Add function hai_density_qq_plot()

[spsanderson]

hai_density_qq_plot <- function(.data, .dist_name_col, .x_col, .y_col,
                                .size = 1, .alpha = 0.382, .interactive = FALSE){
  
  # Tidyeval ----
  dist_name_var <- rlang::enquo(.dist_name_col)
  x_col_var     <- rlang::enquo(.x_col)
  y_col_var     <- rlang::enquo(.y_col)
  size <- as.numeric(.size)
  alpha <- as.numeric(.alpha)
  
  dnv_name <- rlang::quo_name(dist_name_var)
  xcv_name <- rlang::quo_name(x_col_var)
  ycv_name <- rlang::quo_name(y_col_var)
  
  # Checks ----
  if(rlang::quo_is_missing(dist_name_var) | 
     rlang::quo_is_missing(x_col_var) |
     rlang::quo_is_missing(y_col_var)
  ){
    rlang::abort(
    "All parameters must be supplied:
       * .dist_name_col, 
       * .x_col, and
       * .y_col"
    )
  }
  
  if(!is.numeric(alpha) | (alpha > 1) | (alpha < 0)){
    rlang::abort("The .alpha parameter must be a number between 0 and 1")
  }
  
  if(!is.numeric(size) | (size <= 0)){
    rlang::abort("The .size parameter must be a number greater than 0")
  }
  
  # Data setup ----
  data_tbl <- tibble::as_tibble(.data) %>%
    dplyr::ungroup() %>%
    dplyr::select(
      {{dist_name_var}},
      {{x_col_var}},
      {{y_col_var}}
    )
  
  # Plots ----
  plt <- ggplot2::ggplot(
    data = data_tbl,
    mapping = ggplot2::aes_string(
      sample = ycv_name,
      color  = dnv_name,
      group  = dnv_name
    )
  ) +
    ggplot2::stat_qq(size = size, alpha = alpha) +
    ggplot2::stat_qq_line() +
    ggplot2::theme_minimal() +
    ggplot2::labs(
      title = "QQ Plot Comparison",
      color = "Distribution"
    ) +
    ggplot2::theme(legend.position = "bottom")
  
  if(.interactive){
    plt <- plotly::ggplotly(plt)
  }
  
  # Return ----
  return(plt)
}