geom-point.r

#' Points
#'
#' The point geom is used to create scatterplots. The scatterplot is most
#' useful for displaying the relationship between two continuous variables.
#' It can be used to compare one continuous and one categorical variable, or
#' two categorical variables, but a variation like [geom_jitter()],
#' [geom_count()], or [geom_bin2d()] is usually more
#' appropriate. A _bubblechart_ is a scatterplot with a third variable
#' mapped to the size of points.
#'
#' @section Overplotting:
#' The biggest potential problem with a scatterplot is overplotting: whenever
#' you have more than a few points, points may be plotted on top of one
#' another. This can severely distort the visual appearance of the plot.
#' There is no one solution to this problem, but there are some techniques
#' that can help. You can add additional information with
#' [geom_smooth()], [geom_quantile()] or
#' [geom_density_2d()]. If you have few unique `x` values,
#' [geom_boxplot()] may also be useful.
#'
#' Alternatively, you can
#' summarise the number of points at each location and display that in some
#' way, using [geom_count()], [geom_hex()], or
#' [geom_density2d()].
#'
#' Another technique is to make the points transparent (e.g.
#' `geom_point(alpha = 0.05)`) or very small (e.g.
#' `geom_point(shape = ".")`).
#'
#' @eval rd_aesthetics("geom", "point")
#' @inheritParams layer
#' @param na.rm If `FALSE`, the default, missing values are removed with
#'   a warning. If `TRUE`, missing values are silently removed.
#' @param ... Other arguments passed on to [layer()]. These are
#'   often aesthetics, used to set an aesthetic to a fixed value, like
#'   `colour = "red"` or `size = 3`. They may also be parameters
#'   to the paired geom/stat.
#' @export
#' @examples
#' p <- ggplot(mtcars, aes(wt, mpg))
#' p + geom_point()
#'
#' # Add aesthetic mappings
#' p + geom_point(aes(colour = factor(cyl)))
#' p + geom_point(aes(shape = factor(cyl)))
#' # A "bubblechart":
#' p + geom_point(aes(size = qsec))
#'
#' # Set aesthetics to fixed value
#' ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)
#'
#' \donttest{
#' # Varying alpha is useful for large datasets
#' d <- ggplot(diamonds, aes(carat, price))
#' d + geom_point(alpha = 1/10)
#' d + geom_point(alpha = 1/20)
#' d + geom_point(alpha = 1/100)
#' }
#'
#' # For shapes that have a border (like 21), you can colour the inside and
#' # outside separately. Use the stroke aesthetic to modify the width of the
#' # border
#' ggplot(mtcars, aes(wt, mpg)) +
#'   geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)
#'
#' \donttest{
#' # You can create interesting shapes by layering multiple points of
#' # different sizes
#' p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
#' p + geom_point(aes(colour = factor(cyl)), size = 4) +
#'   geom_point(colour = "grey90", size = 1.5)
#' p + geom_point(colour = "black", size = 4.5) +
#'   geom_point(colour = "pink", size = 4) +
#'   geom_point(aes(shape = factor(cyl)))
#'
#' # geom_point warns when missing values have been dropped from the data set
#' # and not plotted, you can turn this off by setting na.rm = TRUE
#' mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
#' ggplot(mtcars2, aes(wt, mpg)) + geom_point()
#' ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE)
#' }
geom_point <- function(mapping = NULL, data = NULL,
                       stat = "identity", position = "identity",
                       ...,
                       na.rm = FALSE,
                       show.legend = NA,
                       inherit.aes = TRUE) {
  layer(
    data = data,
    mapping = mapping,
    stat = stat,
    geom = GeomPoint,
    position = position,
    show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      na.rm = na.rm,
      ...
    )
  )
}

#' @rdname ggplot2-ggproto
#' @format NULL
#' @usage NULL
#' @export
GeomPoint <- ggproto("GeomPoint", Geom,
  required_aes = c("x", "y"),
  non_missing_aes = c("size", "shape", "colour"),
  default_aes = aes(
    shape = 19, colour = "black", size = 1.5, fill = NA,
    alpha = NA, stroke = 0.5
  ),

  draw_panel = function(data, panel_params, coord, na.rm = FALSE) {
    if (is.character(data$shape)) {
      data$shape <- translate_shape_string(data$shape)
    }

    coords <- coord$transform(data, panel_params)
    ggname("geom_point",
      pointsGrob(
        coords$x, coords$y,
        pch = coords$shape,
        gp = gpar(
          col = alpha(coords$colour, coords$alpha),
          fill = alpha(coords$fill, coords$alpha),
          # Stroke is added around the outside of the point
          fontsize = coords$size * .pt + coords$stroke * .stroke / 2,
          lwd = coords$stroke * .stroke / 2
        )
      )
    )
  },

  draw_key = draw_key_point
)

translate_shape_string <- function(shape_string) {
  # strings of length 0 or 1 are interpreted as symbols by grid
  if (nchar(shape_string[1]) <= 1) {
    return(shape_string)
  }

  pch_table <- c(
    "square open"           = 0,
    "circle open"           = 1,
    "triangle open"         = 2,
    "plus"                  = 3,
    "cross"                 = 4,
    "diamond open"          = 5,
    "triangle down open"    = 6,
    "square cross"          = 7,
    "asterisk"              = 8,
    "diamond plus"          = 9,
    "circle plus"           = 10,
    "star"                  = 11,
    "square plus"           = 12,
    "circle cross"          = 13,
    "square triangle"       = 14,
    "triangle square"       = 14,
    "square"                = 15,
    "circle small"          = 16,
    "triangle"              = 17,
    "diamond"               = 18,
    "circle"                = 19,
    "bullet"                = 20,
    "circle filled"         = 21,
    "square filled"         = 22,
    "diamond filled"        = 23,
    "triangle filled"       = 24,
    "triangle down filled"  = 25
  )

  shape_match <- charmatch(shape_string, names(pch_table))

  invalid_strings <- is.na(shape_match)
  nonunique_strings <- shape_match == 0

  if (any(invalid_strings)) {
    bad_string <- unique(shape_string[invalid_strings])
    n_bad <- length(bad_string)

    collapsed_names <- sprintf("\n* '%s'", bad_string[1:min(5, n_bad)])

    more_problems <- if (n_bad > 5) {
      sprintf("\n* ... and %d more problem%s", n_bad - 5, ifelse(n_bad > 6, "s", ""))
    }

    stop(
      "Can't find shape name:",
      collapsed_names,
      more_problems,
      call. = FALSE
    )
  }

  if (any(nonunique_strings)) {
    bad_string <- unique(shape_string[nonunique_strings])
    n_bad <- length(bad_string)

    n_matches <- vapply(
      bad_string[1:min(5, n_bad)],
      function(shape_string) sum(grepl(paste0("^", shape_string), names(pch_table))),
      integer(1)
    )

    collapsed_names <- sprintf(
      "\n* '%s' partially matches %d shape names",
      bad_string[1:min(5, n_bad)], n_matches
    )

    more_problems <- if (n_bad > 5) {
      sprintf("\n* ... and %d more problem%s", n_bad - 5, ifelse(n_bad > 6, "s", ""))
    }

    stop(
      "Shape names must be unambiguous:",
      collapsed_names,
      more_problems,
      call. = FALSE
    )
  }

  unname(pch_table[shape_match])
}