R/geo_level_plot.R

In CT-Data-Haven/cwi: Prep for DataHaven's Community Wellbeing Index

#' Quickly make a `ggplot` to view data by geographic level
#'
#' This is a quick way to make a bar chart, a Cleveland dotplot, or a histogram from a set of data, filled by geographic level.
#' @param data A data frame to use for plotting.
#' @param name Bare column name containing names, i.e. independent variable.
#' @param value Bare column name containing values, i.e. dependent variable.
#' @param level Bare column name containing geographic levels for fill.
#' @param type String: one of `"col"` (bar chart), `"point"` (dot plot), or `"hist"` (histogram); defaults `"col"`.
#' @param hilite String giving the highlight color, used for the lowest geography present.
#' @param title String giving the title, if desired, for the plot.
#' @param dark_gray String giving the named gray color for the highest geography; defaults `"gray20"`.
#' @param light_gray String giving the named gray color for the second lowest geography; defaults `"gray60"`.
#' @param ... Any additional parameters to pass to the underlying geom function.
#' @return A ggplot
#' @export
geo_level_plot <- function(data, name = name, value = value, level = level, type = c("col", "hist", "point"), hilite = "dodgerblue", title = NULL, dark_gray = "gray20", light_gray = "gray60", ...) {
  # type can be column (col), histogram (hist), or point (point)
  type <- rlang::arg_match(type)
  if (!grepl("gr(a|e)y", dark_gray)) dark_gray <- "gray20"
  if (!grepl("gr(a|e)y", light_gray)) light_gray <- "gray60"

  geos <- sort(unique(data[[rlang::as_label(rlang::enquo(level))]]))

  g1 <- as.numeric(stringr::str_extract(dark_gray, "\\d+$") )
  g2 <- as.numeric(stringr::str_extract(light_gray, "\\d+$"))
  pal1 <- round(seq(g1, g2, length.out = length(geos) - 1))
  pal <- c(paste0("gray", pal1), hilite)

  data <- dplyr::ungroup(data)
  data <- dplyr::mutate(data, dplyr::across({{ name }}, ~forcats::fct_reorder(.x, {{ value }})))

  p <- ggplot2::ggplot(data, ggplot2::aes(fill = {{ level }}))
  p <- p + ggplot2::scale_fill_manual(values = pal)
  p <- p + ggplot2::theme_minimal()

  if (type == "hist") {
    p <- p + ggplot2::geom_histogram(ggplot2::aes(x = {{ value }}), ...)
  } else if (type == "point") {
    p <- p + ggplot2::geom_point(ggplot2::aes(x = {{ name }}, y = {{ value }}), stroke = 0, size = 4, shape = 21, ...)
    p <- p + ggplot2::coord_flip()
  } else {
    p <- p + ggplot2::geom_col(ggplot2::aes(x = {{ name }}, y = {{ value }}), width = 0.8, ...)
    p <- p + ggplot2::coord_flip()
    p <- p + ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = c(0, 0.05)))
  }
  if (!is.null(title)) {
    p <- p + ggplot2::ggtitle(title)
  }
  p
}