fm_plot: Create a plot of how fm or fe values change over time

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

Create a plot of how fm or fe values change over time

Description

fm_plot is meant to be used in conjunction with extractFmFe to create a graph with overlaid fm and fe data from a simulation or simulations. UNDER CONSTRUCTION.

Usage

fm_plot(
  fm_dataframe,
  mean_type = "arithmetic",
  figure_type = "means only",
  linear_or_log = "linear",
  colorBy_column,
  color_labels = NA,
  legend_label_color = NA,
  color_set = "default",
  linetype_column,
  linetypes = c("solid", "dashed"),
  line_width = 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,
  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,
  hline_position = NA,
  hline_style = "red dotted",
  vline_position = NA,
  vline_style = "red dotted",
  graph_labels = TRUE,
  graph_title = NA,
  graph_title_size = 14,
  prettify_compound_names = TRUE,
  legend_position = NA,
  existing_exp_details = NA,
  return_caption = FALSE,
  save_graph = NA,
  fig_height = 6,
  fig_width = 5,
  assume_unique = TRUE
)

Arguments

fm_dataframe	input data.frame of fm or fe values generated from running extractFmFe and setting the argument returnOnlyMaxMin to FALSE. Note that this will only give you the kind of data you need for making a graph if these are dynamic fm or fe values and not static, e.g., the data must be from the tab "Time variant %fm and fe"
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 and lighter lines for the mean of each trial of simulated 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 "linear" no axis transformation; this is the default "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 sim_enz_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 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. Like "blue-green", this palette can be especially useful if you are comparing a systematic change in some continuous variable. "purples" a set of purples fading from lavender to aubergine. 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.
linetype_column	the column in sim_enz_dataframe that should be used for determining the line types. For example, if linetype_column is set to Inhibitor, then the default is to show a solid line for no inhibitor being present and then a dashed line 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.
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 sim_enz_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.
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. 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, 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. (Reminder: Numeric data should not be in quotes.)
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
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
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
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 = 100 to have a horizontal line at 100 percent of the baseline enzyme abundance or hline_position = c(50, 100, 200) to have horizontal lines at each of those y values.
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 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
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. 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 or in a figure caption.
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.
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 graph

Examples

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