ct_plot_overlay: Overlay multiple data sets onto a single concentration-time...

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

Overlay multiple data sets onto a single concentration-time graph

Description

ct_plot_overlay is meant to be used in conjunction with extractConcTime_mult to create a graph with overlaid concentration-time data for multiple tissues, compounds, or simulations for easy comparisons. Please see the "Note" section at the bottom of the help file for a more-detailed overview of what this function is designed to do.

Usage

ct_plot_overlay(
  ct_dataframe,
  obs_to_sim_assignment = NA,
  mean_type = "arithmetic",
  figure_type = "means only",
  linear_or_log = "semi-log",
  normalize_by_dose = FALSE,
  colorBy_column,
  color_labels = NA,
  legend_label_color = NA,
  color_set = "default",
  obs_shape = NA,
  obs_color = NA,
  obs_size = NA,
  obs_fill_trans = NA,
  obs_line_trans = NA,
  connect_obs_points = FALSE,
  obs_on_top = TRUE,
  include_errorbars = FALSE,
  errorbar_width = 0.5,
  linetype_column,
  linetype_labels = NA,
  linetypes = c("solid", "dashed"),
  line_width = NA,
  line_transparency = NA,
  legend_label_linetype = NA,
  facet1_column,
  facet1_title = NA,
  facet2_column,
  facet2_title = NA,
  facet_ncol = NA,
  facet_nrow = NA,
  floating_facet_scale = FALSE,
  facet_spacing = NA,
  time_range = NA,
  x_axis_interval = NA,
  x_axis_label = NA,
  time_units_to_use = NA,
  pad_x_axis = TRUE,
  pad_y_axis = TRUE,
  y_axis_limits_lin = NA,
  y_axis_limits_log = NA,
  y_axis_interval = NA,
  y_axis_label = NA,
  conc_units_to_use = NA,
  hline_position = NA,
  hline_style = "red dotted",
  vline_position = NA,
  vline_style = "red dotted",
  graph_labels = TRUE,
  graph_title = NA,
  graph_title_size = 14,
  legend_position = NA,
  legend_orientation = NA,
  border = TRUE,
  prettify_compound_names = TRUE,
  qc_graph = FALSE,
  existing_exp_details = NA,
  name_clinical_study = NA,
  return_caption = FALSE,
  save_graph = NA,
  fig_height = 6,
  fig_width = 5,
  assume_unique = TRUE
)

Arguments

ct_dataframe	the input concentration-time data generated by running the function extractConcTime_mult or extractConcTime. Not quoted.
obs_to_sim_assignment	optionally specify which observed files should be compared to which simulator files. If left as NA and what you supplied for ct_dataframe doesn't already specify which observed data go with which simulated file, this will assume that all observed data goes with all simulated data. To specify, use a named character vector like this: obs_to_sim_assignment = c("obs data 1.xlsx" = "mdz-5mg-qd.xlsx", "obs data 2.xlsx" = "mdz-5mg-qd-cancer.xlsx") If one observed file needs to match more than one simulated file but not all the simulated files, you can do that by separating the simulated files with commas, e.g., obs_to_sim_assignment = c("obs data 1.xlsx" = "mdz-5mg-qd.xlsx, mdz-5mg-qd-fa08.xlsx", "obs data 2.xlsx" = "mdz-5mg-qd-cancer.xlsx, mdz-5mg-qd-cancer-fa08.xlsx"). Pay close attention to the position of commas and quotes there!
mean_type	plot "arithmetic" (default) or "geometric" mean concentrations or "median" concentrations as the main (thickest or only) line for each data set. If this aggregate measure is not available in the simulator output, you'll receive a warning message and we'll plot one that is available.
figure_type	the type of figure to plot. "means only" (default) show only the mean, geometric mean, or median (whatever you chose for "mean_type") "percentiles" plots an opaque line for the mean data and lighter lines for the 5th and 95th percentiles of the simulated data "percentile ribbon" show an opaque line for the mean data and transparent shading for the 5th to 95th percentiles. NOTE: There is a known bug within RStudio that can cause filled semi-transparent areas like you get with the "percentile ribbon" figure type to NOT get graphed for certain versions of RStudio. To get around this, within RStudio, go to Tools –> Global Options –> General –> Graphics –> And then set "Graphics device: backend" to "AGG". Honestly, this is a better option for higher-quality graphics anyway! "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.
linear_or_log	the type of graph to be returned. Options: "semi-log" y axis is log transformed; this is the default "linear" no axis transformation "both vertical" both the linear and the semi-log graphs will be returned, and graphs are stacked vertically "both horizontal" both the linear and the semi-log graphs will be returned, and graphs are stacked horizontally
normalize_by_dose	TRUE or FALSE (default) for whether to show dose-normalized concentration-time profiles
colorBy_column	(optional) the column in ct_dataframe that should be used for determining which color lines and/or points will be. This should be unquoted, e.g., colorBy_column = Tissue.
color_labels	optionally specify a character vector for how you'd like the labels for whatever you choose for colorBy_column to show up in the legend. For example, use color_labels = c("file 1.xlsx" = "fa 0.5", "file 2.xlsx" = "fa 0.2") to indicate that "file 1.xlsx" is for an fa of 0.5 and "file 2.xlsx" is for an fa of 0.2. The order in the legend will match the order designated here.
legend_label_color	optionally indicate on the legend something explanatory about what the colors represent. For example, if colorBy_column = File and legend_label_color = "Simulations with various fa values", that will make the label above the file names in the legend more explanatory than just "File". The default is to use whatever the column name is for colorBy_column. If you don't want a label for this legend item, set this to "none".
color_set	the set of colors to use. Options: "default" a set of colors from Cynthia Brewer et al. from Penn State that are friendly to those with red-green colorblindness. The first three colors are green, orange, and purple. This can also be referred to as "Brewer set 2". If there are only two unique values in the colorBy_column, then Brewer set 1 will be used since red and blue are still easily distinguishable but also more aesthetically pleasing than green and orange. "Brewer set 1" colors selected from the Brewer palette "set 1". The first three colors are red, blue, and green. "ggplot2 default" the default set of colors used in ggplot2 graphs (ggplot2 is an R package for graphing.) "rainbow" colors selected from a rainbow palette. The default palette is limited to something like 6 colors, so if you have more than that, that's when this palette is most useful. It's not very useful when you only need a couple of colors. "blue-green" a set of blues fading into greens. This palette can be especially useful if you are comparing a systematic change in some continuous variable – for example, increasing dose or predicting how a change in intrinsic solubility will affect concentration-time profiles – because the direction of the trend will be clear. "blues" a set of blues fading from sky to navy. Like "blue-green", this palette can be especially useful if you are comparing a systematic change in some continuous variable. "greens" a set of greens fading from chartreuse to forest. Great for showing systematic changes in a continuous variable. "purples" a set of purples fading from lavender to aubergine. Great for showing systematic changes in a continuous variable. "reds" a set of reds from pink to brick. Great for showing systematic changes in a continuous variable. "Tableau" uses the standard Tableau palette; requires the "ggthemes" package "viridis" from the eponymous package by Simon Garnier and ranges colors from purple to blue to green to yellow in a manner that is "printer-friendly, perceptually uniform and easy to read by those with colorblindness", according to the package author a character vector of colors If you'd prefer to set all the colors yourself to exactly the colors you want, you can specify those colors here. An example of how the syntax should look: color_set = c("dodgerblue3", "purple", "#D8212D") or, if you want to specify exactly which item in colorBy_column gets which color, you can supply a named vector. For example, if you're coloring the lines by the compound ID, you could do this: color_set = c("substrate" = "dodgerblue3", "inhibitor 1" = "purple", "primary metabolite 1" = "#D8212D"). If you'd like help creating a specific gradation of colors, please talk to a member of the R Working Group about how to do that using colorRampPalette.
obs_shape	optionally specify what shapes are used to depict observed data for a) the substrate drug alone and b) the substrate drug in the presence of a perpetrator. Input should look like this, for example: c(1, 2) to get an open circle for the substrate and an open triangle for the substrate in the presence of perpetrators, if there are any. If you only specify one value, it will be used for both substrate with and without perpetrators. To see all the possible shapes and what number corresponds to which shape, type ggpubr::show_point_shapes() into the console. If left as NA, substrate alone will be an open circle and substrate + inhibitor 1 will be an open triangle.
obs_color	optionally specify a color to use for observed data if the color isn't already mapped to a specific column. By default, observed data will be the same color as whatever else matches those observed data in colorBy_column, so if you have colored by compound ID, for example, the observed data will also be colored by compound ID. If you have one observed file that you're comparing to multiple simulation files (this is what ct_plot_overlay will do if "File" is NA for the observed data), then the observed data will all be black by default, or you could set that color to be, say, a lovely purple by setting this: obs_color = "darkorchid4". Hex color codes are also ok to use, and setting this to "none" will remove observed data from the graph.
obs_size	optionally specify the size of the points to use for the observed data. If left as NA, the size will be 2.
obs_fill_trans	optionally specify the transparency for the fill of the observed data points, which can be helpful when you have a lot of points overlapping. This only applies when you have specified a value for obs_color and when obs_shape is a shape that has a fill (example: obs_shape = 21 for a filled circle, which is the default). Acceptable values are from 0 (fully transparent, so no fill at all) to 1 (completely opaque or black). If left as the default NA, the observed data points will be 50 percent transparent, so the same as if this were set to 0.5.
obs_line_trans	optionally specify the transparency for the outline of the observed data points, which can be helpful when you have a lot of points overlapping. Acceptable values are from 0 (fully transparent, so no line at all) to 1 (completely opaque or black). If left as the default NA, the observed data points will be opaque, so the same as if this were set to 1.
connect_obs_points	TRUE or FALSE (default) for whether to add connecting lines between observed data points from the same individual
obs_on_top	TRUE (default) or FALSE for whether to show the observed data on top of the simulated data. If FALSE, the simulated data will be on top.
include_errorbars	TRUE or FALSE (default) for whether to include error bars for observed data points. This ONLY applies when you have supplied observed data from V22 or higher because those data files included a column titled "SD/SE", which is what we'll use for determining the error bar heights.
errorbar_width	width of error bars to use in hours (or, if you've used some other time unit, in whatever units are in your data). Default is 0.5.
linetype_column	the column in ct_dataframe that should be used for determining the line types and also the shapes of the points for depicting any observed data. For example, if linetype_column is set to Inhibitor, then the default is to show a solid line (simulated data) and an open circle (observed data) for no inhibitor being present and then a dashed line (simulated data) and an open triangle (observed data) when the inhibitor is present. You can set which types of lines to use with the argument linetypes and you can set which shapes of points you want with the argument obs_shape.
linetype_labels	optionally specify a character vector for how you'd like the labels for whatever you choose for linetype_column to show up in the legend. For example, use linetype_labels = c("file 1.xlsx" = "fa 0.5", "file 2.xlsx" = "fa 0.2") to indicate that "file 1.xlsx" is for an fa of 0.5 and "file 2.xlsx" is for an fa of 0.2. The order in the legend will match the order designated here.
linetypes	the line types to use. Default is "solid" for all lines. You'll need one line type for each possible value in the column you specified for linetype_column. If you get a graph you didn't expect as far as line types go, try checking what all the possible values are for the column you specified for linetype_column. You can do this by checking, e.g., unique(CT$Inhibitor) if your ct_dataframe was named "CT" and the column you set for linetype_column was "Inhibitor". To see possible line types by name, please enter ggpubr::show_line_types() into the console.
line_width	optionally specify how thick to make the lines. Acceptable input is a number; the default is 1 for most lines and 0.8 for some, to give you an idea of where to start.
line_transparency	optionally specify the transparency for the trial mean or percentile lines. Acceptable values are from 0 (fully transparent, so no line at all) to 1 (completely opaque or black). If left as the default NA, this value will be automatically determined.
legend_label_linetype	optionally indicate on the legend something explanatory about what the line types represent. For example, if linetype_column = Inhibitor and legend_label_linetype = "Inhibitor present", that will make the label in the legend above, e.g., "none", and whatever perpetrator was present more explanatory than just "Inhibitor". The default is to use whatever the column name is for linetype_column. If you don't want a label for this legend item, set this to "none".
facet1_column	optionally break up the graph into small multiples; this specifies the first of up to two columns to break up the data by, and the designated column name should be unquoted, e.g., facet1_column = Tissue.
facet1_title	optionally specify a title to describe facet 1. This is ignored if floating_facet_scale is TRUE or if you have specified facet_ncol or facet_nrow.
facet2_column	optionally break up the graph into small multiples; this specifies the second of up to two columns to break up the data by, and the designated column name should be unquoted, e.g., facet2_column = CompoundID. If you have graphs where the rows are broken up by one variable and the columns by another, then this will specify the columns of the graphs.
facet2_title	optionally specify a title to describe facet 2. This is ignored if floating_facet_scale is TRUE or if you have specified facet_ncol or facet_nrow.
facet_ncol	optionally specify the number of columns of facetted graphs you would like to have. This only applies when you have specified a column for facet1_column and/or facet2_column.
facet_nrow	optionally specify the number of rows of facetted graphs you would like to have. This only applies when you have specified a column for facet1_column and/or facet2_column.
floating_facet_scale	TRUE, FALSE (default), "x", "y", or "xy" for whether to allow the axes for each facet of a multi-facetted graph to scale freely to best fit whatever data are present. Default is FALSE, which means that all data will be on the same scale for easy comparison. However, this could mean that some graphs have lines that are hard to see, so you can set this to TRUE to allow the axes to shrink or expand according to what data are present for that facet. If this is set to "x", "y", or "xy", then the scale will only float along that axis. Play around with this to see what we mean. Floating axes comes with a trade-off for the looks of the graphs, though: Setting this to TRUE does mean that your x axis won't automatically have pretty breaks that are sensible for times in hours and that you can't specify intervals or limits for either the x or the y axis. If you're a ggplot2 user, here's what's going on under the hood: If you set floating_facet_scale = FALSE, the default, then ct_plot_overlay will use facet_grid to break up your graphs and set facet1_column to the rows and facet2_column to the columns. If you set floating_facet_scale = TRUE, then ct_plot_overlay will use facet_wrap to break up your data.
facet_spacing	Optionally set the spacing between facets. If left as NA, a best-guess as to a reasonable amount of space will be used. Units are "lines", so try, e.g. facet_spacing = 2.
time_range	time range to display. Options: NA entire time range of data; default a start time and end time in hours only data in that time range, e.g. c(24, 48). Note that there are no quotes around numeric data. "first dose" only the time range of the first dose "last dose" only the time range of the last dose "penultimate dose" only the time range of the 2nd-to-last dose, which can be useful for BID data where the end of the simulation extended past the dosing interval or data when the substrate was dosed BID and the perpetrator was dosed QD a specific dose number with "dose" or "doses" as the prefix the time range encompassing the requested doses, e.g., time_range = "dose 3" for the 3rd dose or time_range = "doses 1 to 4" for doses 1 to 4 "all obs" or "all observed" if you feel like spelling it out Time range will be limited to only times when observed data are present. "last dose to last observed" or "last obs" for short Time range will be limited to the start of the last dose until the last observed data point. "last dose to end" or "last to end" for short Time range will be limited to the start of the last dose until the end of the simulation.
x_axis_interval	set the 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.
x_axis_label	optionally supply a character vector or an expression to use for the x axis label
time_units_to_use	time units to use for graphs. If left as NA, the time units in the source data will be used. Options are "hours", "minutes", "days", or "weeks".
pad_x_axis	optionally add a smidge of padding to the x axis (default is TRUE, which includes some generally reasonable padding). If changed to FALSE, the y axis will be placed right at the beginning of your time range and all data will end exactly at the end of the time range specified. If you want a specific amount of x-axis padding, set this to a number; the default is c(0.02, 0.04), which adds 2% more space to the left side and 4% more space to the right side of the x axis. If you only specify one number, padding is added to the left side.
pad_y_axis	optionally add a smidge of padding to the y axis (default is TRUE, which includes some generally reasonable padding). As with pad_x_axis, if changed to FALSE, the x axis will be placed right at the bottom of your data, possibly cutting a point in half. If you want a specific amount of y-axis padding, set this to a number; the default is c(0.02, 0), which adds 2% more space to the bottom and nothing to the top of the y axis. If you only specify one number, padding is added to the bottom.
y_axis_limits_lin	optionally set the Y axis limits for the linear plot, e.g., c(10, 1000). If left as the default NA, the Y axis limits for the linear plot will be automatically selected. (Setting up semi-log plot y axis intervals manually is a bit tricky and is not currently supported.)
y_axis_limits_log	Optionally set the Y axis limits for the semi-log plot, e.g., c(10, 1000). Values will be rounded down and up, respectively, to the nearest order of magnitude. If left as NA, the Y axis limits for the semi-log plot will be automatically selected. This only applies when you have requested a semi-log plot with linear_or_log.
y_axis_interval	set the linear y-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 intervals.
y_axis_label	optionally supply a character vector or an expression to use for the y axis label
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 existing_exp_details.
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 do want lines would be, for example, hline_position = 10 to have a horizontal line at 10 ng/mL (or whatever your concentration units are) or 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.
hline_style	the line color and type to use for any horizontal lines that you add to the graph with 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 ggpubr::show_line_types() into the console.
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 do want lines would be, for example, vline_position = 12 to have a vertical line at 12 h or 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.
vline_style	the line color and type to use for any vertical lines that you add to the graph with 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 ggpubr::show_line_types() into the console.
graph_labels	TRUE (default) or FALSE for whether to include labels (A, B, C, etc.) for each of the small graphs. (Not applicable if only outputting linear or only semi-log graphs.)
graph_title	optionally specify a title that will be centered across your graph or set of graphs
graph_title_size	the font size for the graph title if it's included; default is 14. This also determines the font size of the graph labels.
legend_position	Specify where you want the legend to be. Options are "left", "right" (default in most scenarios), "bottom", "top", or "none" if you don't want one at all.
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.
border	TRUE (default) or FALSE for whether to include a border around each graph.
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 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. This will be looking for all the compound names in the columns "Compound" and "Inhibitor" in whatever you supply for ct_dataframe. For example, prettify_compound_names = c("Sim-Ketoconazole-400 mg QD" = "ketoconazole", "Wks-Drug ABC-low_ka" = "Drug ABC", "Itraconazole_Fasted Soln and OH-Itraconazole" = "itraconazole") will make those compounds "ketoconazole", "Drug ABC", and "itraconazole" in a legend.
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 extractExpDetails_mult to get information about how your simulation or simulations were set up and supply that object to the argument existing_exp_details.
existing_exp_details	output from extractExpDetails or extractExpDetails_mult to be used with qc_graph
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 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., name_clinical_study = c("101", "102", "103") will become "clinical studies 101, 102, and 103."
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 existing_exp_details. If set to TRUE, you'll get as output a list of the graph, the figure heading, and the figure caption.
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". The nice thing about saving to Word is that the figure title and caption text will be partly filled in automatically, although you should check that the text makes sense in light of your exact graph. If you leave off ".png" or ".docx", 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 automatically saved to disk.
fig_height	figure height in inches; default is 6
fig_width	figure width in inches; default is 5
assume_unique	TRUE (default) or FALSE for whether to assume that the concentration-time data contain no replicates, which messes things up and will likely cause this function to crash. Why would you want to skip this? Because it can take a LOOOOOOONG time if you have a lot of points. If you're sure your data are unique, set this to TRUE and save a fair amount of processing time to make your graph. If you're not sure what we're talking about here or if you get error messages that aren't terribly clear (which generally means that R wrote them and not your friendly SimcypConsultancy package authors), try setting this to FALSE.

Value

a ggplot2 graphs or a set of arranged ggplot2 graphs

Note

To make an overlaid graph, the ct_plot_overlay function will ask you to map specific columns in your source data to aesthetics in your graph, e.g., you can color the lines in your data based on which tissue is being graphed (column in ct_dataframe: Tissue) or set the line type based on whether a DDI perpetrator drug is present (column in ct_dataframe: Inhibitor) or break up the graphs into small multiples based on which simulation it is (column in ct_dataframe: File). When you run this function, two of the messages you'll see will be "Columns that vary in your data: ..." and "Graphing aesthetics you've assigned: ..." We set up these messages to try to let you know what columns in your source data – the object you supplied for ct_dataframe – have multiple unique values that might be useful for mapping aesthetics in your graphs. If you have a problem with your graphs such as multiple lines that are the same color or multiple lines that are the same line type and that's not what you expected and it's not clear why you're getting that graph, this pair of messages is meant to help you figure out what groups are present in your data and which of them you have mapped to an aesthetic aspect of your graph.

The faceting arguments may take a little playing around to understand and will probably be clearest if you're already a ggplot2 user. (This and all graphing functions in the SimcypConsultancy package use the package ggplot2 to make graphs.) Here's what's going on under the hood with the faceting arguments: If you set floating_facet_scale = FALSE, the default, then ct_plot_overlay will use facet_grid to break up your graphs and set facet1_column to the rows and facet2_column to the columns. If you set floating_facet_scale = TRUE, then ct_plot_overlay will use facet_wrap to break up your data and facet1_column will be the first variable it uses and facet2_column will be the second.

A note reqgarding observed data: There are some nuances to overlaying observed data. For detailed instructions and examples, please see the SharePoint file "Simcyp PBPKConsult R Files - Simcyp PBPKConsult R Files/SimcypConsultancy function examples and instructions/Concentration-time plots 3 - overlaying plots/Concentration-time-plot-examples-3.docx". (Sorry, we are unable to include a link to it here.) If you have observed concentration-time data to match your simulated data but you don't see those observed data on your graph, please check the help file for the function you used – either extractConcTime or extractConcTime_mult – to extract your data. Something has likely gone wrong in the data extraction.

Examples

data(MDZct)
ct_plot_overlay(ct_dataframe = MDZct, colorBy_column = File)

# Setting the legend labels for color to be more interpretable. Note
# that the order matches the order listed here, not the alphabetical
# order of the files.
ct_plot_overlay(ct_dataframe = MDZct, colorBy_column = File,
                color_labels = c("mdz-5mg-sd-fa1.xlsx" = "fa 1",
                                 "mdz-5mg-sd-fa0_8.xlsx" = "fa 0.8",
                                 "mdz-5mg-sd-fa0_6.xlsx" = "fa 0.6",
                                 "mdz-5mg-sd-fa0_4.xlsx" = "fa 0.4"))

# An example of how you might set the column "File" for a specific
# observed data file:
MyData <- MyData %>%
   mutate(File = case_when(ObsFile == "ObservedData1.xlsx" ~ "SimFileA.xlsx",
                           ObsFile == "ObservedData2.xlsx" ~ "SimFileB.xlsx"))

shirewoman2/Consultancy documentation built on Feb. 18, 2025, 10 p.m.