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#' @title A helper function that takes simulation results and produces ggplot 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 main list indicates the number of separate plots to make.
#' Each list entry is itself a list, and corresponds to one plot and
#' needs to contain the following information/elements: \cr
#' 1. A data frame list element 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. \cr
#' 2. Meta-data for the plot, provided in the following variables: \cr
#' optional: plottype - One of "Lineplot" (default if nothing is provided),"Scatterplot","Boxplot", "Mixedplot". \cr
#' optional: xlab, ylab - Strings to label axes. \cr
#' optional: xscale, yscale - Scaling of axes, valid ggplot2 expression, e.g. "identity" or "log10". \cr
#' optional: xmin, xmax, ymin, ymax - Manual min and max for axes. \cr
#' optional: makelegend - TRUE/FALSE, add legend to plot. Assume true if not provided. \cr
#' optional: legendtitle - Legend title, if NULL/not supplied, default is used \cr
#' optional: legendlocation - if "left" is specified, top left. Otherwise top. \cr
#' optional: linesize - Width of line, numeric, i.e. 1.5, 2, etc. set to 1.5 if not supplied. \cr
#' optional: pallette - overwrite plot colors by providing a vector of color names or hex numbers to be used for the plot. \cr
#' optional: title - A title for each plot. \cr
#' optional: for multiple plots, specify res[[1]]$ncols to define number of columns \cr
#'
#' @return A ggplot plot structure for display in a Shiny UI.
#' @details This function can be called to produce plots, i.e. those displayed for each app.
#' The input needed by this function is produced by either calling the \code{\link{run_model}} function (as done when going through the UI)
#' or manually transforming the output from a simulate_ function into the correct list structure as explained here.
#' @rawNamespace import(ggplot2, except = last_plot)
#' @importFrom stats reshape
#' @importFrom gridExtra grid.arrange
#' @importFrom rlang .data
#' @author Andreas Handel
#' @export
generate_ggplot <- function(res)
{
# change ggplot color palette to color-blind friendly
# http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
# I added more colors at the end to have 12, enough for all simulations
# the ones I added are likely not color-blind friendly but rarely used in the app
cbfpalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#00523B","#D5C711","#0019B2","#cc0000")
#nplots contains the number of plots to be produced.
nplots = length(res) #length of list
allplots=list() #will hold all plots
for (n in 1:nplots) #loop to create each plot
{
resnow = res[[n]]
#if a data frame called 'ts' exists, assume that this one is the data to be plotted
#otherwise use the data frame called 'dat'
#one of the 2 must exist, otherwise the function will not work
if (!is.null(resnow$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
}
#if nothing is provided, we assume a line plot. That could lead to silly plots.
plottype <- if(is.null(resnow$plottype)) {'Lineplot'} else {resnow$plottype}
#if the first column is called 'Time' (as returned from several of the simulators)
#rename to xvals for consistency 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
{
#using basic reshape function to reformat data
dat = stats::reshape(rawdat, varying = colnames(rawdat)[-1], v.names = 'yvals', timevar = "varnames", times = colnames(rawdat)[-1], direction = 'long', new.row.names = NULL)
dat$id <- NULL
}
#code variable names as factor and level them so they show up right in plot - factor is needed for plotting and text
mylevels = unique(dat$varnames)
dat$varnames = factor(dat$varnames, levels = mylevels, ordered = TRUE)
#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)
#lower and upper bounds for plots, these are used if none are provided by calling function
lb = 1e-10
ub = 1e20
#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
#the unusual notation for the aes settings is needed for use inside a package
#see here: https://ggplot2.tidyverse.org/dev/articles/ggplot2-in-packages.html
if (is.null(dat$IDvar))
{
p1 = ggplot2::ggplot(dat, ggplot2::aes(x = .data$xvals) )
}
else
{
p1 = ggplot2::ggplot(dat, ggplot2::aes(x = .data$xvals, group = .data$IDvar) )
}
###choose between different types of plots
if (plottype == 'Scatterplot')
{
p2 = p1 + ggplot2::geom_point(data = dat, aes(y = .data$yvals, color = factor(.data$varnames, ordered = FALSE), shape = factor(.data$varnames, ordered = FALSE)), size = linesize, na.rm=TRUE)
}
if (plottype == 'Boxplot')
{
p2 = p1 + ggplot2::geom_boxplot(data = dat, aes( y = .data$yvals, color = as.factor(.data$varnames)), size = linesize, na.rm=TRUE)
}
if (plottype == 'Lineplot')
{
p2 = p1 + ggplot2::geom_line(data = dat, aes( y = .data$yvals, color = as.factor(.data$varnames), linetype = as.factor(.data$varnames)), 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'), aes( y = .data$yvals, color = as.factor(.data$varnames), linetype = as.factor(.data$varnames)), size = linesize)
p2 = p1a + ggplot2::geom_point(data = dplyr::filter(dat,style == 'point'), aes( y = .data$yvals, color = as.factor(.data$varnames), shape = factor(.data$varnames, ordered = FALSE)), size = 2.5*linesize)
}
#set x-axis. 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)
p3 = p3 + ggplot2::labs(x = NULL)
}
else
{
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 and if provided, label
p4 = p3 + ggplot2::scale_y_continuous(trans = yscaletrans, limits=c(ymin,ymax))
if (!is.null(resnow$ylab)) { p4 = p4 + ggplot2::ylab(resnow$ylab) }
#apply title if provided
if (!is.null(resnow$title))
{
p4 = p4 + ggplot2::ggtitle(resnow$title)
}
#modify overall theme
p5 = p4 + ggplot2::theme_bw(base_size = 18)
#default palette is set, overwritten if user provided
plotpalette = cbfpalette
if (!is.null(resnow$palette)) {plotpalette = resnow$palette }
#do legend if TRUE or not provided
if (is.null(resnow$makelegend) || resnow$makelegend)
{
if (!is.null(resnow$legendlocation) && resnow$legendlocation == "left")
{
legendlocation = c(0,1)
}
else #default placement on top
{
legendlocation = "top"
}
legendtitle = ifelse(is.null(resnow$legendtitle), "Variables", resnow$legendtitle)
if (plottype != 'Mixedplot')
{
nvars = length(unique(dat$varnames))
p5a = p5 + ggplot2::guides(col = ggplot2::guide_legend(nrow=2, byrow=TRUE,title.position = 'left'))
p5b = p5a + ggplot2::theme(legend.position = legendlocation) #default is top
p5c = p5b + ggplot2::theme(legend.key.width = grid::unit(3, "line")) #line thickness
p5d = p5c + ggplot2::scale_colour_manual(name = legendtitle, values=plotpalette[1:nvars]) #color for each variable
p5e = p5d + ggplot2::scale_linetype_discrete(name = legendtitle) #line type for each variable
pfinal = p5e + ggplot2::scale_shape_discrete(name = legendtitle) #symbol type for symbols
}
if (plottype == 'Mixedplot')
{
#trying to get legend right for combined line and symbol plots
#not fully working yet
#for data/symbols, legend still shows both lines and symbols, no matter what the plot is
# Compute the number of types and methods
npoints = length(unique(dplyr::filter(dat,style == 'point')$varnames))
nlines = length(unique(dplyr::filter(dat,style == 'line')$varnames))
p5a = p5 + ggplot2::guides(col = ggplot2::guide_legend(nrow=2, byrow=TRUE,title.position = 'left'))
p5b = p5a + ggplot2::theme(legend.position = legendlocation) #default is top
p5c = p5b + ggplot2::theme(legend.key.width = grid::unit(3, "line")) #line thickness
p5d = p5c + ggplot2::scale_colour_manual(name = legendtitle, values=plotpalette[1:(nlines+npoints)]) #color for each variable
p5e = p5d + ggplot2::scale_linetype_discrete(name = legendtitle, guide = "none") #symbol type for symbols; here is some trickery to make the legend look combined (turn off legend title/name)
pfinal = p5e + ggplot2::scale_shape_discrete(name = "", guide = "none") #symbol type for symbols
}
} #end doing legend
else
{
pfinal = p5 + ggplot2::theme(legend.position="none") + ggplot2::scale_colour_manual(values=plotpalette)
}
allplots[[n]] = pfinal
} #end loop over individual plots
#using gridExtra pacakge for multiple plots, ggplot for a single one
#potential advantage is that for a single ggplot, one could use interactive features
#such as klicking on point and displaying value
#currently not implemented
#cowplot is an alternative to arrange plots.
#There's a reason I ended up using grid.arrange() instead of cowplot but I can't recall
if (nplots>1)
{
#number of columns needs to be stored in 1st list element
resultplot <- gridExtra::grid.arrange(grobs = allplots, ncol = res[[1]]$ncols)
#resultplot <- gridExtra::arrangeGrob(grobs = allplots, ncol = res[[1]]$ncols)
#cowplot::plot_grid(plotlist = allplots, ncol = res[[1]]$ncol)
}
if (nplots==1)
{
resultplot <- pfinal
}
return(resultplot)
}
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