# Cache the value of an expression on the file system.
#
# The expression will be evaluated if there is no matching cache file found.
# The cache files will be located in a directory "cache" located in the current
# working directory.
#
# @param name Human readable part of the cache file name.
# @param objects A vector of objects that this expression depends on. The hash
#   of those objects will be used for identifying the cache file.
cached <- function(name, objects, expr) {
  if (!dir.exists("cache")) {
    dir.create("cache")
  }

  id <- rlang::hash(objects)
  cache_file <- sprintf("cache/%s_%s.rda", name, id)

  if (file.exists(cache_file)) {
    # If the cache file exists, we restore the data from it.
    load(cache_file)
  } else {
    # If the cache file doesn't exist, we have to do the computation.
    data <- expr

    # The results are cached for the next run.
    save(data, file = cache_file, compress = "xz")
  }

  data
}

#' Format and round a numeric value.
#'
#' @param number The number to use.
#' @param digits Number of decimal places.
#'
#' @return A character value.
#' @noRd
num <- function(number, digits) {
  format(round(number, digits = digits), nsmall = digits)
}

#' Find the densest value in the data.
#'
#' This function assumes that data represents a continuous variable and finds
#' a single value with the highest estimated density. This can be used to
#' estimate the mode of the data. If there is only one value that value is
#' returned. If multiple density maxima with the same density exist, their mean
#' is returned.
#'
#' @param data The input data.
#'
#' @return The densest value of data.
#'
#' @export
densest <- function(data) {
  as.numeric(if (length(data) <= 0) {
    NULL
  } else if (length(data) == 1) {
    data
  } else {
    density <- stats::density(data)
    mean(density$x[density$y == max(density$y)])
  })
}

# This is needed to make data.table's symbols available within the package.
#' @import data.table
NULL