ct_plot_1stlast: Make concentration-time plots of the 1st and last doses only

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

Make concentration-time plots of the 1st and last doses only

Description

ct_plot_1stlast makes concentration-time plots like Freddy and Laura Sh. discussed. :)

Usage

ct_plot_1stlast(
  ct_dataframe,
  obs_to_sim_assignment = NA,
  mean_type = "arithmetic",
  figure_type = "means only",
  linear_or_log = "semi-log",
  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_1st = NA,
  x_axis_interval_1st = NA,
  time_range_last = NA,
  x_axis_interval_last = NA,
  x_axis_label = 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_size = 14,
  legend_position = NA,
  legend_orientation = NA,
  prettify_compound_names = TRUE,
  name_clinical_study = NA,
  existing_exp_details = NA,
  return_caption = FALSE,
  save_graph = NA,
  fig_height = NA,
  fig_width = NA
)

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!
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
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 light blue to dark blue. Like "blue-green", this palette can be especially useful if you are comparing a systematic change in some 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. We recommend setting this to CompoundID for this particular function. 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. If floating_facet_scale is FALSE and you haven't specified facet_ncol or facet_nrow, then facet1_column will designate the rows of the output graphs.
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 floating_facet_scale is FALSE and you haven't specified facet_ncol or facet_nrow, then facet2_column will designate the columns of the output 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 or FALSE (default) 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. 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.
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. (Reminder: Numeric data should not be in quotes.)
time_range_1st	time range for the "1st" dose, really, the first panel in the set of graphs (in other words, it doesn't have to be exactly the 1st dose)
x_axis_interval_1st	x axis interval to use for the 1st panel in the set of graphs
time_range_last	time range for the "last" dose, really, the second panel in the set of graphs (in other words, it doesn't have to be exactly the 1st dose)
x_axis_interval_last	x axis interval to use for the 1st panel in the set of graphs
x_axis_label	optionally supply a character vector or an expression to use for the x axis label
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 NA, the Y axis limits for the linear plot will be automatically selected. This only applies when you have requested a linear plot with linear_or_log.
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 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.
graph_title_size	the font size for the graph titles; 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.
prettify_compound_names	set this to a) TRUE (default) or FALSE for whether to make the compound names in the legend prettier or b) supply a named character vector to set it to the exact name you'd prefer to see in your legend. For example, 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, and prettify_compound_names = TRUE will make some reasonable guesses about what a prettier compound name should be. An example of setting this to TRUE: "SV-Rifampicin-MD" would become "rifampicin", and "Sim-Ketoconazole-200 mg BID" would become "ketoconazole".
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."
existing_exp_details	output from extractExpDetails or extractExpDetails_mult to be used for creating figure headings and captions tailored to the specific simulation when saving to a Word file
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
fig_width	figure width in inches

Value

a ggplot2 graphs or a set of arranged ggplot2 graphs

Examples

data(MDZct)

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