auxiliary/old code/generate_plots.R

In ahgroup/modelbuilder: Building Compartmental Simulation Models Without Coding

#' @title A helper function that takes result from the simulators and produces plots
#'
#' @description This function generates plots to be displayed in the Shiny UI.
#' This is a helper function. This function processes results returned from the simulation, supplied as a list.
#' @param res A list structure containing all simulation results that are to be plotted.
#'    The length of the list indicates the number of separate plots to make.
#'    Each list entry corresponds to one plot and
#'    needs to contain the following information/elements:
#'    1. A data frame called "dat" or "ts". If the data frame is "ts" it is assumed to be
#'    a time series and by default a line plot will be produced and labeled Time/Numbers.
#'    For plotting, the data needs to be in a format with one column called xvals, one column yvals,
#'    one column called varnames that contains names for different variables.
#'    Varnames needs to be a factor variable or will be converted to one.
#'    If a column 'varnames' exist, it is assumed the data is in the right format. Otherwise it will be transformed.
#'    An optional column called IDvar can be provided for further grouping (i.e. multiple lines for stochastic simulations).
#'    If plottype is 'mixedplot' an additional column called 'style' indicating line or point plot
#'    for each variable is needed.
#'    2. Meta-data for the plot, provided in the following variables:
#'    optional: plottype - One of "Lineplot" (default is nothing is provided),"Scatterplot","Boxplot", "Mixedplot".
#'    optional: xlab, ylab - Strings to label axes.
#'    optional: xscale, yscale - Scaling of axes, valid ggplot2 expression, e.g. "identity" or "log10".
#'    optional: xmin, xmax, ymin, ymax - Manual min and max for axes.
#'    optional: legendtitle - Legend title, if NULL/not supplied no legend will be plotted.
#'    optional: linesize - Width of line, numeric, i.e. 1.5, 2, etc. set to 1.5 if not supplied.
#'    optional: title - A title for each plot.
#'
#' @return A plot structure for display in a Shiny UI.
#' @details This function is called by the Shiny server to produce plots returned to the Shiny UI.
#' Create plots run the simulation with default parameters just call the function:
#' result <- simulate_basicbacteria()
#' @author Andreas Handel
#' @import ggplot2
#' @importFrom gridExtra grid.arrange
#' @export

generate_plots <- function(res)
{

    #nplots contains the number of plots to be produced.
    nplots = length(res) #length of list

    allplots=list() #will hold all plots

    #lower and upper bounds for plots, these are used if none are provided by calling fuction
    lb = 1e-10;
    ub = 1e20;

    for (n in 1:nplots) #loop to create each plot
    {
      resnow = res[[n]]

      #if a data frame called 'ts' exists, assume that this one is the data to be plotted
      #otherwise use the data frame called 'dat'
      #one of the 2 must exist, otherwise the function will not work
      if (!is.null(resnow$ts))
      {
        rawdat = resnow$ts #if a timeseries is sent in and no x- and y-labels provided, we set default 'Time' and 'Numbers'
        if (is.null(resnow$ylab)) {resnow$ylab = 'Numbers'}
        if (is.null(resnow$xlab)) {resnow$xlab = 'Time'}
      }
      else {
        rawdat = resnow$dat
      }

      plottype <- if(is.null(resnow$plottype)) {'Lineplot'} else  {resnow$plottype} #if nothing is provided, we assume a line plot. That could lead to silly plots.



      #if the first column is called 'Time' (as returned from several of the simulators)
      #rename to xvals for consistency and so the code below will work
      if ( colnames(rawdat)[1] == 'Time' | colnames(rawdat)[1] == 'time' ) {colnames(rawdat)[1] <- 'xvals'}

      #for the plotting below, the data need to be in the form xvals/yvals/varnames
      #if the data is instead in xvals/var1/var2/var3/etc. - which is what the simulator functions produce
      #we need to re-format
      #if the data frame already has a column called 'varnames', we assume it's already properly formatted as xvals/yvals/varnames
      if ('varnames' %in% colnames(rawdat))
      {
        dat = rawdat
      }
      else
      {
        dat = tidyr::gather(rawdat, -xvals, value = "yvals", key = "varnames")
      }

      #code variable names as factor and level them so they show up right in plot - factor is needed for plotting and text
      mylevels = unique(dat$varnames)
      dat$varnames = factor(dat$varnames, levels = mylevels)

      #browser()


      #see if user/calling function supplied x- and y-axis transformation information
      xscaletrans <- ifelse(is.null(resnow$xscale), 'identity',resnow$xscale)
      yscaletrans <- ifelse(is.null(resnow$yscale), 'identity',resnow$yscale)

      #if we want a plot on log scale, set any value in the data at or below 0 to some small number
      if (xscaletrans !='identity') {dat$xvals[dat$xvals<=0]=lb}
      if (yscaletrans !='identity') {dat$yvals[dat$yvals<=0]=lb}

      #if exist, apply user-supplied x- and y-axis limits
      #if min/max axes values are not supplied
      #we'll set them here to make sure they are not crazy high or low
      xmin <- if(is.null(resnow$xmin)) {max(lb,min(dat$xvals))} else  {resnow$xmin}
      ymin <- if(is.null(resnow$ymin)) {max(lb,min(dat$yvals))} else  {resnow$ymin}
      xmax <- if(is.null(resnow$xmax)) {min(ub,max(dat$xvals))} else  {resnow$xmax}
      ymax <- if(is.null(resnow$ymax)) {min(ub,max(dat$yvals))} else  {resnow$ymax}

      #set line size as given by app or to 1.5 by default
      linesize = ifelse(is.null(resnow$linesize), 1.5, resnow$linesize)

      #if the IDvar variable exists, use it for further stratification, otherwise just stratify on varnames
      if (is.null(dat$IDvar))
      {
        p1 = ggplot2::ggplot(dat, ggplot2::aes(x = xvals, y = yvals, color = varnames, linetype = varnames, shape = varnames) )
      }
      if (!is.null(dat$IDvar))
      {
        p1 = ggplot2::ggplot(dat, ggplot2::aes(x = xvals, y = yvals, color = varnames, linetype = varnames, group = IDvar) )
      }

      if (plottype == 'Scatterplot')
      {
        p2 = p1 + ggplot2::geom_point( size = linesize, na.rm=TRUE)
      }
      if (plottype == 'Boxplot')
      {
        p2 = p1 + ggplot2::geom_boxplot()
      }
      if (plottype == 'Lineplot') #if nothing is provided for plottype, we assume a lineplot is wanted
      {
        p2 = p1 + ggplot2::geom_line(size = linesize, na.rm=TRUE)
      }
      if (plottype == 'Mixedplot')
      {
        #a mix of lines and points. for this, the dataframe needs to contain an extra column indicating line or point
        p1a = p1 + ggplot2::geom_line(data = dplyr::filter(dat,style == 'line'), size = linesize)
        p2 = p1a + ggplot2::geom_point(data = dplyr::filter(dat,style == 'point'), size = 2.5*linesize)
      }



      #no numbering/labels on x-axis for boxplots
      if (plottype == 'Boxplot')
      {
        p3 = p2 + ggplot2::scale_x_continuous(trans = xscaletrans, limits=c(xmin,xmax), breaks = NULL, labels = NULL)
      }
      if (plottype != 'Boxplot')
      {
        p3 = p2 + ggplot2::scale_x_continuous(trans = xscaletrans, limits=c(xmin,xmax))
        if (!is.null(resnow$xlab)) { p3 = p3 + ggplot2::xlab(resnow$xlab) }
      }

      #apply y-axis
      p5 = p3 + ggplot2::scale_y_continuous(trans = yscaletrans, limits=c(ymin,ymax))
      if (!is.null(resnow$ylab)) { p5 = p5 + ggplot2::ylab(resnow$ylab) }

      #do legend
      if (is.null(resnow$legend))
      {
        p6 = p5 + ggplot2::theme(legend.position="none")
      }
      else
      {
        p6 = p5 + ggplot2::theme(legend.key.width = grid::unit(3,"line")) + ggplot2::scale_colour_discrete(name  = resnow$legend) + ggplot2::scale_linetype_discrete(name = resnow$legend) + ggplot2::scale_shape_discrete(name = resnow$legend)
      }

      #apply title if provided
      if (!is.null(resnow$title)) { p6 = p6 + ggplot2::ggtitle(resnow$title) }

      #modify overall theme and legend details
      pfinal = p6 + ggplot2::theme_bw(base_size = 18) + ggplot2::theme(legend.position = c(0, 1), legend.justification=c(0,1), legend.key.width = unit(4,"line"), legend.background = element_rect(size=0.5, linetype="solid", colour ="black"))

      allplots[[n]] = pfinal

    } #end loop over individual plots

    #using gridExtra pacakge for multiple plots, ggplot for a single one
    #potential advantage is that for a single ggplot, one could use interactive features
    #such as klicking on point and displaying value
    #currently not implemented
    #cowplot is an alternative to arrange plots.
    #There's a reason I ended up using grid.arrange() instead of cowplot but I can't recall

    if (n>1)
    {
      #number of columns needs to be stored in 1st list element
      gridExtra::grid.arrange(grobs = allplots, ncol = res[[1]]$ncol)
      #cowplot::plot_grid(plotlist = allplots, ncol = res[[1]]$ncol)

    }
    if (n==1)
    {
      graphics::plot(pfinal)
    }
}