R/filter_summary_pie_box.R

In compbiomed/animalcules: Interactive microbiome analysis toolkit

#' Data visualization by pie chart / box plot
#'
#' @param MAE A multi-assay experiment object
#' @param samples_discard The list of samples to filter
#' @param filter_type Either 'By Microbes' or 'By Metadata'
#' @param sample_condition Which condition to check e.g. 'SEX'
#' @return A plotly object
#'
#' @examples
#' data_dir <- system.file("extdata/MAE.rds", package = "animalcules")
#' toy_data <- readRDS(data_dir)
#' result <- filter_summary_pie_box(toy_data,
#'   samples_discard = c("subject_2", "subject_4"),
#'   filter_type = "By Microbes",
#'   sample_condition = "SEX"
#' )
#' result
#'
#' @import dplyr
#' @import plotly
#' @import magrittr
#' @import reshape2
#' @import MultiAssayExperiment
#'
#' @export
filter_summary_pie_box <- function(MAE,
    samples_discard = NULL,
    filter_type,
    sample_condition) {
    # Subset the data
    MAE_subset <-
        mae_pick_samples(MAE = MAE, discard_samples = samples_discard)
    # Extract data
    microbe <- MAE_subset[["MicrobeGenetics"]]
    # host <- MAE_subset[['HostGenetics']]
    sam_table <- as.data.frame(colData(microbe)) # sample x condition
    counts_table <-
        as.data.frame(assays(microbe))[, rownames(sam_table)] #organism x sample
    # Add count summary data to sample table
    sam_table[, "Reads"] <- colSums(counts_table[, rownames(sam_table)])
    sam_table[, "Taxnum"] <- apply(counts_table, 2, function(x) sum(x >= 1))
    # select filter type
    if (filter_type == "Microbes") {
        cov <- "Taxnum"
    } else {
        cov <- sample_condition
    }
    # Use density plot if the variable has more than
    # 8 unique values Use pie chart if
    # the variable has less than 8 unique values
    num_levels <- length(unique(unlist(sam_table[, cov])))
    if (num_levels > 8 & num_levels / nrow(sam_table) >=
            0.3 & !is.character(unlist(sam_table[
                ,
                cov
            ]))) {
        vec <- unlist(sam_table[, cov])
        hover.txt <- paste(rownames(sam_table), ", ", vec, sep = "")
        num.scatter <- plotly::plot_ly(
            y = vec, jitter = 0.3,
            pointpos = -1.8, boxpoints = "all",
            hoverinfo = "text",
            text = hover.txt,
            marker = list(color = "rgb(7,40,89)"),
            line = list(color = "rgb(7,40,89)"),
            name = cov, type = "box"
        ) %>%
            layout(title = cov, yaxis = list(title = cov))
        num.scatter$elementId <- NULL
        return(num.scatter)
    } else {
        cat.df <- data.frame(table(sam_table[, cov]))
        cat.pie <- plotly::plot_ly(cat.df,
            labels = ~Var1,
            values = ~Freq, type = "pie",
            showlegend = FALSE
        ) %>% layout(title = cov)
        cat.pie$elementId <- NULL
        return(cat.pie)
    }
}