, 2))
#'
#' # When zooming the coordinate system, we see a subset of original 50 bins,
#' # displayed bigger
#' d + coord_cartesian(xlim = c(0, 2))
coord_cartesian <- function(xlim = NULL, ylim = NULL, wise = FALSE) {
coord(limits = list(x = xlim, y = ylim), wise = wise,
subclass = "cartesian")
}
#' @S3method is.linear cartesian
is.linear.cartesian <- function(coord) TRUE
#' @S3method coord_distance cartesian
coord_distance.cartesian <- function(coord, x, y, details) {
max_dist <- dist_euclidean(details$x.range, details$y.range)
dist_euclidean(x, y) / max_dist
}
#' @S3method coord_transform cartesian
coord_transform.cartesian <- function(., data, details) {
rescale_x <- function(data) rescale(data, from = details$x.range)
rescale_y <- function(data) rescale(data, from = details$y.range)
data <- transform_position(data, rescale_x, rescale_y)
transform_position(data, trim_infinite_01, trim_infinite_01)
}
#' @S3method coord_train cartesian
coord_train.cartesian <- function(coord, scales) {
if (is.null(coord$limits$x)) {
x.range <- scale_dimension(scales$x)
} else {
x.range <- range(scale_transform(scales$x, coord$limits[["x"]]))
if (coord$wise) {
scales$x$limits <- x.range
x.range <- expand_range(x.range,
scales$x$expand[1], scales$x$expand[2])
}
}
x.major <- rescale(scale_break_positions(scales$x), from = x.range)
x.minor <- rescale(scale_breaks_minor(scales$x), from = x.range)
x.labels <- scale_labels(scales$x)
if (is.null(coord$limits$y)) {
y.range <- scale_dimension(scales$y)
} else {
y.range <- range(scale_transform(scales$y, coord$limits$y))
if (coord$wise) {
scales$y$limits <- y.range
y.range <- expand_range(y.range,
scales$y$expand[1], scales$y$expand[2])
}
}
y.major <- rescale(scale_break_positions(scales$y), from = y.range)
y.minor <- rescale(scale_breaks_minor(scales$y), from = y.range)
y.labels <- scale_labels(scales$y)
list(
x.range = x.range, y.range = y.range,
x.major = x.major, x.minor = x.minor, x.labels = x.labels,
y.major = y.major, y.minor = y.minor, y.labels = y.labels
)
}
icon.cartesian <- function(.) {
gTree(children = gList(
segmentsGrob(c(0, 0.25), c(0.25, 0), c(1, 0.25), c(0.25, 1), gp=gpar(col="grey50", lwd=0.5)),
segmentsGrob(c(0, 0.75), c(0.75, 0), c(1, 0.75), c(0.75, 1), gp=gpar(col="grey50", lwd=0.5)),
segmentsGrob(c(0, 0.5), c(0.5, 0), c(1, 0.5), c(0.5, 1))
))
}
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