#' Plot violin for regions

 #'

 #' This function plots a violin plot of the methylation proportion for each region in the regions table. The methylation proportion is calculated as the mean of the modification probability wihtin each region and the violin represents the . The regions are then grouped and coloured by the group_col column in the regions table or samples(x).
 #'

 #' @param x the NanoMethResult object.
 #' @param regions a table of regions containing at least columns chr, strand,
 #'   start and end. Any additional columns can be used for grouping.
 #' @param binary_threshold the modification probability such that calls with
 #'   modification probability above the threshold are considered methylated, and
 #'   those with probability equal or below are considered unmethylated.
 #' @param group_col the column to group aggregated trends by. This column can
 #'   be in from the regions table or samples(x).
 #' @param palette the ggplot colour palette used for groups.
 #'

 #' @return a ggplot object containing the methylation violin plot.

 #'
 #' @examples
 #' nmr <- load_example_nanomethresult()
 #' gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))

 #' plot_violin(nmr, gene_anno)
 #' plot_violin(nmr, gene_anno, group_col = "sample")

 #'
 #' @export

 plot_violin <- function(

     x,
     regions,

     binary_threshold = 0.5,

     group_col = "group",

     palette = ggplot2::scale_colour_brewer(palette = "Set1")
 ) {
     if (!is.null(group_col)) {
         avail_columns <- c(colnames(samples(x)), colnames(regions))
         assertthat::assert_that(
             group_col %in% avail_columns,
             msg = glue::glue("'{group_col}' could not be found in columns of 'regions' or samples(x)")
         )
     }
 
     # grouped regions crashes downstream operations
     regions <- ungroup(regions)
 
     regions$methy_data <- purrr::map(
         seq_len(nrow(regions)),
         ~query_methy(
             x,
             regions$chr[.x],
             regions$start[.x],
             regions$end[.x],
             force = TRUE
         )
     )
 
     # remove regions with no data
     regions <- regions %>%

         dplyr::filter(purrr::map_lgl(.data$methy_data, function(x) nrow(x) != 0))

 
     region_data <- regions %>%
         dplyr::select(!dplyr::any_of(c("chr", "strand", "start", "end"))) %>%
         tidyr::unnest("methy_data") %>%

         dplyr::summarise(
             methy_prop = mean(.data$mod_prob > binary_threshold),
             .by = c("gene_id", "symbol", "sample", "chr", "strand")) %>%

         dplyr::inner_join(samples(x), by = "sample", multiple = "all")
 
     if (!is.null(group_col)) {
         aes_spec <- ggplot2::aes(
                 x = .data[[group_col]],
                 y = .data$methy_prop,
                 col = .data$group)
     } else {
         aes_spec <- ggplot2::aes(

                 x = .data[[group_col]],

                 y = .data$methy_prop)
     }
 
     ggplot2::ggplot(region_data, aes_spec) +

         ggplot2::geom_violin(draw_quantiles = 0.5) +

         ggplot2::scale_y_continuous(limits = c(0, 1)) +
         ggplot2::theme_bw() +
         palette
 }