R/krsa_violin_plot_grouped.R

In CogDisResLab/KRSA: KRSA: Kinome Random Sampling Analyzer

#' Generates grouped violin plots based on peptides signals intensities with t tests options
#'
#' Takes in the scaled dataset from krsa_scaleModel() and plot violin figures using ggplot2
#'
#' @param data the scaled dataset from krsa_scaleModel
#' @param peptides vector of peptides
#' @param grp_comp list of group comparison names
#' @param groups (optional) a vector of group names
#' @param test perform two group test
#' @param test_method type of test (default is wilcox.test)
#' @param violin add violin layer
#' @param dots add dotplot layer
#' @param lines add lines layer
#' @param avg_line draw averaged line across the two groups
#' @param ... arguments passed to ggsignif
#'
#'
#' @return ggplot figure
#'
#' @family plots
#'
#' @export
#'
#' @examples
#' TRUE
krsa_violin_plot_grouped <- function(data, peptides, grp_comp = NULL, groups = NULL, test = F, test_method = "wilcox.test",
                                     violin = TRUE, dots = FALSE, lines = FALSE, avg_line = T, ...) {
    data %>%
        dplyr::filter(Peptide %in% peptides) %>%
        dplyr::filter(!is.na(slope)) %>%
        {
            if (!is.null(groups)) dplyr::filter(., Group %in% groups) else .
        } -> data

    data %>%
        ggplot2::ggplot(ggplot2::aes(Group, slope)) -> gg

    if (violin) {
        gg <- gg + ggplot2::geom_violin(ggplot2::aes(fill = Group), show.legend = F, trim = F, width = 0.4)
    }


    gg <- gg + ggplot2::geom_boxplot(ggplot2::aes(fill = Group), width = 0.1, show.legend = F)

    if (dots) {
        gg <- gg + ggplot2::geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 1, alpha = 1 / 2)
    }

    if (lines) {
        gg <- gg + ggplot2::geom_line(ggplot2::aes(group = Peptide), alpha = 1 / 2)
    }
    if (test & !is.null(grp_comp)) {
        gg <- gg + ggsignif::geom_signif(
            comparisons = grp_comp,
            test = test_method,
            ...
        )
    }

    if (avg_line) {
        data %>%
            group_by(Group) %>%
            summarise(slopeM = mean(slope)) -> avg_data

        gg <- gg + ggplot2::geom_line(
            data = avg_data, ggplot2::aes(Group, slopeM, group = 1),
            color = "black", size = 3
        )
    }

    gg +
        ggplot2::labs(
            x = "",
            y = "Signal Intensity"
        ) +
        ggplot2::theme_bw()
}