#' @importFrom ggplot2 autoplot
#' @export
ggplot2::autoplot

new_mc_sum <- function(x, ...){
  tibble::new_tibble(x, subclass = "mc_sum")
}

autoplot.mc_sum <- function(object, ...){

  # defensive code to make sure people have ggplot2 installed
  if (!requireNamespace("ggplot2", quietly = TRUE)) {
    stop("ggplot2 is needed for this function to work. Install it via install.packages(\"ggplot2\")", call. = FALSE)
  }

  # check that the object is what you expect
  else if (!inherits(object, "mc_sum")) {
    stop("autoplot.mc_sum requires an mc_sum object, use object=object")
  }

  # plotting code
  ggplot2::ggplot(object,
                  ggplot2::aes(x = case,
                               y = n_miss)) +
    ggplot2::geom_col(width = 1,
                      colour = "#484878", # lorikeet purple
                      fill = "#484878") + # lorikeet purple
    ggplot2::coord_flip() +
    ggplot2::labs(y = "# Missing",
                  x = "Cases") +
    ggplot2::theme_minimal() +
    ggplot2::scale_x_reverse()

}

fit <- lm(Ozone ~ ., airquality)
fit
summary(fit)
summary(airquality)

class(fit)
class(airquality)


miss_case_summary(airquality)

autoplot(p1, type = "pairs")
autoplot(p1, type = "points")

miss_case_summary(airquality)

gg_miss_case(airquality)

miss_case_summary(airquality) %>%
  autoplot() +
  ggplot2::labs(title = "my title",
                x = "new_x",
                y = "new_y",
                subtitle = "my new subtitle")

##

detect_outliers(data) # returns data of class "stream_outlier"

detect_outliers(data) %>% autoplot(type = "ts")

detect_outliers(data) %>% extract_data_ts()

detect_outliers(data) %>% autoplot(type = "hd")

detect_outliers(data) %>% extract_data_hd()

#

new_odd_data <- function(x){
  structure(x,
            class = c("odd_water_data", class(x)))
}

detect_water_outlier <- function(x){

  # magical oddstream detection happens here

  # odd_data <- odd_water_stream(x)
  return(new_odd_data(odd_data))
}


autoplot.whatever_class

autoplot.odd_water_data <- function(object,
                                    type = "ts",
                                    title,
                                    ...){

  if (type == "ts") {
    # extract_data_ts is a function to get the data out of the plot.
    extract_data_ts(object) %>%
      ggplot2::ggplot(aes(x)) +
      ggplot2::labs(title = title)
      # the rest of your ggplot2 code

  }

  if (type == "hd") {

  }

}


detect_outliers(data)