R/autoplot-randomwalk.R

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

#' Automatic Plot of Random Walk Data
#'
#' @family Autoplot
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @details This function will produce a simple random walk plot from a `tidy_`
#' distribution function.
#'
#' @description This is an auto-plotting function that will take in a `tidy_`
#' distribution function and a few arguments with regard to the output of the
#' visualization.
#'
#' 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 tidy_`distribution` function like
#' `tidy_normal()`
#' @param .line_size The size param 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 .interactive A Boolean value of TRUE/FALSE, FALSE is the default. TRUE
#' will return an interactive `plotly` plot.
#'
#' @examples
#' tidy_normal(.sd = .1, .num_sims = 5) |>
#'   tidy_random_walk(.value_type = "cum_sum") |>
#'   tidy_random_walk_autoplot()
#'
#' tidy_normal(.sd = .1, .num_sims = 20) |>
#'   tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |>
#'   tidy_random_walk_autoplot()
#'
#' @return
#' A ggplot or a plotly plot.
#'
#' @export
#'

tidy_random_walk_autoplot <- function(.data, .line_size = .5, .geom_rug = FALSE,
                                      .geom_smooth = FALSE, .interactive = FALSE) {

  # Tidyeval ----
  line_size <- as.numeric(.line_size)

  # Get the data attributes for plot title/subtitle ---
  atb <- attributes(.data)
  n <- atb$all$.n
  sims <- atb$all$.num_sims
  # dist_type <- stringr::str_remove(atb$all$tibble_type, "tidy_") |>
  #   stringr::str_replace_all(pattern = "_", " ") |>
  #   stringr::str_to_title()
  dist_type <- dist_type_extractor(atb$all$tibble_type)

  sub_title <- paste0(
    "Data Points: ", n, " - ",
    "Simulations: ", sims, "\n",
    "Distribution Family: ", dist_type, "\n",
    "Parameters: ", if (atb$all$tibble_type == "tidy_gaussian") {
      paste0("Mean: ", atb$all$.mean, " - SD: ", atb$all$.sd)
    } else if (atb$all$tibble_type == "tidy_gamma") {
      paste0("Shape: ", atb$all$.shape, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type == "tidy_beta") {
      paste0("Shape1: ", atb$all$.shape1, " - Shape2: ", atb$all$.shape2, " - NCP: ", atb$all$.ncp)
    } else if (atb$all$tibble_type %in% c("tidy_poisson", "tidy_zero_truncated_poisson")) {
      paste0("Lambda: ", atb$all$.lambda)
    } else if (atb$all$tibble_type == "tidy_f") {
      paste0("DF1: ", atb$all$.df1, " - DF2: ", atb$all$.df2, " - NCP: ", atb$all$.ncp)
    } else if (atb$all$tibble_type == "tidy_hypergeometric") {
      paste0("M: ", atb$all$.m, " - NN: ", atb$all$.nn, " - K: ", atb$all$.k)
    } else if (atb$all$tibble_type == "tidy_lognormal") {
      paste0("Mean Log: ", atb$all$.meanlog, " - SD Log: ", atb$all$.sdlog)
    } else if (atb$all$tibble_type == "tidy_cauchy") {
      paste0("Location: ", atb$all$.location, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type %in% c("tidy_chisquare", "tidy_t")) {
      paste0("DF: ", atb$all$.df, " - NPC: ", atb$all$.ncp)
    } else if (atb$all$tibble_type == "tidy_weibull") {
      paste0("Shape: ", atb$all$.schape, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type == "tidy_uniform") {
      paste0("Max: ", atb$all$.max, " - Min: ", atb$all$.min)
    } else if (atb$all$tibble_type == "tidy_logistic") {
      paste0("Location: ", atb$all$.location, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type == "tidy_exponential") {
      paste0("Rate: ", atb$all$.rate)
    } else if (atb$all$tibble_type == "tidy_empirical") {
      paste0("Empirical - No params")
    } else if (atb$all$tibble_type %in% c(
      "tidy_binomial", "tidy_negative_binomial",
      "tidy_zero_truncated_binomial",
      "tidy_zero_truncated_negative_binomial"
    )) {
      paste0("Size: ", atb$all$.size, " - Prob: ", atb$all$.prob)
    } else if (atb$all$tibble_type %in% c("tidy_geometric", "tidy_zero_truncated_geometric",
                                          "tidy_bernoulli")) {
      paste0("Prob: ", atb$all$.prob)
    } else if (atb$all$tibble_type %in% c("tidy_pareto_single_parameter")) {
      paste0("Shape: ", atb$all$.shape, " - Min: ", atb$all$.min)
    } else if (atb$all$tibble_type %in% c("tidy_pareto", "tidy_inverse_pareto")) {
      paste0("Shape: ", atb$all$.shape, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type %in% c(
      "tidy_generalized_pareto",
      "tidy_burr", "tidy_inverse_burr"
    )) {
      paste0(
        "Shape1: ", atb$all$.shape1, " - ",
        "Shape2: ", atb$all$.shape2, " - ",
        "Rate: ", atb$all$.rate, " - ",
        "Scale: ", atb$all$.scale
      )
    } else if (atb$all$tibble_type %in% c(
      "tidy_paralogistic",
      "tidy_inverse_gamma",
      "tidy_inverse_weibull"
    )
    ) {
      paste0(
        "Shape: ", atb$all$.shape, " - ",
        "Rate: ", atb$all$.rate, " - ",
        "Scale: ", atb$all$.scale
      )
    } else if (atb$all$tibble_type == "tidy_inverse_exponential") {
      paste0("Rate: ", atb$all$.rate, " - Scale: ", atb$all$.scale)
    } else if (atb$all$tibble_type == "tidy_inverse_gaussian") {
      paste0(
        "Mean: ", atb$all$.mean, " - ",
        "Shape: ", atb$all$.shape, " - ",
        "Dispersion: ", atb$all$.dispersion
      )
    } else if (atb$all$tibble_type == "tidy_generalized_beta") {
      paste0(
        "Shape1: ", atb$all$.shape1, " - ",
        "Shape2: ", atb$all$.shape2, " - ",
        "Shape3: ", atb$all$.shape3, " - ",
        "Scale: ", atb$all$.scale, " - ",
        "Rate: ", atb$all$.rate
      )
    } else if (atb$tibble_type == "tidy_triangular") {
      paste0(
        "Min: ", atb$.min, " - ",
        "Max: ", atb$.max, " - ",
        "Mode: ", atb$.mode
      )
    }
  )

  # Plot Legend logic ----
  leg_pos <- if (atb$all$tibble_type == "tidy_empirical") {
    "none"
  } else if (sims > 9) {
    "none"
  } else {
    "bottom"
  }

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

  # Plot ----
  plt <- data_tbl |>
    ggplot2::ggplot(ggplot2::aes(
      x = x, y = random_walk_value,
      group = sim_number, color = sim_number
    )) +
    ggplot2::geom_line(linewidth = line_size) +
    ggplot2::theme_minimal() +
    ggplot2::labs(
      title = "Random Walk Plot",
      subtitle = sub_title,
      color = "Simulation",
      x = "Time",
      y = "Value"
    ) +
    ggplot2::theme(legend.position = leg_pos)

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

  if (.geom_smooth) {
    plt <- plt +
      ggplot2::geom_smooth(
        ggplot2::aes(
          group = FALSE
        ),
        se = FALSE,
        color = "black",
        linetype = "dashed"
      )
  }

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

  # Return ----
  return(plt)
}