R/ct_plot_obs.R
In shirewoman2/Consultancy: Standardized tools for using R within the Simcyp Consultancy Team
Defines functions
ct_plot_obs
Documented in
ct_plot_obs
#' Plot observed concentration-time data
#'
#' \code{ct_plot_obs} is meant to be used in conjunction with
#' \code{\link{extractObsConcTime}} or \code{\link{extractObsConcTime_mult}} to
#' create single graphs with overlaid observed concentration-time data.
#'
#' @param ct_dataframe the input concentration-time data generated by running
#' the function \code{\link{extractConcObsTime_mult}} or
#' \code{\link{extractObsConcTime}}. Not quoted.
#' @param mean_type plot "arithmetic" (default) or "geometric" mean
#' concentrations or "median" concentrations as the main (thickest or only)
#' line for each data set. If you don't want to show the means, set this to
#' "none". If you \emph{only} want to show the mean data and not the
#' individual, set this to whatever mean type you want plus "only", e.g.,
#' \code{"arithmetic only"} to show only the arithmetic mean data and none of
#' the individuals. If you haven't included mean data in \code{ct_dataframe},
#' the mean will be calculated for you using our best guess as to what the
#' groups should be. In other words, you may want to calculate the means
#' yourself just to be sure you're getting what you expect. Talk to Laura Sh.
#' if you would like help here.
#' @param nominal_times optionally specify what the nominal times were for the
#' concentration-time data. If left as NA, when we calculate the means, we'll
#' group the data by the times \emph{exactly} as listed in the individual
#' data. If you know that the nominal times were, say, 0, 0.5, 1, 2, and 4
#' hours but that the times listed in the data were the exact draw times, then
#' supply a numeric vector like this: \code{nominal_times = c(0, 0.5, 1, 2,
#' 4)} and we will match each individual time with the nominal time closest to
#' it. This way, you'll get data from a draw time at, say, 4.1 h grouped with
#' another draw time at, say, 3.95 h; they'll both be put into the 4-hour bin.
#' @param linear_or_log the type of graph to be returned. Options: \describe{
#' \item{"semi-log"}{y axis is log transformed; this is the default}
#'
#' \item{"linear"}{no axis transformation}
#'
#' \item{"both vertical"}{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 stacked horizontally}}
#' @param colorBy_column (optional) the column in \code{ct_dataframe} that
#' should be used for determining which color lines and/or points will be.
#' This should be unquoted, e.g., \code{colorBy_column = Tissue}.
#' @param color_labels optionally specify a character vector for how you'd like
#' the labels for whatever you choose for \code{colorBy_column} to show up in
#' the legend. For example, use \code{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.
#' @param legend_label_color optionally indicate on the legend something
#' explanatory about what the colors represent. For example, if
#' \code{colorBy_column = File} and \code{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 \code{colorBy_column}. If you don't want a
#' label for this legend item, set this to "none".
#' @param color_set the set of colors to use. Options: \describe{
#'
#' \item{"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.}
#'
#' \item{"Brewer set 1"}{colors selected from the Brewer palette "set 1". The
#' first three colors are red, blue, and green.}
#'
#' \item{"ggplot2 default"}{the default set of colors used in ggplot2 graphs
#' (ggplot2 is an R package for graphing.)}
#'
#' \item{"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 \emph{not} very useful
#' when you only need a couple of colors.}
#'
#' \item{"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.}
#'
#' \item{"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.}
#'
#' \item{"Tableau"}{uses the standard Tableau palette; requires the "ggthemes"
#' package}
#'
#' \item{"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}
#'
#' \item{a character vector of colors}{If you'd prefer to set all the colors
#' yourself to \emph{exactly} the colors you want, you can specify those
#' colors here. An example of how the syntax should look: \code{color_set =
#' c("dodgerblue3", "purple", "#D8212D")} or, if you want to specify exactly
#' which item in \code{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: \code{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
#' \link{colorRampPalette}.}}
#'
#' @param 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:
#' \code{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 \code{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.
#' @param obs_size optionally specify the size of the points to use for the
#' observed data. If left as NA, the size will be 2.
#' @param 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
#' \code{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: \code{obs_color =
#' "darkorchid4"}. Hex color codes are also ok to use, and setting this to
#' "none" will remove observed data from the graph.
#' @param 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
#' \code{obs_color} and when \code{obs_shape} is a shape that has a fill
#' (example: \code{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.
#' @param obs_line_trans 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), and the default is 0.5 for 50%
#' transparent.
#' @param connect_obs_points TRUE (default) or FALSE for whether to add
#' connecting lines between observed data points from the same individual
#' @param 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.
#' @param 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.
#' @param linetype_column the column in \code{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 \code{linetype_column} is set
#' to \code{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 \emph{is} present. You can set which types of lines to
#' use with the argument \code{linetypes} and you can set which shapes of
#' points you want with the argument \code{obs_shape}.
#' @param linetype_labels optionally specify a character vector for how you'd
#' like the labels for whatever you choose for \code{linetype_column} to show
#' up in the legend. For example, use \code{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.
#' @param 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 \code{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 \code{linetype_column}. You can do this by
#' checking, e.g., \code{unique(CT$Inhibitor)} if your ct_dataframe was named
#' "CT" and the column you set for \code{linetype_column} was "Inhibitor". To
#' see possible line types by name, please enter
#' \code{ggpubr::show_line_types()} into the console.
#' @param 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.
#' @param 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.
#' @param legend_label_linetype optionally indicate on the legend something
#' explanatory about what the line types represent. For example, if
#' \code{linetype_column = Inhibitor} and \code{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
#' \code{linetype_column}. If you don't want a label for this legend item, set
#' this to "none".
#' @param 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., \code{facet1_column =
#' Tissue}. If \code{floating_facet_scale} is FALSE and you haven't specified
#' \code{facet_ncol} or \code{facet_nrow}, then \code{facet1_column} will
#' designate the rows of the output graphs.
#' @param 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., \code{facet2_column =
#' CompoundID}. If \code{floating_facet_scale} is FALSE and you haven't
#' specified \code{facet_ncol} or \code{facet_nrow}, then
#' \code{facet2_column} will designate the columns of the output graphs.
#' @param facet1_title optionally specify a title to describe facet 1. This is
#' ignored if \code{floating_facet_scale} is TRUE or if you have specified
#' \code{facet_ncol} or \code{facet_nrow}.
#' @param facet2_title optionally specify a title to describe facet 2. This is
#' ignored if \code{floating_facet_scale} is TRUE or if you have specified
#' \code{facet_ncol} or \code{facet_nrow}.
#' @param 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 \code{facet1_column} and/or \code{facet2_column}.
#' @param 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 \code{facet1_column} and/or \code{facet2_column}.
#' @param 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.
#' @param 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. \code{facet_spacing = 2}. (Reminder: Numeric data
#' should not be in quotes.)
#' @param time_range time range to display. Options: \describe{
#'
#' \item{NA}{entire time range of data; default}
#'
#' \item{a start time and end time in hours}{only data in that time range,
#' e.g. \code{c(24, 48)}. Note that there are no quotes around numeric data.}
#'
#' \item{"first dose"}{only the time range of the first dose}
#'
#' \item{"last dose"}{only the time range of the last dose}
#'
#' \item{"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}
#'
#' \item{a specific dose number with "dose" or "doses" as the prefix}{the time
#' range encompassing the requested doses, e.g., \code{time_range = "dose 3"}
#' for the 3rd dose or \code{time_range = "doses 1 to 4"} for doses 1 to 4}
#'
#' \item{"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.}
#'
#' \item{"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.} }
#'
#' @param 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.
#' @param x_axis_label optionally supply a character vector or an expression to
#' use for the x axis label
#' @param 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 \emph{exactly} at the end of the time range
#' specified. If you want a \emph{specific} amount of x-axis padding, set this
#' to a number; the default is \code{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.
#' @param pad_y_axis optionally add a smidge of padding to the y axis (default
#' is TRUE, which includes some generally reasonable padding). As with
#' \code{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
#' \emph{specific} amount of y-axis padding, set this to a number; the default
#' is \code{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.
#' @param y_axis_limits_lin Optionally set the Y axis limits for the linear
#' plot, e.g., \code{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 \code{linear_or_log}.
#' @param y_axis_limits_log Optionally set the Y axis limits for the semi-log
#' plot, e.g., \code{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 \code{linear_or_log}.
#' @param 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.
#' @param y_axis_label optionally supply a character vector or an expression to
#' use for the y axis label
#' @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 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 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.)
#' @param graph_title optionally specify a title that will be centered across
#' your graph or set of graphs
#' @param 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.
#' @param 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.
#' @param 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, \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, and \code{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".
#' @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".
#' 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.
#' @param fig_height figure height in inches
#' @param fig_width figure width in inches
#' @param normalize_by_dose TRUE or FALSE (default) for whether to show
#' dose-normalized concentration-time profiles
#' @param indiv_on_top TRUE (default) or FALSE for whether to show the
#' individual observed data on top of the mean observed data. If FALSE, the
#' mean data will be on top.
#' @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.
#'
#' @return returns a ggplot2 graph
#' @export
#'
#' @examples
#' # None yet
#'
ct_plot_obs <- function(ct_dataframe,
mean_type = "arithmetic",
nominal_times = NA,
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 = 0.5,
connect_obs_points = TRUE,
indiv_on_top = FALSE,
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,
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",
name_clinical_study = NA,
graph_labels = TRUE,
graph_title = NA,
graph_title_size = 14,
legend_position = NA,
legend_orientation = NA,
prettify_compound_names = TRUE,
save_graph = NA,
fig_height = NA,
fig_width = NA){
# 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(nrow(ct_dataframe) == 0){
stop("Please check your input. The data.frame you supplied for ct_dataframe doesn't have any rows.",
call. = FALSE)
}
# If they didn't include the column "Simulated" and they're using this
# particular function, that's probably b/c all of the data are observed.
if("Simulated" %in% names(ct_dataframe) == FALSE){
ct_dataframe$Simulated <- FALSE
}
# Main body of function -------------------------------------------------
# Setting things up for nonstandard evaluation
facet1_column <- rlang::enquo(facet1_column)
facet2_column <- rlang::enquo(facet2_column)
colorBy_column <- rlang::enquo(colorBy_column)
linetype_column <- rlang::enquo(linetype_column)
NSEcols <- c(as_label(facet1_column),
as_label(facet2_column),
as_label(colorBy_column),
as_label(linetype_column))
NSEcols <- NSEcols[NSEcols != "<empty>"]
ct_dataframe <- ct_dataframe %>% filter(Simulated == FALSE)
if(any(complete.cases(nominal_times))){
nominal_times <- as.numeric(nominal_times)
ct_dataframe$Time <- centerBin(ct_dataframe$Time, breaks = nominal_times)
}
if(str_detect(mean_type, "only")){
# This is when they only want to see means and not individual data. The
# easiest way to do this, I think, is to just set the obs_line_trans to 0.
mean_type <- sub("( )?only", "", mean_type)
obs_line_trans <- 0
}
if(any(c("obs mean", "obs geomean") %in% ct_dataframe$Trial) == FALSE &
mean_type != "none"){
# For calculating means, grouping by everything except conc and columns
# that would be just for one individual.
GroupingCols <- c("Compound", "CompoundID", "Inhibitor", "Simulated",
"Tissue", "Time", "Time_units", "Conc_units",
"Dose_sub", "Dose_inhib", "Dose_inhib2",
"InfDuration_sub", "InfDuration_inhib",
"InfDuration_inhib2", "Dose_units", "DoseNum",
"ObsFile", "Period", "Species", NSEcols)
suppressMessages(
CTagg <- ct_dataframe %>%
group_by(across(.cols = any_of(GroupingCols))) %>%
summarize(Conc = switch(mean_type,
"arithmetic" = mean(Conc, na.rm = T),
"geometric" = gm_mean(Conc),
"median" = median(Conc, na.rm = T))) %>%
mutate(Trial = switch(mean_type,
"arithmetic" = "mean",
"geometric" = "geomean",
"median" = "median"))
)
ct_dataframe <- bind_rows(ct_dataframe, CTagg)
}
# DoseNum column might not be present. Setting to NA in that case.
if("DoseNum" %in% names(ct_dataframe) == FALSE){
ct_dataframe$DoseNum <- NA
}
# Compound column might not be present. Setting to "UNKNOWN COMPOUND" in that
# case.
if("Compound" %in% names(ct_dataframe) == FALSE){
ct_dataframe$Compound <- "UNKNOWN COMPOUND"
}
# Need no NA values here
ct_dataframe$Compound[is.na(ct_dataframe$Compound)] <- "UNKNOWN COMPOUND"
# Including hacks to make this work
ct_plot_overlay(ct_dataframe %>%
mutate(Tissue_subtype = NA,
File = ObsFile,
Simulated = ifelse(
Trial %in% c("mean", "geomean", "median"), TRUE, FALSE)),
normalize_by_dose = normalize_by_dose,
colorBy_column = !!colorBy_column,
linetype_column = !!linetype_column,
facet1_column = !!facet1_column,
facet1_title = facet1_title,
facet2_column = !!facet2_column,
facet2_title = facet2_title,
obs_to_sim_assignment = NA,
mean_type = mean_type,
figure_type = "means only",
linear_or_log = linear_or_log,
color_labels = color_labels,
legend_label_color = legend_label_color,
color_set = color_set,
obs_shape = obs_shape,
obs_color = obs_color,
obs_size = obs_size,
obs_fill_trans = obs_fill_trans,
obs_line_trans = obs_line_trans,
obs_on_top = indiv_on_top,
connect_obs_points = connect_obs_points,
include_errorbars = include_errorbars,
errorbar_width = errorbar_width,
linetype_labels = linetype_labels,
linetypes = linetypes,
line_width = line_width,
line_transparency = line_transparency,
legend_label_linetype = legend_label_linetype,
facet_ncol = facet_ncol,
facet_nrow = facet_nrow,
floating_facet_scale = floating_facet_scale,
facet_spacing = facet_spacing,
time_range = time_range,
x_axis_interval = x_axis_interval,
x_axis_label = x_axis_label,
pad_x_axis = pad_x_axis,
pad_y_axis = pad_y_axis,
y_axis_limits_lin = y_axis_limits_lin,
y_axis_limits_log = y_axis_limits_log,
y_axis_interval = y_axis_interval,
y_axis_label = y_axis_label,
hline_position = hline_position,
hline_style = hline_style,
vline_position = vline_position,
vline_style = vline_style,
name_clinical_study = name_clinical_study,
graph_labels = graph_labels,
graph_title = graph_title,
graph_title_size = graph_title_size,
legend_position = legend_position,
legend_orientation = legend_orientation,
prettify_compound_names = prettify_compound_names,
qc_graph = FALSE,
existing_exp_details = NA,
save_graph = save_graph,
fig_height = fig_height,
fig_width = fig_width)
}
shirewoman2/Consultancy documentation built on Feb. 18, 2025, 10 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions ct_plot_obs
Documented in ct_plot_obs
#' Plot observed concentration-time data
#'
#' \code{ct_plot_obs} is meant to be used in conjunction with
#' \code{\link{extractObsConcTime}} or \code{\link{extractObsConcTime_mult}} to
#' create single graphs with overlaid observed concentration-time data.
#'
#' @param ct_dataframe the input concentration-time data generated by running
#' the function \code{\link{extractConcObsTime_mult}} or
#' \code{\link{extractObsConcTime}}. Not quoted.
#' @param mean_type plot "arithmetic" (default) or "geometric" mean
#' concentrations or "median" concentrations as the main (thickest or only)
#' line for each data set. If you don't want to show the means, set this to
#' "none". If you \emph{only} want to show the mean data and not the
#' individual, set this to whatever mean type you want plus "only", e.g.,
#' \code{"arithmetic only"} to show only the arithmetic mean data and none of
#' the individuals. If you haven't included mean data in \code{ct_dataframe},
#' the mean will be calculated for you using our best guess as to what the
#' groups should be. In other words, you may want to calculate the means
#' yourself just to be sure you're getting what you expect. Talk to Laura Sh.
#' if you would like help here.
#' @param nominal_times optionally specify what the nominal times were for the
#' concentration-time data. If left as NA, when we calculate the means, we'll
#' group the data by the times \emph{exactly} as listed in the individual
#' data. If you know that the nominal times were, say, 0, 0.5, 1, 2, and 4
#' hours but that the times listed in the data were the exact draw times, then
#' supply a numeric vector like this: \code{nominal_times = c(0, 0.5, 1, 2,
#' 4)} and we will match each individual time with the nominal time closest to
#' it. This way, you'll get data from a draw time at, say, 4.1 h grouped with
#' another draw time at, say, 3.95 h; they'll both be put into the 4-hour bin.
#' @param linear_or_log the type of graph to be returned. Options: \describe{
#' \item{"semi-log"}{y axis is log transformed; this is the default}
#'
#' \item{"linear"}{no axis transformation}
#'
#' \item{"both vertical"}{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 stacked horizontally}}
#' @param colorBy_column (optional) the column in \code{ct_dataframe} that
#' should be used for determining which color lines and/or points will be.
#' This should be unquoted, e.g., \code{colorBy_column = Tissue}.
#' @param color_labels optionally specify a character vector for how you'd like
#' the labels for whatever you choose for \code{colorBy_column} to show up in
#' the legend. For example, use \code{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.
#' @param legend_label_color optionally indicate on the legend something
#' explanatory about what the colors represent. For example, if
#' \code{colorBy_column = File} and \code{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 \code{colorBy_column}. If you don't want a
#' label for this legend item, set this to "none".
#' @param color_set the set of colors to use. Options: \describe{
#'
#' \item{"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.}
#'
#' \item{"Brewer set 1"}{colors selected from the Brewer palette "set 1". The
#' first three colors are red, blue, and green.}
#'
#' \item{"ggplot2 default"}{the default set of colors used in ggplot2 graphs
#' (ggplot2 is an R package for graphing.)}
#'
#' \item{"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 \emph{not} very useful
#' when you only need a couple of colors.}
#'
#' \item{"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.}
#'
#' \item{"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.}
#'
#' \item{"Tableau"}{uses the standard Tableau palette; requires the "ggthemes"
#' package}
#'
#' \item{"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}
#'
#' \item{a character vector of colors}{If you'd prefer to set all the colors
#' yourself to \emph{exactly} the colors you want, you can specify those
#' colors here. An example of how the syntax should look: \code{color_set =
#' c("dodgerblue3", "purple", "#D8212D")} or, if you want to specify exactly
#' which item in \code{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: \code{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
#' \link{colorRampPalette}.}}
#'
#' @param 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:
#' \code{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 \code{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.
#' @param obs_size optionally specify the size of the points to use for the
#' observed data. If left as NA, the size will be 2.
#' @param 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
#' \code{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: \code{obs_color =
#' "darkorchid4"}. Hex color codes are also ok to use, and setting this to
#' "none" will remove observed data from the graph.
#' @param 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
#' \code{obs_color} and when \code{obs_shape} is a shape that has a fill
#' (example: \code{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.
#' @param obs_line_trans 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), and the default is 0.5 for 50%
#' transparent.
#' @param connect_obs_points TRUE (default) or FALSE for whether to add
#' connecting lines between observed data points from the same individual
#' @param 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.
#' @param 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.
#' @param linetype_column the column in \code{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 \code{linetype_column} is set
#' to \code{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 \emph{is} present. You can set which types of lines to
#' use with the argument \code{linetypes} and you can set which shapes of
#' points you want with the argument \code{obs_shape}.
#' @param linetype_labels optionally specify a character vector for how you'd
#' like the labels for whatever you choose for \code{linetype_column} to show
#' up in the legend. For example, use \code{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.
#' @param 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 \code{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 \code{linetype_column}. You can do this by
#' checking, e.g., \code{unique(CT$Inhibitor)} if your ct_dataframe was named
#' "CT" and the column you set for \code{linetype_column} was "Inhibitor". To
#' see possible line types by name, please enter
#' \code{ggpubr::show_line_types()} into the console.
#' @param 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.
#' @param 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.
#' @param legend_label_linetype optionally indicate on the legend something
#' explanatory about what the line types represent. For example, if
#' \code{linetype_column = Inhibitor} and \code{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
#' \code{linetype_column}. If you don't want a label for this legend item, set
#' this to "none".
#' @param 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., \code{facet1_column =
#' Tissue}. If \code{floating_facet_scale} is FALSE and you haven't specified
#' \code{facet_ncol} or \code{facet_nrow}, then \code{facet1_column} will
#' designate the rows of the output graphs.
#' @param 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., \code{facet2_column =
#' CompoundID}. If \code{floating_facet_scale} is FALSE and you haven't
#' specified \code{facet_ncol} or \code{facet_nrow}, then
#' \code{facet2_column} will designate the columns of the output graphs.
#' @param facet1_title optionally specify a title to describe facet 1. This is
#' ignored if \code{floating_facet_scale} is TRUE or if you have specified
#' \code{facet_ncol} or \code{facet_nrow}.
#' @param facet2_title optionally specify a title to describe facet 2. This is
#' ignored if \code{floating_facet_scale} is TRUE or if you have specified
#' \code{facet_ncol} or \code{facet_nrow}.
#' @param 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 \code{facet1_column} and/or \code{facet2_column}.
#' @param 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 \code{facet1_column} and/or \code{facet2_column}.
#' @param 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.
#' @param 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. \code{facet_spacing = 2}. (Reminder: Numeric data
#' should not be in quotes.)
#' @param time_range time range to display. Options: \describe{
#'
#' \item{NA}{entire time range of data; default}
#'
#' \item{a start time and end time in hours}{only data in that time range,
#' e.g. \code{c(24, 48)}. Note that there are no quotes around numeric data.}
#'
#' \item{"first dose"}{only the time range of the first dose}
#'
#' \item{"last dose"}{only the time range of the last dose}
#'
#' \item{"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}
#'
#' \item{a specific dose number with "dose" or "doses" as the prefix}{the time
#' range encompassing the requested doses, e.g., \code{time_range = "dose 3"}
#' for the 3rd dose or \code{time_range = "doses 1 to 4"} for doses 1 to 4}
#'
#' \item{"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.}
#'
#' \item{"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.} }
#'
#' @param 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.
#' @param x_axis_label optionally supply a character vector or an expression to
#' use for the x axis label
#' @param 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 \emph{exactly} at the end of the time range
#' specified. If you want a \emph{specific} amount of x-axis padding, set this
#' to a number; the default is \code{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.
#' @param pad_y_axis optionally add a smidge of padding to the y axis (default
#' is TRUE, which includes some generally reasonable padding). As with
#' \code{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
#' \emph{specific} amount of y-axis padding, set this to a number; the default
#' is \code{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.
#' @param y_axis_limits_lin Optionally set the Y axis limits for the linear
#' plot, e.g., \code{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 \code{linear_or_log}.
#' @param y_axis_limits_log Optionally set the Y axis limits for the semi-log
#' plot, e.g., \code{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 \code{linear_or_log}.
#' @param 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.
#' @param y_axis_label optionally supply a character vector or an expression to
#' use for the y axis label
#' @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 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 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.)
#' @param graph_title optionally specify a title that will be centered across
#' your graph or set of graphs
#' @param 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.
#' @param 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.
#' @param 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, \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, and \code{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".
#' @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".
#' 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.
#' @param fig_height figure height in inches
#' @param fig_width figure width in inches
#' @param normalize_by_dose TRUE or FALSE (default) for whether to show
#' dose-normalized concentration-time profiles
#' @param indiv_on_top TRUE (default) or FALSE for whether to show the
#' individual observed data on top of the mean observed data. If FALSE, the
#' mean data will be on top.
#' @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.
#'
#' @return returns a ggplot2 graph
#' @export
#'
#' @examples
#' # None yet
#'
ct_plot_obs <- function(ct_dataframe,
mean_type = "arithmetic",
nominal_times = NA,
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 = 0.5,
connect_obs_points = TRUE,
indiv_on_top = FALSE,
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,
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",
name_clinical_study = NA,
graph_labels = TRUE,
graph_title = NA,
graph_title_size = 14,
legend_position = NA,
legend_orientation = NA,
prettify_compound_names = TRUE,
save_graph = NA,
fig_height = NA,
fig_width = NA){
# 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(nrow(ct_dataframe) == 0){
stop("Please check your input. The data.frame you supplied for ct_dataframe doesn't have any rows.",
call. = FALSE)
}
# If they didn't include the column "Simulated" and they're using this
# particular function, that's probably b/c all of the data are observed.
if("Simulated" %in% names(ct_dataframe) == FALSE){
ct_dataframe$Simulated <- FALSE
}
# Main body of function -------------------------------------------------
# Setting things up for nonstandard evaluation
facet1_column <- rlang::enquo(facet1_column)
facet2_column <- rlang::enquo(facet2_column)
colorBy_column <- rlang::enquo(colorBy_column)
linetype_column <- rlang::enquo(linetype_column)
NSEcols <- c(as_label(facet1_column),
as_label(facet2_column),
as_label(colorBy_column),
as_label(linetype_column))
NSEcols <- NSEcols[NSEcols != "<empty>"]
ct_dataframe <- ct_dataframe %>% filter(Simulated == FALSE)
if(any(complete.cases(nominal_times))){
nominal_times <- as.numeric(nominal_times)
ct_dataframe$Time <- centerBin(ct_dataframe$Time, breaks = nominal_times)
}
if(str_detect(mean_type, "only")){
# This is when they only want to see means and not individual data. The
# easiest way to do this, I think, is to just set the obs_line_trans to 0.
mean_type <- sub("( )?only", "", mean_type)
obs_line_trans <- 0
}
if(any(c("obs mean", "obs geomean") %in% ct_dataframe$Trial) == FALSE &
mean_type != "none"){
# For calculating means, grouping by everything except conc and columns
# that would be just for one individual.
GroupingCols <- c("Compound", "CompoundID", "Inhibitor", "Simulated",
"Tissue", "Time", "Time_units", "Conc_units",
"Dose_sub", "Dose_inhib", "Dose_inhib2",
"InfDuration_sub", "InfDuration_inhib",
"InfDuration_inhib2", "Dose_units", "DoseNum",
"ObsFile", "Period", "Species", NSEcols)
suppressMessages(
CTagg <- ct_dataframe %>%
group_by(across(.cols = any_of(GroupingCols))) %>%
summarize(Conc = switch(mean_type,
"arithmetic" = mean(Conc, na.rm = T),
"geometric" = gm_mean(Conc),
"median" = median(Conc, na.rm = T))) %>%
mutate(Trial = switch(mean_type,
"arithmetic" = "mean",
"geometric" = "geomean",
"median" = "median"))
)
ct_dataframe <- bind_rows(ct_dataframe, CTagg)
}
# DoseNum column might not be present. Setting to NA in that case.
if("DoseNum" %in% names(ct_dataframe) == FALSE){
ct_dataframe$DoseNum <- NA
}
# Compound column might not be present. Setting to "UNKNOWN COMPOUND" in that
# case.
if("Compound" %in% names(ct_dataframe) == FALSE){
ct_dataframe$Compound <- "UNKNOWN COMPOUND"
}
# Need no NA values here
ct_dataframe$Compound[is.na(ct_dataframe$Compound)] <- "UNKNOWN COMPOUND"
# Including hacks to make this work
ct_plot_overlay(ct_dataframe %>%
mutate(Tissue_subtype = NA,
File = ObsFile,
Simulated = ifelse(
Trial %in% c("mean", "geomean", "median"), TRUE, FALSE)),
normalize_by_dose = normalize_by_dose,
colorBy_column = !!colorBy_column,
linetype_column = !!linetype_column,
facet1_column = !!facet1_column,
facet1_title = facet1_title,
facet2_column = !!facet2_column,
facet2_title = facet2_title,
obs_to_sim_assignment = NA,
mean_type = mean_type,
figure_type = "means only",
linear_or_log = linear_or_log,
color_labels = color_labels,
legend_label_color = legend_label_color,
color_set = color_set,
obs_shape = obs_shape,
obs_color = obs_color,
obs_size = obs_size,
obs_fill_trans = obs_fill_trans,
obs_line_trans = obs_line_trans,
obs_on_top = indiv_on_top,
connect_obs_points = connect_obs_points,
include_errorbars = include_errorbars,
errorbar_width = errorbar_width,
linetype_labels = linetype_labels,
linetypes = linetypes,
line_width = line_width,
line_transparency = line_transparency,
legend_label_linetype = legend_label_linetype,
facet_ncol = facet_ncol,
facet_nrow = facet_nrow,
floating_facet_scale = floating_facet_scale,
facet_spacing = facet_spacing,
time_range = time_range,
x_axis_interval = x_axis_interval,
x_axis_label = x_axis_label,
pad_x_axis = pad_x_axis,
pad_y_axis = pad_y_axis,
y_axis_limits_lin = y_axis_limits_lin,
y_axis_limits_log = y_axis_limits_log,
y_axis_interval = y_axis_interval,
y_axis_label = y_axis_label,
hline_position = hline_position,
hline_style = hline_style,
vline_position = vline_position,
vline_style = vline_style,
name_clinical_study = name_clinical_study,
graph_labels = graph_labels,
graph_title = graph_title,
graph_title_size = graph_title_size,
legend_position = legend_position,
legend_orientation = legend_orientation,
prettify_compound_names = prettify_compound_names,
qc_graph = FALSE,
existing_exp_details = NA,
save_graph = save_graph,
fig_height = fig_height,
fig_width = fig_width)
}
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