#' Create a new ggplot
#'
#' `ggplot()` initializes a ggplot object. It can be used to
#' declare the input data frame for a graphic and to specify the
#' set of plot aesthetics intended to be common throughout all
#' subsequent layers unless specifically overridden.
#'
#' `ggplot()` is used to construct the initial plot object,
#' and is almost always followed by a plus sign (`+`) to add
#' components to the plot.
#'
#' There are three common patterns used to invoke `ggplot()`:
#'
#' * `ggplot(data = df, mapping = aes(x, y, other aesthetics))`
#' * `ggplot(data = df)`
#' * `ggplot()`
#'
#' The first pattern is recommended if all layers use the same
#' data and the same set of aesthetics, although this method
#' can also be used when adding a layer using data from another
#' data frame.
#'
#' The second pattern specifies the default data frame to use
#' for the plot, but no aesthetics are defined up front. This
#' is useful when one data frame is used predominantly for the
#' plot, but the aesthetics vary from one layer to another.
#'
#' The third pattern initializes a skeleton `ggplot` object, which
#' is fleshed out as layers are added. This is useful when
#' multiple data frames are used to produce different layers, as
#' is often the case in complex graphics.
#'
#' The `data =` and `mapping =` specifications in the arguments are optional
#' (and are often omitted in practice), so long as the data and the mapping
#' values are passed into the function in the right order. In the examples
#' below, however, they are left in place for clarity.
#'
#' @param data Default dataset to use for plot. If not already a data.frame,
#' will be converted to one by [fortify()]. If not specified,
#' must be supplied in each layer added to the plot.
#' @param mapping Default list of aesthetic mappings to use for plot.
#' If not specified, must be supplied in each layer added to the plot.
#' @param ... Other arguments passed on to methods. Not currently used.
#' @param environment `r lifecycle::badge("deprecated")` Used prior to tidy
#' evaluation.
#' @export
#' @seealso
#' The `r link_book("first steps chapter", "getting-started")`
#' @examples
#' # Create a data frame with some sample data, then create a data frame
#' # containing the mean value for each group in the sample data.
#' set.seed(1)
#'
#' sample_df <- data.frame(
#' group = factor(rep(letters[1:3], each = 10)),
#' value = rnorm(30)
#' )
#'
#' group_means_df <- setNames(
#' aggregate(value ~ group, sample_df, mean),
#' c("group", "group_mean")
#' )
#'
#' # The following three code blocks create the same graphic, each using one
#' # of the three patterns specified above. In each graphic, the sample data
#' # are plotted in the first layer and the group means data frame is used to
#' # plot larger red points on top of the sample data in the second layer.
#'
#' # Pattern 1
#' # Both the `data` and `mapping` arguments are passed into the `ggplot()`
#' # call. Those arguments are omitted in the first `geom_point()` layer
#' # because they get passed along from the `ggplot()` call. Note that the
#' # second `geom_point()` layer re-uses the `x = group` aesthetic through
#' # that mechanism but overrides the y-position aesthetic.
#' ggplot(data = sample_df, mapping = aes(x = group, y = value)) +
#' geom_point() +
#' geom_point(
#' mapping = aes(y = group_mean), data = group_means_df,
#' colour = 'red', size = 3
#' )
#'
#' # Pattern 2
#' # Same plot as above, passing only the `data` argument into the `ggplot()`
#' # call. The `mapping` arguments are now required in each `geom_point()`
#' # layer because there is no `mapping` argument passed along from the
#' # `ggplot()` call.
#' ggplot(data = sample_df) +
#' geom_point(mapping = aes(x = group, y = value)) +
#' geom_point(
#' mapping = aes(x = group, y = group_mean), data = group_means_df,
#' colour = 'red', size = 3
#' )
#'
#' # Pattern 3
#' # Same plot as above, passing neither the `data` or `mapping` arguments
#' # into the `ggplot()` call. Both those arguments are now required in
#' # each `geom_point()` layer. This pattern can be particularly useful when
#' # creating more complex graphics with many layers using data from multiple
#' # data frames.
#' ggplot() +
#' geom_point(mapping = aes(x = group, y = value), data = sample_df) +
#' geom_point(
#' mapping = aes(x = group, y = group_mean), data = group_means_df,
#' colour = 'red', size = 3
#' )
ggplot <- function(data = NULL, mapping = aes(), ...,
environment = parent.frame()) {
UseMethod("ggplot")
}
#' @export
ggplot.default <- function(data = NULL, mapping = aes(), ...,
environment = parent.frame()) {
if (!missing(mapping) && !inherits(mapping, "uneval")) {
cli::cli_abort(c(
"{.arg mapping} must be created with {.fn aes}.",
"x" = "You've supplied {.obj_type_friendly {mapping}}."
))
}
data <- fortify(data, ...)
p <- structure(list(
data = data,
layers = list(),
scales = scales_list(),
guides = guides_list(),
mapping = mapping,
theme = list(),
coordinates = coord_cartesian(default = TRUE),
facet = facet_null(),
plot_env = environment,
layout = ggproto(NULL, Layout)
), class = c("gg", "ggplot"))
p$labels <- make_labels(mapping)
set_last_plot(p)
p
}
#' @export
ggplot.function <- function(data = NULL, mapping = aes(), ...,
environment = parent.frame()) {
# Added to avoid functions end in ggplot.default
cli::cli_abort(c(
"{.arg data} cannot be a function.",
"i" = "Have you misspelled the {.arg data} argument in {.fn ggplot}"
))
}
plot_clone <- function(plot) {
p <- plot
p$scales <- plot$scales$clone()
p
}
#' Reports whether x is a ggplot object
#' @param x An object to test
#' @keywords internal
#' @export
is.ggplot <- function(x) inherits(x, "ggplot")
#' Explicitly draw plot
#'
#' Generally, you do not need to print or plot a ggplot2 plot explicitly: the
#' default top-level print method will do it for you. You will, however, need
#' to call `print()` explicitly if you want to draw a plot inside a
#' function or for loop.
#'
#' @param x plot to display
#' @param newpage draw new (empty) page first?
#' @param vp viewport to draw plot in
#' @param ... other arguments not used by this method
#' @keywords hplot
#' @return Invisibly returns the original plot.
#' @export
#' @method print ggplot
#' @examples
#' colours <- list(~class, ~drv, ~fl)
#'
#' # Doesn't seem to do anything!
#' for (colour in colours) {
#' ggplot(mpg, aes_(~ displ, ~ hwy, colour = colour)) +
#' geom_point()
#' }
#'
#' # Works when we explicitly print the plots
#' for (colour in colours) {
#' print(ggplot(mpg, aes_(~ displ, ~ hwy, colour = colour)) +
#' geom_point())
#' }
print.ggplot <- function(x, newpage = is.null(vp), vp = NULL, ...) {
set_last_plot(x)
if (newpage) grid.newpage()
# Record dependency on 'ggplot2' on the display list
# (AFTER grid.newpage())
grDevices::recordGraphics(
requireNamespace("ggplot2", quietly = TRUE),
list(),
getNamespace("ggplot2")
)
data <- ggplot_build(x)
gtable <- ggplot_gtable(data)
if (is.null(vp)) {
grid.draw(gtable)
} else {
if (is.character(vp)) seekViewport(vp) else pushViewport(vp)
grid.draw(gtable)
upViewport()
}
if (isTRUE(getOption("BrailleR.VI")) && rlang::is_installed("BrailleR")) {
print(asNamespace("BrailleR")$VI(x))
}
invisible(x)
}
#' @rdname print.ggplot
#' @method plot ggplot
#' @export
plot.ggplot <- print.ggplot
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.