R/gg_boxplot_legend.R

In emilelatour/laviz: What the Package Does (One Line, Title Case)

#' @title
#' Legend for a box plot
#'
#' @description
#' A figure legend that explains the components and symbols for a box plot.
#'
#' @references
#' https://owi.usgs.gov/blog/boxplots/
#'
#' @param title The text for the title
#' @param family Font family; may need to load `extrafont` package first
#'
#' @import ggplot2
#' @importFrom stringr str_wrap
#'
#' @return
#' A ggplot object
#'
#' @export
#'
#' @examples
#' library(ggplot2)
#' ggplot_box_legend()
#'
ggplot_box_legend <- function(title = "Boxplot legend",
                              family = "") {

  # Create data to use in the boxplot legend:
  set.seed(100)

  sample_df <- data.frame(parameter = "test",
                          values = sample(500))

  # Extend the top whisker a bit:
  sample_df$values[1:100] <- 701:800
  # Make sure there's only 1 lower outlier:
  sample_df$values[1] <- -350

  # Function to calculate important values:
  ggplot2_boxplot <- function(x){

    quartiles <- as.numeric(quantile(x,
                                     probs = c(0.25, 0.5, 0.75)))

    names(quartiles) <- c("25th percentile",
                          "50th percentile\n(median)",
                          "75th percentile")

    IQR <- diff(quartiles[c(1,3)])

    upper_whisker <- max(x[x < (quartiles[3] + 1.5 * IQR)])
    lower_whisker <- min(x[x > (quartiles[1] - 1.5 * IQR)])

    upper_dots <- x[x > (quartiles[3] + 1.5 * IQR)]
    lower_dots <- x[x < (quartiles[1] - 1.5 * IQR)]

    return(list("quartiles" = quartiles,
                "25th percentile" = as.numeric(quartiles[1]),
                "50th percentile\n(median)" = as.numeric(quartiles[2]),
                "75th percentile" = as.numeric(quartiles[3]),
                "IQR" = IQR,
                "upper_whisker" = upper_whisker,
                "lower_whisker" = lower_whisker,
                "upper_dots" = upper_dots,
                "lower_dots" = lower_dots))
  }

  # Get those values:
  ggplot_output <- ggplot2_boxplot(sample_df$values)

  # Lots of text in the legend, make it smaller and consistent font:
  update_geom_defaults("text",
                       list(size = 3,
                            hjust = 0,
                            family = family))
  # Labels don't inherit text:
  update_geom_defaults("label",
                       list(size = 3,
                            hjust = 0,
                            family = family))

  # Create the legend:
  # The main elements of the plot (the boxplot, error bars, and count)
  # are the easy part.
  # The text describing each of those takes a lot of fiddling to
  # get the location and style just right:
  explain_plot <- ggplot() +
    stat_boxplot(data = sample_df,
                 aes(x = parameter,
                     y = values),
                 geom = "errorbar",
                 width = 0.225) +
    geom_boxplot(data = sample_df,
                 aes(x = parameter,
                     y = values),
                 width = 0.3,
                 fill = "lightgrey") +
    theme_minimal(base_size = 5,
                  base_family = family) +
    geom_segment(aes(x = 1.9,
                     xend = 1.9,
                     y = ggplot_output[["25th percentile"]],
                     yend = ggplot_output[["75th percentile"]])) +
    geom_segment(aes(x = 1.2,
                     xend = 1.9,
                     y = ggplot_output[["25th percentile"]],
                     yend = ggplot_output[["25th percentile"]])) +
    geom_segment(aes(x = 1.2,
                     xend = 1.9,
                     y = ggplot_output[["75th percentile"]],
                     yend = ggplot_output[["75th percentile"]])) +
    geom_text(aes(x = 2.0,
                  y = ggplot_output[["50th percentile\n(median)"]]),
              label = "Interquartile\nrange",
              fontface = "bold",
              vjust = 0.4) +
   geom_text(aes(x = c(1.17, 1.17),
                  y = c(ggplot_output[["upper_whisker"]],
                        ggplot_output[["lower_whisker"]]),
                  label = stringr::str_wrap(c("Largest value within 1.5 times interquartile range above 75th percentile",
                            "Smallest value within 1.5 times interquartile range below 25th percentile"),
                            width = 35)),
              fontface = "bold",
              vjust = 0.9) +
    geom_text(aes(x = c(1.17),
                  y =  ggplot_output[["lower_dots"]],
                  label = stringr::str_wrap("Potential outlier; value more than 1.5 times and less than 3 times the interquartile range beyond either end of the box",
                                            width = 35)),
              vjust = 0.85) +
    geom_label(aes(x = 1.17,
                   y = ggplot_output[["quartiles"]],
                   label = names(ggplot_output[["quartiles"]])),
               vjust = c(0.4, 0.85, 0.4),
               fill = "white",
               label.size = 0) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank(),
          panel.grid = element_blank(),
          aspect.ratio = 4/3,
          # plot.title = element_text(hjust = 0.5,
          #                           size = 10),
          plot.title = element_text(size = 10)
          ) +
    coord_cartesian(xlim = c(1.4, 3.1),
                    ylim = c(-600, 900)) +
    labs(title = title,
         x = NULL,
         y = NULL)

  return(explain_plot)

}