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 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 `#' Box and whiskers plot.#'#' The upper and lower "hinges" correspond to the first and third quartiles#' (the 25th and 7th percentiles). This differs slightly from the method used#' by the \code{boxplot} function, and may be apparent with small samples.#' See \code{\link{boxplot.stats}} for for more information on how hinge#' positions are calculated for \code{boxplot}.#'#' The upper whisker extends from the hinge to the highest value that is within#' 1.5 * IQR of the hinge, where IQR is the inter-quartile range, or distance#' between the first and third quartiles. The lower whisker extends from the#' hinge to the lowest value within 1.5 * IQR of the hinge. Data beyond the#' end of the whiskers are outliers and plotted as points (as specified by Tukey).#'#' In a notched box plot, the notches extend \code{1.58 * IQR / sqrt(n)}.#' This gives a roughly 95% confidence interval for comparing medians.#' See McGill et al. (1978) for more details.#'#' @seealso \code{\link{stat_quantile}} to view quantiles conditioned on a#' continuous variable, \code{\link{geom_jitter}} for another way to look #' at conditional distributions"#' @inheritParams geom_point#' @param outlier.colour colour for outlying points#' @param outlier.shape shape of outlying points#' @param outlier.size size of outlying points#' @param notch if \code{FALSE} (default) make a standard box plot. If#' \code{TRUE}, make a notched box plot. Notches are used to compare groups;#' if the notches of two boxes do not overlap, this is strong evidence that #' the medians differ.#' @param notchwidth for a notched box plot, width of the notch relative to#' the body (default 0.5)#' @export#'#' @references McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of#' box plots. The American Statistician 32, 12-16.#'#' @examples#' \donttest{#' p <- ggplot(mtcars, aes(factor(cyl), mpg))#' #' p + geom_boxplot()#' qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot")#' #' p + geom_boxplot() + geom_jitter()#' p + geom_boxplot() + coord_flip()#' qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot") +#' coord_flip()#'#' p + geom_boxplot(notch = TRUE)#' p + geom_boxplot(notch = TRUE, notchwidth = .3)#' #' p + geom_boxplot(outlier.colour = "green", outlier.size = 3)#' #' # Add aesthetic mappings#' # Note that boxplots are automatically dodged when any aesthetic is #' # a factor#' p + geom_boxplot(aes(fill = cyl))#' p + geom_boxplot(aes(fill = factor(cyl)))#' p + geom_boxplot(aes(fill = factor(vs)))#' p + geom_boxplot(aes(fill = factor(am)))#' #' # Set aesthetics to fixed value#' p + geom_boxplot(fill = "grey80", colour = "#3366FF")#' qplot(factor(cyl), mpg, data = mtcars, geom = "boxplot", #' colour = I("#3366FF"))#' #' # Scales vs. coordinate transforms -------#' # Scale transformations occur before the boxplot statistics are computed.#' # Coordinate transformations occur afterwards. Observe the effect on the#' # number of outliers.#' library(plyr) # to access round_any#' m <- ggplot(movies, aes(y = votes, x = rating,#' group = round_any(rating, 0.5)))#' m + geom_boxplot()#' m + geom_boxplot() + scale_y_log10()#' m + geom_boxplot() + coord_trans(y = "log10")#' m + geom_boxplot() + scale_y_log10() + coord_trans(y = "log10")#' #' # Boxplots with continuous x:#' # Use the group aesthetic to group observations in boxplots#' qplot(year, budget, data = movies, geom = "boxplot")#' qplot(year, budget, data = movies, geom = "boxplot", #' group = round_any(year, 10, floor))#'#' # Using precomputed statistics#' # generate sample data#' abc <- adply(matrix(rnorm(100), ncol = 5), 2, quantile, c(0, .25, .5, .75, 1))#' b <- ggplot(abc, aes(x = X1, ymin = `0%`, lower = `25%`, middle = `50%`, upper = `75%`, ymax = `100%`)) #' b + geom_boxplot(stat = "identity")#' b + geom_boxplot(stat = "identity") + coord_flip()#' b + geom_boxplot(aes(fill = X1), stat = "identity")#' }geom_boxplot <- function (mapping = NULL, data = NULL, stat = "boxplot", position = "dodge", outlier.colour = "black", outlier.shape = 16, outlier.size = 2,notch = FALSE, notchwidth = .5, ...) {  GeomBoxplot\$new(mapping = mapping, data = data, stat = stat,   position = position, outlier.colour = outlier.colour, outlier.shape = outlier.shape,   outlier.size = outlier.size, notch = notch, notchwidth = notchwidth, ...)}GeomBoxplot <- proto(Geom, {  objname <- "boxplot"  reparameterise <- function(., df, params) {    df\$width <- df\$width %||%       params\$width %||% (resolution(df\$x, FALSE) * 0.9)    if (!is.null(df\$outliers)) {      suppressWarnings({        out_min <- vapply(df\$outliers, min, numeric(1))        out_max <- vapply(df\$outliers, max, numeric(1))      })            df\$ymin_final <- pmin(out_min, df\$ymin)      df\$ymax_final <- pmax(out_max, df\$ymax)    }         transform(df,      xmin = x - width / 2, xmax = x + width / 2, width = NULL    )  }    draw <- function(., data, ..., fatten = 2, outlier.colour = NULL, outlier.shape = NULL, outlier.size = 2,                   notch = FALSE, notchwidth = .5) {     common <- data.frame(      colour = data\$colour,       size = data\$size,       linetype = data\$linetype,      fill = alpha(data\$fill, data\$alpha),       group = NA,       stringsAsFactors = FALSE    )    whiskers <- data.frame(      x = data\$x,      xend = data\$x,       y = c(data\$upper, data\$lower),       yend = c(data\$ymax, data\$ymin),      alpha = NA,      common)    box <- data.frame(      xmin = data\$xmin,       xmax = data\$xmax,       ymin = data\$lower,       y = data\$middle,       ymax = data\$upper,      ynotchlower = ifelse(notch, data\$notchlower, NA),      ynotchupper = ifelse(notch, data\$notchupper, NA),      notchwidth = notchwidth,      alpha = data\$alpha,       common)        if (!is.null(data\$outliers) && length(data\$outliers[[1]] >= 1)) {      outliers <- data.frame(        y = data\$outliers[[1]],        x = data\$x[1],        colour = outlier.colour %||% data\$colour[1],        shape = outlier.shape %||% data\$shape[1],        size = outlier.size %||% data\$size[1],        fill = NA,        alpha = NA,        stringsAsFactors = FALSE)      outliers_grob <- GeomPoint\$draw(outliers, ...)    } else {      outliers_grob <- NULL    }        ggname(.\$my_name(), grobTree(      outliers_grob,      GeomSegment\$draw(whiskers, ...),      GeomCrossbar\$draw(box, fatten = fatten, ...)    ))  }  guide_geom <- function(.) "boxplot"   draw_legend <- function(., data, ...) {    data <- aesdefaults(data, .\$default_aes(), list(...))    gp <- with(data, gpar(col=colour, fill=alpha(fill, alpha), lwd=size * .pt))    gTree(gp = gp, children = gList(      linesGrob(0.5, c(0.1, 0.25)),      linesGrob(0.5, c(0.75, 0.9)),      rectGrob(height=0.5, width=0.75),      linesGrob(c(0.125, 0.875), 0.5)    ))  }  icon <- function(.) {    gTree(children=gList(      segmentsGrob(c(0.3, 0.7), c(0.1, 0.2), c(0.3, 0.7), c(0.7, 0.95)),      rectGrob(c(0.3, 0.7), c(0.6, 0.8), width=0.3, height=c(0.4, 0.4), vjust=1),      segmentsGrob(c(0.15, 0.55), c(0.5, 0.6), c(0.45, 0.85), c(0.5, 0.6))    ))  }    default_stat <- function(.) StatBoxplot  default_pos <- function(.) PositionDodge  default_aes <- function(.) aes(weight=1, colour="grey20", fill="white", size=0.5, alpha = NA, shape = 16, linetype = "solid")  required_aes <- c("x", "lower", "upper", "middle", "ymin", "ymax")})`
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