R/plot_templates.R

In InteragencyEcologicalProgram/smonitr: IEP Seasonal Monitoring Report Tools

#' Column plot template for IEP Seasonal Monitoring Report figures
#'
#' A template function for producing column plots for the IEP
#' Seasonal Monitoring Report.
#'
#' @inheritParams ggplot2::ggplot
#' @inheritParams smr_x_axis
#' @param nudge.factor Vertical nudge of missing data symbols,
#'   as a fraction of the data range. Default is 0.015 (1.5%).
#' @param ... other arguments to pass to [`geom_col()`]
#'
#' @examples
#' test = data.frame(year = 2004:2018, catch = c(sample(100:500, 14), NA))
#' standard_column_plot(test, aes(x = year, y = catch), 2018)
#'
#' @import rlang
#' @importFrom purrr map_chr
#' @import ggplot2
#' @export
standard_column_plot = function(data, mapping, report_year,
  season = c("annual", "winter", "spring", "summer", "fall"),
  type = c("all", "recent"), nudge.factor = 0.015, ...) {
  # define the x-axis
  xaxis = smr_x_axis(report_year = report_year, type = type,
    season = season)
  # check that the columns specified in mapping exist in data
  if (!all(map_chr(unname(mapping), as_name) %in% names(data))) {
    stop("Argument \"data\" does not contain columns: ",
      paste(shQuote(setdiff(map_chr(unname(mapping), as_name),
        names(data))), collapse = ", "))
  }
  # column type checking
  for (col in mapping[c("x", "y")]) {
    if (!is.numeric(data[[as_name(col)]])) {
      stop("Column ", shQuote(as_name(col)), " must be numeric.")
    }
  }
  # convert year to integer
  data[as_name(mapping$x)] = as.integer(data[[as_name(mapping$x)]])
  # calculate nudge distance for missing data
#  nudge.range = data[[as_name(mapping$x)]] >= xaxis$limits[1] &
 #   data[[as_name(mapping$x)]] <= xaxis$limits[2]
  nudge = diff(range(data[[as_name(mapping$y)]], na.rm = TRUE,
    finite = TRUE)) * nudge.factor

  # construct the plot
  ggplot(data, mapping) +
    smr_theme() +
    xaxis +
    smr_y_axis() +
    geom_col(...) +
    stat_missing(nudge_y = nudge) +
    stat_lt_avg()
}