plotly_contour_dynamic_range

#' QT Outlier Explorer
#'
#' @param data ECG data structured as one record per person per visit per measurement. See details for column requirements.
#' @param settings named list of settings with the parameters specified below.
#'
#' @details The settings object provides details the columns in the data set.
#'
#' \itemize{
#'  \item{"id_col"}{ID column}
#'  \item{"value_col"}{Value column}
#'  \item{"measure_col"}{Measure column}
#'  \item{"measure_values"}{Measure values}
#'  \item{"visit_col"}{Visit column}
#'  \item{"visitn_col"}{Visit number column (numeric)}
#'  \item{"baseline_flag_col}{Baseline flag column}
#'  \item{"baseline_flag_values}{Baseline flag value}
#' }
#'
#'
#' @return returns a chart object
#'
#' @import plotly
#' @import rlang
#' @importFrom rlang .data
#' @import dplyr
#'
#' @export




QT_Outlier_Explorer_Overall <- function(data, settings) {



    # choose between observed or change values
    if (settings$plot_what == "Observed") {
        value_var <- settings$value_col
    } else if (settings$plot_what == "Change") {
        value_var <- "CHG" # assuming change variable is named CHG, check CDISC standard
    }

    data_filtered <- data %>%
        filter(.data[[settings$measure_col]] %in% settings$measure_values)

    # Derive columns to be presented on x and y axis based on user choices
    data1 <- data_filtered %>%
        mutate(
            X_VAR = .data[[settings$Outlier_X_var]],
            Y_VAR = .data[[value_var]]
        )

    # Derive X axis title based on selected variables
    if (settings$Outlier_X_var == settings$value_col) {
        X_Title <- "Observed Values"
    } else if (settings$Outlier_X_var == settings$base_col) {
        X_Title <- "Baseline"
    }

    # Derive Y axis title based on selected variables
    if (settings$plot_what == "Observed") {
        Y_Title <- "Observed Values"
    } else if (settings$plot_what == "Change") {
        Y_Title <- "Change from Baseline"
    }


    # horizontal reference line
    hline <- function(y = 0, color = "blue") {
        list(
            type = "line",
            x0 = 0,
            x1 = 1,
            xref = "paper",
            y0 = y,
            y1 = y,
            line = list(color = color, width = 2, dash = "dash")
        )
    }


    # dynamic contour
    # https://stackoverflow.com/questions/41980772/equivalent-of-abline-in-plotly

    # a function to calculate your abline
    p_abline <- function(x, a = -1, b = 450) {
        y <- a * x + b
        return(y)
    }

    # find min , max

    findMinMax <- function(d) {
        x <- d[["X_VAR"]]
        y <- d[["Y_VAR"]]
        c(min_x=min(x, na.rm=TRUE), max_x=max(x, na.rm=TRUE), min_y=min(y, na.rm=TRUE), max_y=max(y, na.rm=TRUE))
    }

    m <- findMinMax(data1)


    # define reference lines based on Y axis variable
    if (settings$plot_what == "Observed") {
        reflines <- list()
        if (any(settings$RefLines %in% "QTc Interval > 450")) {
            reflines[[1]] <- hline(y = 450)
        }
        if (any(settings$RefLines %in% "QTc Interval > 480")) {
            reflines[[2]] <- hline(y = 480)
        }
        if (any(settings$RefLines %in% "QTc Interval > 500")) {
            reflines[[3]] <- hline(y = 500)
        }
    } else if (settings$plot_what == "Change") {
        reflines <- list()
        reflines[[1]] <-
            list(
                type = "line",
                # width= 2,
                line = list(dash = "dash", color = "red"),
                x0 = m["min_x"],
                x1 = m["max_x"],
                y0 = p_abline(m["min_x"], a=-1, b=450),
                y1 = p_abline(m["max_x"], a=-1, b=450)
            )
        if (any(settings$RefLines %in% "QTc Change from Baseline > 30")) {
            reflines[[2]] <- hline(y = 30)
        }
        if (any(settings$RefLines %in% "QTc Change from Baseline > 60")) {
            reflines[[3]] <- hline(y = 60)
        }
        if (any(settings$RefLines %in% "QTc Change from Baseline x=y Line")) {
            reflines[[4]] <- list(
                type = "line",
                x0 = 0,
                x1 = 1,
                xref = "paper",
                y0 = 0,
                y1 = 1,
                yref = "paper",
                line = list(color = "red", width = 2, dash = "dash")
            )
        }
    }


    fig <- data1 %>%
        plot_ly(
            x = ~X_VAR,
            y = ~Y_VAR,
            size = 10,
            color = ~ .data[[settings$group_col]],
            text = ~ paste0(
                .data[[settings$measure_col]], "<br>Time point: ", .data[[settings$visit_col]], "<br>Treatment: ",
                .data[[settings$group_col]], "<br>X-Value:", X_VAR, "<br>Y-Value: ", Y_VAR
            ),
            hoverinfo = "text",
            type = "scatter",
            mode = "markers",
            key = ~ .data[[settings$id_col]],
            source = "QT_outlier_Explorer_click"
        ) %>%
        layout(
            margin = list(b = 100),
            xaxis  = list(title = X_Title),
            yaxis  = list(title = Y_Title),
            shapes = reflines
        )


    return(fig)
}