R/ct_plot3.R

In shirewoman2/Consultancy: Standardized tools for using R within the Simcyp Consultancy Team

#' Concentration-time plots of the full time range, first dose, and last dose
#'
#' Conveniently group three concentration-time plots of multiple-dose regimen
#' data together: \enumerate{\item{the full time range across the top,}
#' \item{the first dose in the lower left quadrant, and}  \item{the last dose in
#' the lower right quadrant.}} This uses either \code{\link{ct_plot}} or
#' \code{\link{ct_plot_overlay}} behind the scenes to make the graphs, so most
#' of the options available for those functions will work here.
#'
#' @param ct_dataframe the input concentration-time data generated by running
#'   either the function \code{\link{extractConcTime}} or the function
#'   \code{\link{extractConcTime_mult}}
#' @param overlay TRUE or FALSE for whether to make overlaid graphs, i.e.,
#'   whether to use \code{\link{ct_plot_overlay}} (TRUE) or use
#'   \code{\link{ct_plot}} (FALSE) to generate the graphs. If you have more than
#'   one kind of tissue or more than one simulation file or more than one
#'   compound, we'll set this to TRUE automatically.
#' @param figure_type type of figure to plot. Options are:
#'
#'   \describe{
#'
#'   \item{"percentiles"}{(default) plots an opaque line for the mean data,
#'   lighter lines for the 5th and 95th percentiles of the simulated data, and
#'   open circles for the observed data. If an effecter were present, the
#'   default is dashed lines for the data in the presence of a perpetrator.}
#'
#'   \item{"trial means"}{plots an opaque line for the mean data, lighter lines
#'   for the mean of each trial of simulated data, and open circles for the
#'   observed data. If a perpetrator were present, lighter dashed lines indicate
#'   the mean of each trial of simulated data in the presence of the perpetrator.}
#'
#'   \item{"percentile ribbon"}{plots an opaque line for the mean data,
#'   transparent shading for the 5th to 95th percentiles of the simulated data,
#'   and open circles for the observed data. If a perpetrator were present, the
#'   default is to show the data without the perpetrator in blue and the data in
#'   the presence of the perpetrator in red. Note: You may sometimes see some
#'   artifacts -- especially for semi-log plots -- where the ribbon gets partly
#'   cut off. For arcane reasons we don't want to bore you with here, we can't
#'   easily prevent this. However, a possible fix is to set your y axis limits
#'   for the semi-log plot to be wider using \code{y_axis_limits_log}.}
#'
#'   \item{"means only"}{plots a black line for the mean data and, if an
#'   perpetrator was modeled, a dashed line for the concentration-time data with
#'   Inhibitor 1.}
#'
#'   \item{"Freddy"}{Freddy's favorite style of plot with trial means in light
#'   gray, the overall mean in thicker black, the 5th and 95th percentiles in
#'   dashed lines, and the observed data in semi-transparent purple-blue. Graphs
#'   with a perpetrator present lose the trial means, and the percentiles switch
#'   to solid, gray lines. \strong{An editorial comment:} While this does not
#'   align with the officially sanctioned template at this time, this looks
#'   \emph{sharp}, makes it easy to see the defining characteristics of the
#'   data, and I recommend checking it out, even just for your own purposes of
#'   examining your data. If the color is too much for you but you like the
#'   rest, try setting \code{obs_color = "black", obs_shape = c(1, 2)}. -LSh}}
#'
#' @param mean_type graph "arithmetic" (default) or "geometric" means or
#'   "median" for median concentrations. If that option was not included in the
#'   output, you'll get a warning and the graph will include one that was.
#' @param linear_or_log the type of graph to be returned. Options: \describe{
#'   \item{"semi-log"}{y axis is log transformed}
#'
#'   \item{"linear"}{no axis transformation}
#'
#'   \item{"both vertical"}{(default) both the linear and the semi-log graphs
#'   will be returned, and graphs are stacked vertically}
#'
#'   \item{"both horizontal"}{both the linear and the semi-log graphs will be
#'   returned, and graphs are side by side horizontally}
#'
#'   \item{"horizontal and vertical"}{both the linear and the semi-log graphs
#'   will be returned, and graphs are side by side horizontally (one graph; file
#'   name will end in "- horizontal") and stacked vertically (second graph; file
#'   name will end in "- vertical"). This option, which was designed to create
#'   the vertically stacked version of a graph for a report and the horizontal,
#'   side-by-side version for a presentation, is a bit different from the others
#'   since it will return two separate files. In the RStudio "Plots" window,
#'   you'll only see the vertically stacked version. Setting \code{fig_height}
#'   and \code{fig_width} will adjust only the dimensions of the horizontal
#'   figure; the default values will be used for the vertical one. If you
#'   request Word output, only the vertical plot will be saved in Word format;
#'   the horizontal plot will be saved as a png file.}}
#' @param time_range_LL time range to use for the lower-left graph; defaults to
#'   "first dose" but any option acceptable for \code{time_range} for the
#'   function \code{\link{ct_plot}} will also work.
#' @param x_axis_interval_LL optionally set the lower left graph x-axis major
#'   tick-mark interval. Acceptable input: any number or leave as NA to accept
#'   default values, which are generally reasonable guesses as to aesthetically
#'   pleasing and PK-relevant intervals.
#' @param x_axis_interval_U optionally set the upper graph x-axis major
#'   tick-mark interval. Acceptable input: any number or leave as NA to accept
#'   default values, which are generally reasonable guesses as to aesthetically
#'   pleasing and PK-relevant intervals.
#' @param time_range_LR time range to use for the lower-right graph; defaults to
#'   "first dose" but any option acceptable for \code{time_range} for the
#'   function \code{\link{ct_plot}} will also work.
#' @param x_axis_interval_LR optionally set the lower right graph x-axis major
#'   tick-mark interval. Acceptable input: any number or leave as NA to accept
#'   default values, which are generally reasonable guesses as to aesthetically
#'   pleasing and PK-relevant intervals.
#' @param graph_title_U graph title for the upper, full-time-range graph;
#'   defaults to "Full time range"
#' @param graph_title_LL graph title for the lower left graph; defaults to
#'   "First dose"
#' @param graph_title_LR graph title for the lower right graph; defaults to
#'   "Last dose"
#' @param conc_units_to_use concentration units to use for graphs. If left as
#'   NA, the concentration units in the source data will be used. Acceptable
#'   options are "mg/L", "mg/mL", "µg/L" (or "ug/L"), "µg/mL" (or "ug/mL"),
#'   "ng/L", "ng/mL", "µM" (or "uM"), or "nM". If you want to use a molar
#'   concentration and your source data were in mass per volume units or vice
#'   versa, you'll need to provide something for the argument
#'   \code{existing_exp_details}.
#' @param hline_position numerical position(s) of any horizontal lines to add to
#'   the graph. The default is NA to have no lines, and good syntax if you
#'   \emph{do} want lines would be, for example, \code{hline_position = 10} to
#'   have a horizontal line at 10 ng/mL (or whatever your concentration units
#'   are) or \code{hline_position = c(10, 100, 1000)} to have horizontal lines
#'   at each of those y values. Examples of where this might be useful would be
#'   to indicate a toxicity threshold, a target Cmin, or the lower limit of
#'   quantification for the assay used to generate the concentration-time data.
#' @param hline_style the line color and type to use for any horizontal lines
#'   that you add to the graph with \code{hline_position}. Default is "red
#'   dotted", but any combination of 1) a color in R and 2) a named linetype is
#'   acceptable. Examples: "red dotted", "blue dashed", or "#FFBE33 longdash".
#'   To see all the possible linetypes, type \code{ggpubr::show_line_types()}
#'   into the console.
#' @param vline_position numerical position(s) of any vertical lines to add to
#'   the graph. The default is NA to have no lines, and good syntax if you
#'   \emph{do} want lines would be, for example, \code{vline_position = 12} to
#'   have a vertical line at 12 h or \code{vline_position = seq(from = 0, to =
#'   168, by = 24)} to have horizontal lines every 24 hours for one week.
#'   Examples of where this might be useful would be indicating dosing times or
#'   the time at which some other drug was started or stopped.
#' @param vline_style the line color and type to use for any vertical lines that
#'   you add to the graph with \code{vline_position}. Default is "red dotted",
#'   but any combination of 1) a color in R and 2) a named linetype is
#'   acceptable. Examples: "red dotted", "blue dashed", or "#FFBE33 longdash".
#'   To see all the possible linetypes, type \code{ggpubr::show_line_types()}
#'   into the console.
#' @param legend_position Specify where you want the legend to be. Options are
#'   "left", "right", "bottom", "top", or "none" (default) if you don't want one
#'   at all. If you include the legend but then some graphs do have a legend and
#'   some graphs do not (e.g., some have perpetrators and some do not so there's
#'   nothing to put in a legend), the alignment between sets of graphs will be a
#'   bit off.
#' @param legend_orientation optionally specify how the legend entries should be
#'   oriented. Options are "vertical" or "horizontal", and, if left as NA, the
#'   legend entries will be "vertical" when the legend is on the  left or right
#'   and "horizontal" when it's on the top or bottom.
#' @param qc_graph TRUE or FALSE (default) on whether to create a second copy of
#'   the graph where the left panel shows the original graph and the right panel
#'   shows information about the simulation trial design. This works MUCH faster
#'   when you have already used \code{\link{extractExpDetails_mult}} to get
#'   information about how your simulation or simulations were set up and supply
#'   that object to the argument \code{existing_exp_details}.
#' @param existing_exp_details output from \code{\link{extractExpDetails}} or
#'   \code{\link{extractExpDetails_mult}} to be used with \code{qc_graph}
#' @param prettify_compound_names TRUE (default), FALSE or a character vector:
#'   This is asking whether to make compound names prettier in legend entries
#'   and in any Word output files. This was designed for simulations where the
#'   substrate and any metabolites, perpetrators, or perpetrator metabolites are
#'   among the standard options for the simulator, and leaving
#'   \code{prettify_compound_names = TRUE} will make the name of those compounds
#'   something more human readable. For example, "SV-Rifampicin-MD" will become
#'   "rifampicin", and "Sim-Midazolam" will become "midazolam". Setting this to
#'   FALSE will leave the compound names as is. For an approach with more
#'   control over what the compound names will look like in legends and Word
#'   output, set each compound to the exact name you  want with a named
#'   character vector. For example, \code{prettify_compound_names =
#'   c("Sim-Ketoconazole-400 mg QD" = "ketoconazole", "Wks-Drug ABC-low_ka" =
#'   "Drug ABC")} will make those compounds "ketoconazole" and "Drug ABC" in a
#'   legend.
#' @param name_clinical_study optionally specify the name(s) of the clinical
#'   study or studies for any observed data. This only affects the caption of
#'   the graph. For example, specifying \code{name_clinical_study = "101, fed
#'   cohort"} will result in a figure caption that reads in part "clinical study
#'   101, fed cohort". If you have more than one study, that's fine; we'll take
#'   care of stringing them together appropriately. Just list them as a
#'   character vector, e.g., \code{name_clinical_study = c("101",
#'   "102", "103")} will become "clinical studies 101, 102, and 103."
#' @param return_caption TRUE or FALSE (default) for whether to return any
#'   caption text to use with the graph. This works best if you supply something
#'   for the argument \code{existing_exp_details}. If set to TRUE, you'll get as
#'   output a list of the graph, the figure heading, and the figure caption.
#' @param save_graph optionally save the output graph by supplying a file name
#'   in quotes here, e.g., "My conc time graph.png" or "My conc time
#'   graph.docx". If you leave off ".png" or ".docx" from the file name, it will
#'   be saved as a png file, but if you specify a different graphical file
#'   extension, it will be saved as that file format. Acceptable graphical file
#'   extensions are "eps", "ps", "jpeg", "jpg", "tiff", "png", "bmp", or "svg".
#'   Do not include any slashes, dollar signs, or periods in the file name.
#'   Leaving this as NA means the file will not be saved to disk.
#' @param fig_height figure height in inches; default is 6
#' @param fig_width figure width in inches; default is 5
#' @param ... arguments that pass through to \code{\link{ct_plot}} or
#'   \code{\link{ct_plot_overlay}}
#' @param graph_labels TRUE (default) or FALSE for whether to include labels (A,
#'   B, C) for each of the small graphs.

#'
#' @return a set of 3 arranged ggplot2 graphs
#' @export
#'
#' @examples
#'
#' # We'll use the objects LMVct and MDZ_Keto for these examples. They're
#' # included in the package. 
#' 
#' # Examples with a single compound, tissue, and simulation ----------------------
#' 
#' # These are all graphs where, if you made one of the panels on its own, you
#' # would use the function ct_plot.
#' 
#' # Simplest option:
#' ct_plot3(ct_dataframe = LMVct)
#' 
#' # Change the 3 graph titles
#' ct_plot3(ct_dataframe = LMVct, 
#'          graph_title_U = "Full time range for letermovir", 
#'          graph_title_LL = "Dose 1 of letermovir", 
#'          graph_title_LR = "Dose 8 of letermovir")
#' 
#' # Tweak the time intervals for the bottom row graphs
#' ct_plot3(ct_dataframe = LMVct, 
#'          graph_title_LL = "Dose 1 & 2", 
#'          time_range_LL = "doses 1 to 2", 
#'          graph_title_LR = "Dose 7 & 8", 
#'          time_range_LR = "doses 7 to 8")
#' 
#' # Make any other adjustments that you would typically make with ct_plot. 
#' 
#' 
#' # Examples with multiple compounds, tissues, and/or simulations overlaid -------
#' 
#' # These are all graphs where, if you made one of the panels on its own, you
#' # would use the function ct_plot_overlay.
#' ct_plot3(ct_dataframe = MDZ_Keto %>% 
#'             filter(CompoundID == "substrate" & 
#'                       Tissue == "plasma"), 
#'          overlay = TRUE, colorBy_column = Inhibitor)
#' 
#' # Make any other adjustments that you would typically make with ct_plot_overlay.
#' # This option might be a bit much! But at least it shows how it would be
#' # done. :)
#' ct_plot3(ct_dataframe = MDZ_Keto, 
#'          overlay = TRUE,
#'          colorBy_column = Inhibitor, 
#'          facet1_column = Tissue, 
#'          facet2_column = Compound)
#' 
#' 
#' # You can adjust the graph titles and time ranges the same way as with a single
#' # simulation.
#' 

ct_plot3 <- function(ct_dataframe, 
                     overlay = FALSE, 
                     figure_type = "means only",
                     mean_type = "arithmetic",
                     linear_or_log = "semi-log", 
                     graph_title_U = "Full time range",
                     x_axis_interval_U = NA, 
                     graph_title_LL = "First dose", 
                     time_range_LL = "first dose",
                     x_axis_interval_LL = NA,
                     graph_title_LR = "Last dose",
                     time_range_LR = "last dose",
                     x_axis_interval_LR = NA,
                     legend_position = "none",
                     legend_orientation = NA, 
                     graph_labels = TRUE, 
                     conc_units_to_use = NA, 
                     hline_position = NA, 
                     hline_style = "red dotted", 
                     vline_position = NA, 
                     vline_style = "red dotted",
                     qc_graph = FALSE,
                     existing_exp_details = NA,
                     prettify_compound_names = TRUE,
                     name_clinical_study = NA, 
                     return_caption = FALSE, 
                     save_graph = NA,
                     fig_height = 6,
                     fig_width = 5,
                     ...){
   
   # error catching ---------------------------------------------------
   
   # Check whether tidyverse is loaded
   if("package:tidyverse" %in% search() == FALSE){
      stop("The SimcypConsultancy R package requires the package tidyverse to be loaded, and it doesn't appear to be loaded yet. Please run\nlibrary(tidyverse)\n    ...and then try again.", 
           call. = FALSE)
   }
   
   if(length(sort(unique(ct_dataframe$DoseNum))) == 1){
      stop("ct_plot3 is only for multiple-dose scenarios, but these data appear to be for only one dose.")
   }
   
   if(hasArg("time_range")){
      stop("You cannot supply anything for 'time_range' here because ct_plot3 needs to set the time range automatically. Please remove 'time_range' from your arguments.")
   }
   
   
   if(nrow(ct_dataframe) == 0){
      stop("Please check your input. The data.frame you supplied for ct_dataframe doesn't have any rows.", 
           call. = FALSE)
   }
   
   # Getting experimental details if they didn't supply them and want to have a
   # QC graph
   if(qc_graph == TRUE | "logical" %in% class(existing_exp_details) == FALSE){
      
      if("logical" %in% class(existing_exp_details)){ 
         Deets <- tryCatch(
            extractExpDetails(sim_data_file = unique(ct_dataframe$File), 
                              exp_details = "Summary and Input")[["MainDetails"]], 
            error = function(x) "missing file")
      } else {
         Deets <- harmonize_details(existing_exp_details)[["MainDetails"]] %>%
            filter(File %in% unique(ct_dataframe$File))
         
         if(nrow(Deets == 0)){
            Deets <- tryCatch(
               extractExpDetails(sim_data_file = unique(ct_dataframe$File), 
                                 exp_details = "Summary and Input")[["MainDetails"]], 
               error = function(x) "missing file")
         }
      }
      
      if(qc_graph == TRUE & 
         (class(Deets)[1] == "character" || nrow(Deets) == 0)){
         warning("We couldn't find the source Excel file for this graph, so we can't QC it.", 
                 call. = FALSE)
         qc_graph <- FALSE
      }
   }
   
   
   # main body of function -------------------------------------------
   
   if(graph_labels){
      labels <- "AUTO"
   } else {
      labels <- NULL
   }
   
   MyTissue <- unique(ct_dataframe$Tissue)
   MyTissueSubtype <- ifelse("Tissue_subtype" %in% names(ct_dataframe), 
                             unique(ct_dataframe$Tissue_subtype), 
                             "none")
   MyCompoundID <- unique(as.character(ct_dataframe$CompoundID))
   
   NumProfiles <- ifelse(length(MyTissue) == 1 & length(MyCompoundID) == 1 &
                            length(MyTissueSubtype) == 1,
                         "single", "multiple")
   
   overlay <- ifelse(NumProfiles == "multiple", TRUE, overlay)
   
   if(overlay){
      
      A <- ct_plot_overlay(ct_dataframe = ct_dataframe,
                           figure_type = figure_type,
                           mean_type = mean_type, 
                           linear_or_log = linear_or_log,
                           time_range = NA,
                           x_axis_interval = x_axis_interval_U,
                           existing_exp_details = existing_exp_details, 
                           conc_units_to_use = conc_units_to_use, 
                           graph_title = graph_title_U,
                           qc_graph = FALSE,
                           graph_labels = FALSE, 
                           legend_position = legend_position, 
                           legend_orientation = legend_orientation, 
                           ..., 
                           save_graph = NA)
      
      # Suppressing messages and warnings after the 1st set.
      suppressWarnings(suppressMessages(
         B <- ct_plot_overlay(ct_dataframe = ct_dataframe,
                              figure_type = figure_type,
                              mean_type = mean_type, 
                              linear_or_log = linear_or_log,
                              time_range = time_range_LL,
                              x_axis_interval = x_axis_interval_LL,
                              existing_exp_details = existing_exp_details, 
                              conc_units_to_use = conc_units_to_use, 
                              graph_title = graph_title_LL,
                              qc_graph = FALSE,
                              graph_labels = FALSE, 
                              legend_position = "none", 
                              legend_orientation = legend_orientation, 
                              ..., 
                              save_graph = NA)))
      
      suppressWarnings(suppressMessages(
         C <- ct_plot_overlay(ct_dataframe = ct_dataframe,
                              figure_type = figure_type,
                              mean_type = mean_type, 
                              linear_or_log = linear_or_log,
                              time_range = time_range_LR, 
                              graph_title = graph_title_LR,
                              x_axis_interval = x_axis_interval_LR,
                              existing_exp_details = existing_exp_details, 
                              conc_units_to_use = conc_units_to_use, 
                              qc_graph = FALSE,
                              graph_labels = FALSE, 
                              legend_position = "none", 
                              legend_orientation = legend_orientation, 
                              ..., 
                              save_graph = NA)))
      
      suppressWarnings(
         Out <- ggpubr::ggarrange(A, 
                                  ggpubr::ggarrange(
                                     B, C, 
                                     labels = switch(as.character(graph_labels), 
                                                     "TRUE" = list("", "C"), 
                                                     "FALSE" = NULL), 
                                     legend = "none"), 
                                  
                                  nrow = 2, 
                                  labels = labels, 
                                  common.legend = TRUE, legend = legend_position)
      )
      
   } else {
      
      if(hasArg("colorBy_column") | hasArg("facet1_column") |
         hasArg("facet2_column")){
         stop("It looks like you wanted to use 'ct_plot_overlay' with this function to create graphs of overlaid concentration-time data, but you have specified 'overlay = FALSE' (or left it as the default). Please change 'overlay' to TRUE and try again.")
      }
      
      A <- ct_plot(ct_dataframe = ct_dataframe,
                   figure_type = figure_type,
                   mean_type = mean_type, 
                   linear_or_log = linear_or_log,
                   legend_position = legend_position,
                   legend_orientation = legend_orientation, 
                   time_range = NA, 
                   x_axis_interval = x_axis_interval_U,
                   graph_title = graph_title_U,
                   existing_exp_details = existing_exp_details, 
                   conc_units_to_use = conc_units_to_use, 
                   save_graph = NA, 
                   qc_graph = FALSE,
                   graph_labels = FALSE, 
                   ...)
      
      suppressWarnings(suppressMessages(
         B <- ct_plot(ct_dataframe = ct_dataframe,
                      figure_type = figure_type,
                      mean_type = mean_type, 
                      linear_or_log = linear_or_log,
                      legend_position = "none",
                      legend_orientation = legend_orientation, 
                      ..., 
                      time_range = time_range_LL, 
                      x_axis_interval = x_axis_interval_LL,
                      graph_title = graph_title_LL,
                      existing_exp_details = existing_exp_details, 
                      conc_units_to_use = conc_units_to_use, 
                      qc_graph = FALSE,
                      graph_labels = FALSE, 
                      save_graph = NA)))
      
      suppressWarnings(suppressMessages(
         C <- ct_plot(ct_dataframe = ct_dataframe,
                      figure_type = figure_type,
                      mean_type = mean_type, 
                      linear_or_log = linear_or_log,
                      legend_position = "none",
                      legend_orientation = legend_orientation, 
                      qc_graph = FALSE,
                      ..., 
                      time_range = time_range_LR, 
                      x_axis_interval = x_axis_interval_LR,
                      existing_exp_details = existing_exp_details, 
                      conc_units_to_use = conc_units_to_use, 
                      graph_title = graph_title_LR,
                      graph_labels = FALSE, 
                      save_graph = NA)))
      
      if(legend_position == "none"){
         Out <- ggpubr::ggarrange(
            A, 
            ggpubr::ggarrange(
               B, C, 
               labels = switch(as.character(graph_labels), 
                               "TRUE" = list("", "C"), 
                               "FALSE" = NULL), 
               legend = "none"), 
            labels = labels, 
            nrow = 2, legend = "none")
         
      } else {
         Out <- ggpubr::ggarrange(
            A, 
            ggpubr::ggarrange(
               B, C, 
               labels = switch(as.character(graph_labels), 
                               "TRUE" = list("", "C"), 
                               "FALSE" = NULL), 
               legend = "none"), 
            labels = labels, 
            nrow = 2, common.legend = TRUE, legend = "bottom")
      }
   }
   
   # Setting up figure caption --------------------------------------------
   
   FigText <- make_ct_caption(ct_dataframe = ct_dataframe, 
                              single_or_multiple_profiles = NumProfiles, 
                              plot_type = "concentration-time", 
                              existing_exp_details = existing_exp_details, 
                              mean_type = mean_type, 
                              linear_or_log = linear_or_log, 
                              figure_type = figure_type, 
                              prettify_compound_names = prettify_compound_names, 
                              name_clinical_study = name_clinical_study, 
                              hline_position = hline_position, 
                              vline_position = vline_position, 
                              hline_style = hline_style, 
                              vline_style = vline_style)
   
   
   # Saving ------------------------------------------------------------------
   
   Out <- list("graph" = Out)
   
   if(qc_graph){
      
      QCTable <- formatTable_Simcyp(
         annotateDetails(as.data.frame(Deets) %>%
                            filter(File %in% unique(ct_dataframe$File)), 
                         detail_set = "Methods") %>% 
            select(-c(SimulatorSection, matches("All files"), Sheet, 
                      Notes, CompoundID, Compound)), 
         shading_column = Detail)
      
      Out[["QCgraph"]] <- ggpubr::ggarrange(
         plotlist = list(Out$graph, flextable::gen_grob(QCTable)),
         font.label = list(size = graph_title_size))
   }
   
   if(complete.cases(save_graph)){
      FileName <- save_graph
      if(str_detect(FileName, "\\.")){
         # Making sure they've got a good extension
         Ext <- sub("\\.", "", str_extract(FileName, "\\..*"))
         FileName <- sub(paste0(".", Ext), "", FileName)
         Ext <- ifelse(Ext %in% c("eps", "ps", "jpeg", "tiff",
                                  "png", "bmp", "svg", "jpg", "docx"), 
                       Ext, "png")
         FileName <- paste0(FileName, ".", Ext)
      } else {
         FileName <- paste0(FileName, ".png")
         Ext <- "png"
      }
      
      if(qc_graph & Ext != "docx"){
         ggsave(sub(paste0("\\.", Ext), " - QC.png", FileName), 
                height = fig_height, width = fig_width * 2, dpi = 600, 
                ggpubr::ggarrange(plotlist = list(Out$QCgraph), 
                                  nrow = 1))
      }
      
      if(Ext == "docx"){
         # This is when they want a Word file as output
         OutPath <- dirname(FileName)
         
         if(OutPath == "."){
            OutPath <- getwd()
         }
         
         FileName <- basename(FileName)
         
         # Setting some values that don't make sense for this scenario but are
         # needed for making the Rmd file work.
         EnzPlot <- FALSE
         ReleaseProfPlot <- FALSE
         DissolutionProfPlot <- FALSE
         
         rmarkdown::render(system.file("rmarkdown/templates/multctplot/skeleton/skeleton.Rmd",
                                       package="SimcypConsultancy"), 
                           output_dir = OutPath, 
                           output_file = FileName, 
                           quiet = TRUE)
         # Note: The "system.file" part of the call means "go to where the
         # package is installed, search for the file listed, and return its
         # full path.
         
      } else {
         # This is when they want any kind of graphical file format.
         ggsave(FileName, height = fig_height, width = fig_width, dpi = 600, 
                plot = Out$graph)
         
      }
   }
   
   if(return_caption){
      Out[["figure_heading"]] <- FigText$heading
      Out[["figure_caption"]] <- FigText$caption
   } 
   
   if(length(Out) == 1){
      Out <- Out[[1]]
   }
   
   return(Out)
   
}