R/autoplot-combined-dist.R

In TidyDensity: Functions for Tidy Analysis and Generation of Random Data

#' Automatic Plot of Combined Multi Dist Data
#'
#' @family Autoplot
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will spit out one of the following plots:
#' -  `density`
#' -  `quantile`
#' -  `probability`
#' -  `qq`
#'
#' @description This is an auto plotting function that will take in a `tidy_`
#' distribution function and a few arguments, one being the plot type, which is
#' a quoted string of one of the following:
#' -  `density`
#' -  `quantile`
#' -  `probablity`
#' -  `qq`
#'
#' If the number of simulations exceeds 9 then the legend will not print. The plot
#' subtitle is put together by the attributes of the table passed to the function.
#'
#' @param .data The data passed in from a the function `tidy_multi_dist()`
#' @param .plot_type This is a quoted string like 'density'
#' @param .line_size The size param ggplot
#' @param .geom_point A Boolean value of TREU/FALSE, FALSE is the default. TRUE
#' will return a plot with `ggplot2::ggeom_point()`
#' @param .point_size The point size param for ggplot
#' @param .geom_rug A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
#' will return the use of `ggplot2::geom_rug()`
#' @param .geom_smooth A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
#' will return the use of `ggplot2::geom_smooth()` The `aes` parameter of group is
#' set to FALSE. This ensures a single smoothing band returned with SE also set to
#' FALSE. Color is set to 'black' and `linetype` is 'dashed'.
#' @param .geom_jitter A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
#' will return the use of `ggplot2::geom_jitter()`
#' @param .interactive A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
#' will return an interactive `plotly` plot.
#'
#' @examples
#' combined_tbl <- tidy_combine_distributions(
#'   tidy_normal(),
#'   tidy_gamma(),
#'   tidy_beta()
#' )
#'
#' combined_tbl
#'
#' combined_tbl %>%
#'   tidy_combined_autoplot()
#'
#' combined_tbl %>%
#'   tidy_combined_autoplot(.plot_type = "qq")
#'
#' @return
#' A ggplot or a plotly plot.
#'
#' @export
#'

tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .5,
                                   .geom_point = FALSE, .point_size = 1,
                                   .geom_rug = FALSE, .geom_smooth = FALSE,
                                   .geom_jitter = FALSE, .interactive = FALSE) {

  # Plot type ----
  plot_type <- tolower(as.character(.plot_type))
  line_size <- as.numeric(.line_size)
  point_size <- as.numeric(.point_size)

  # Get the data attributes
  atb <- attributes(.data)
  ns <- atb$.param_list$.num_sims
  ps <- attributes(.data)$all$ps
  ps <- rep(ps, (ns * nrow(expand.grid(atb$.param_list))))
  qs <- attributes(.data)$all$qs
  qs <- rep(qs, (ns * nrow(expand.grid(atb$.param_list))))

  # Checks on data ---
  if (!is.data.frame(.data)) {
    rlang::abort("The .data parameter must be a valid data.frame from a `tidy_`
                     distribution function.  ")
  }

  if (!atb$tibble_type == "tidy_multi_dist_combine") {
    rlang::abort(
      message = "The data passed must come from the
            `tidy_combine_distributions()` function.",
      use_cli_format = TRUE
    )
  }

  if (!is.numeric(.line_size) | !is.numeric(.point_size) | .line_size < 0 | .point_size < 0) {
    rlang::abort(
      message = "The parameters .line_size and .point_size must be numeric and
                     greater than 0.",
      use_cli_format = TRUE
    )
  }

  if (!plot_type %in% c("density", "quantile", "probability", "qq", "mcmc")) {
    rlang::abort(
      message = "You have chose an unsupported plot type.",
      use_cli_format = TRUE
    )
  }

  # Data ----
  data_tbl <- dplyr::as_tibble(.data)

  # Data for ggplot
  n <- max(data_tbl$x)
  sims <- max(as.numeric(data_tbl$sim_number))

  sub_title <- paste0(
    "Data Points: ", n, " - ",
    "Simulations: ", sims
  )

  # Plot logic ----
  leg_pos <- if (sims > 9) {
    "none"
  } else {
    "bottom"
  }

  if (plot_type == "density") {
    plt <- data_tbl %>%
      ggplot2::ggplot(
        ggplot2::aes(
          x = dx, y = dy,
          group = interaction(dist_type, sim_number),
          color = dist_type
        )
      ) +
      ggplot2::geom_line(size = line_size) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = "Density Plot",
        subtitle = sub_title,
        color = "Simulation"
      ) +
      ggplot2::theme(legend.position = leg_pos)
  } else if (plot_type == "quantile") {
    ## EDIT
    data_tbl <- data_tbl %>%
      dplyr::select(sim_number, dist_type, q) %>%
      dplyr::group_by(sim_number, dist_type) %>%
      dplyr::arrange(q) %>%
      dplyr::mutate(x = 1:dplyr::n() %>%
        tidy_scale_zero_one_vec()) %>%
      dplyr::ungroup()
    ## End EDIT
    plt <- data_tbl %>%
      dplyr::filter(q > -Inf, q < Inf) %>%
      ggplot2::ggplot(
        ggplot2::aes(
          # x = tidy_scale_zero_one_vec(dx),
          x = x,
          y = tidy_scale_zero_one_vec(q),
          group = interaction(dist_type, sim_number),
          color = dist_type
        )
      ) +
      ggplot2::geom_line(size = line_size) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = "Quantile Plot",
        subtitle = sub_title,
        x = "",
        y = "",
        color = "Simulation"
      ) +
      ggplot2::theme(legend.position = leg_pos)
  } else if (plot_type == "probability") {
    plt <- data_tbl %>%
      ggplot2::ggplot(
        ggplot2::aes(
          x = y,
          group = interaction(dist_type, sim_number),
          color = dist_type
        )
      ) +
      ggplot2::stat_ecdf(size = line_size) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = "Probability Plot",
        subtitle = sub_title,
        color = "Simulation",
        x = "dx"
      ) +
      ggplot2::theme(legend.position = leg_pos)
  } else if (plot_type == "qq") {
    plt <- data_tbl %>%
      ggplot2::ggplot(
        ggplot2::aes(
          sample = y,
          group = interaction(dist_type, sim_number),
          color = dist_type
        )
      ) +
      ggplot2::stat_qq(size = point_size) +
      ggplot2::stat_qq_line(size = line_size) +
      ggplot2::theme_minimal() +
      ggplot2::labs(
        title = "QQ Plot",
        subtitle = sub_title,
        color = "Simulation"
      ) +
      ggplot2::theme(legend.position = leg_pos)
  } else if (plot_type == "mcmc") {
    plt <- data_tbl %>%
      dplyr::group_by(sim_number) %>%
      dplyr::mutate(cmy = dplyr::cummean(y)) %>%
      dplyr::ungroup() %>%
      ggplot2::ggplot(ggplot2::aes(
        x = x, y = cmy, group = sim_number, color = sim_number
      )) +
      ggplot2::geom_line() +
      ggplot2::theme_minimal() +
      ggplot2::scale_x_continuous(trans = "log10") +
      ggplot2::labs(
        title = "MCMC Cumulative Mean Plot",
        caption = "X is on log10 scale.",
        subtitle = sub_title,
        color = "Simulation",
        x = "",
        y = ""
      ) +
      ggplot2::theme(legend.position = leg_pos)
  }

  if (.geom_rug) {
    plt <- plt +
      ggplot2::geom_rug()
  }

  if ((.geom_point) & (!plot_type == "qq")) {
    plt <- plt +
      ggplot2::geom_point(size = point_size)
  }

  if (.geom_smooth & !plot_type == "mcmc") {
    max_dy <- max(data_tbl$dy)

    plt <- plt +
      ggplot2::geom_smooth(
        ggplot2::aes(
          group = FALSE
        ),
        se = FALSE,
        color = "black",
        linetype = "dashed"
      ) +
      ggplot2::ylim(0, max_dy)
  } else if (.geom_smooth & plot_type == "mcmc") {
    plt <- plt +
      ggplot2::geom_smooth(
        ggplot2::aes(
          group = FALSE
        ),
        se = FALSE,
        color = "black",
        linetype = "dashed"
      )
  }

  if (.geom_jitter) {
    plt <- plt +
      ggplot2::geom_jitter()
  }

  if (.interactive) {
    plt <- plotly::ggplotly(plt)
  }

  # Return ----
  return(plt)
}