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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) { c(train_cartesian(scales\$x, coord\$limits\$x, "x", coord\$wise), train_cartesian(scales\$y, coord\$limits\$y, "y", coord\$wise)) } train_cartesian <- memoise(function(scale, limits, name, wise) { if (is.null(limits)) { range <- scale_dimension(scale) } else { range <- range(scale_transform(scale, limits)) if (wise) { scale\$limits <- limits range <- expand_range(range, scale\$expand[1], scale\$expand[2]) } } major <- rescale(scale_break_positions(scale), from = range) minor <- rescale(scale_breaks_minor_positions(scale), from = range) labels <- scale_labels(scale) out <- list(range = range, major = major, minor = minor, labels = labels) names(out) <- paste(name, names(out), sep = ".") out }) 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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