Copy of meanSdPlot from vsn package with minor edits

meanSdPlot.R

meanSdPlot <- function(x, ranks = TRUE, xlab = ifelse(ranks, "rank(mean)", "mean"), 
                       ylab = "sd", pch, plot = TRUE, bins = 50, ...) {
      
      stopifnot(is.logical(ranks), length(ranks) == 1, !is.na(ranks))

      n = nrow(x)
      if (n == 0L) {
        warning("In 'meanSdPlot': input matrix 'x' has 0 rows. There is nothing to be done.")
        return()
      }
      if (!missing(pch)) {
        warning("In 'meanSdPlot': 'pch' is ignored.")
      }
      
      px   = rowMeans(x, na.rm = TRUE)
      ## here I (Mike Love) edit rowV to rowVars
      py   = sqrt(matrixStats::rowVars(x, mean = px, na.rm = TRUE))
      rpx  = rank(px, na.last = FALSE, ties.method = "random")
                
      ## run median with centers at dm, 2*dm, 3*dm,... and width 2*dm
      dm        = 0.025
      midpoints = seq(dm, 1-dm, by = dm)
      within    = function(x, x1, x2) { (x >= x1) & (x <= x2) }
      mediwind  = function(mp) median(py[within(rpx/n, mp - 2*dm, mp + 2*dm)], na.rm = TRUE)
      rq.sds    = sapply(midpoints, mediwind)
                
      res = if(ranks) {
        list(rank = midpoints*n, sd = rq.sds, px = rpx, py = py)
      } else {
        list(quantile = quantile(px, probs = midpoints, na.rm = TRUE), sd = rq.sds, px = px, py = py)
      }
      
      fmt = function() function(x) format(round(x, 0), nsmall = 0L, scientific = FALSE)

      res$gg = ggplot(data.frame(px = res$px, py = res$py),
            aes_string(x = "px", y = "py")) + xlab(xlab) + ylab(ylab) +
            geom_hex(bins = bins, ...) +
            scale_fill_gradient(name = "count", trans = "log", labels = fmt()) + 
          geom_line(aes_string(x = "x", y = "y"),
                    data = data.frame(x = res[[1]], y = res$sd), color = "red")
              
      if (plot) print(res$gg)
      
      return(invisible(res))
}