## Overview

As of version 2, Pmetrics uses the plotly package for most plots. Plots made with plotly are interactive, so that moving your cursor over them results in pop up information being displayed. A nice feature of plotly is the ggplotly command which takes any ggplot plot object and turns it into a plotly plot. This route doesn’t provide complete control of plotly objects, so Pmetrics uses plotly from the ground up.

However, the documentation for plotly is a bit complex and often lacking in sufficient examples. This vignette attempts to orient you to the specific aspects of plotly most relevant to Pmetrics.

## General

Plotly is based on a foundation of lists. Most plot elements are lists whose arguments control characteristics of the elements. Pmetrics chiefly uses only a handful of the elements.

Full descriptions of plotly elements and options to control them can be accessed by typing plotly::schema() into the R console. If you have already loaded plotly you can just type schema(). You will see something like the menu below in the Viewer tab in Rstudio.

In plotly, the traces items control appearance of the data. The layout items control everything to do with the appearance of the axes and the legend.

## Traces

Pmetrics almost exclusively uses scatter traces, and those elements can be referenced by expanding traces ▶ scatter ▶ attributes.

There are many such attributes, but the most commonly used and accessible to the user are line and marker.

### Line attributes

All plotly plots in Pmetrics have a line argument which maps to the line attribute of a plotly scatter trace. When Pmetrics detects a boolean argument, e.g. TRUE or FALSE it either plots the default line or suppresses the line altogether. A good example of this is the line argument to plot.PM_op to generate an observed vs. predicted plot. In this function, line is a list of named lines that can be plotted. If NPex is a PM_result object loaded with PM_load() then the default is NPex$plot$op(line = list(lm = F, loess = T, ref = T)).This means that a linear model (regression) will not be plotted, a loess regression will be plotted, and a reference line with slope = 1 and intercept = 0 will also be plotted. If we are happy with all the defaults, we simply need to call the plot method for the PM_op object field in NPex.

NPex$op$plot()

Below is a simple example of how to change the default lines plotted, but retain the default formatting. Now a linear regression (using the R lm function) is plotted with a reference line, but not a loess regression.

NPex$op$plot(line = list(lm = T, loess = F, ref = T))

The default formats for lines may differ from function to function and are indicated in the help files. However all lines may be formatted using three arguments as a list.

• color Specify the color of the line as a character with either the
• name: a good reference in R is to use the colors() function, e.g. “dodgerblue”
• hexadecimal code: this can be searched on the web, and is specified as a character with leading “#” followed by 6 hexidecimal digits, two each for Red, Green, Blue, e.g. “#C0C0C0”. Adding an extra two digits specifies transparency, with 00 fully transparent and FF fully opaque, e.g. #C0C0C066”.
• a plotly function toRGB which takes a color name and alpha (transparency) value on a scale of 0 - 1, e.g., toRGB("red", 0.5).
• width Units are pixels.
• dash Sets the dash style of lines. Set to a dash type string (solid,dot, dash, longdash, dashdot, or longdashdot) or a dash length list in px (eg 5px,10px,2px,2px)
NPex$op$plot(line = list(loess = list(color = "orange", dash = "dashdot", width = 2)))

Note the default is lm = F and ref = T, so we don’t need to specify them if that’s what we want.

### Marker attributes

All plotly plots in Pmetrics have a marker argument which maps to the marker attribute of a plotly scatter trace. When Pmetrics detects a boolean argument, e.g. TRUE or FALSE it either plots the default marker or suppresses the marker altogether. A good example of this is also the marker argument to plot.PM_op to generate an observed vs. predicted plot. In this function, marker controls the plotting symbol. If NPex is a PM_result object loaded with PM_load() then the default is NPex$plot$op(marker = T).This means that the symbols will be plotted with default formatting. If we are happy with all the defaults, we simply need to call the plot method for the PM_op object field in NPex.

NPex$op$plot()

The default formats for markers may differ from function to function and are indicated in the help files. However all markers may be formatted using most commonly four arguments as a list.

• color Specify the color of the marker fill as a character with either the
• name: a good reference in R is to use the colors() function, e.g. “dodgerblue”
• hexadecimal code: this can be searched on the web, and is specified as a character with leading “#” followed by 6 hexidecimal digits, two each for Red, Green, Blue, e.g. “#C0C0C0”. Adding an extra two digits specifies transparency, with 00 fully transparent and FF fully opaque, e.g. #C0C0C066”.
• a plotly function toRGB which takes a color name and alpha (transparency) value on a scale of 0 - 1, e.g., toRGB("red", 0.5).
• size Units are points.
• symbol Either a character name or the numeric value associated with the character. See schema() traces ▶ scatter ▶ attributes ▶ marker ▶ symbol ▶ values for all the possibilities, e.g. “circle-open” is equivalent to 100, “square” is equivalent to 1, and “diamond-open-dot” is equivalent to 302.
• opacity On a scale of 0 (transparent) to 1 (opaque).

Any other marker attribute is possible, including line, which itself is a list to specify attributes of the outline for a marker.

NPex$op$plot(marker = list(color = "slategray", size = 12,
opacity = 0.5, line = list(color = "navy")))

## Layout

Layout layout ▶ layoutAttributes ▶ displays all the options for the layout. Pmetrics plot functions currently only provide access to the following layout options: * legend * xaxis * yaxis * title Details of how each is accessed are below.

### Legend

In all relevant Pmetrics plotly plots, the argument for legend can take one of three forms: TRUE, FALSE, or a list. The first and second options choose to include the default legend or to suppress the legend, respectively.

Specifying legend as a list allows for detailed formatting of the legend using any of the attributes documented by typing schema() into the R console, and expanding layout ▶ layoutAttributes ▶ legend. These can be included as elements in the legend list.

Here’s a basic legend.

NPex$data$plot(legend = T)

…and a more complex one.

NPex$data$plot(line = list(join = F,
pred = list(NPex$post, color = "navy")), legend = list( x = 0.75, y = 0.85, borderwidth = 1, bgcolor = "antiquewhite" ), xlim = c(119, 146)) ### X axis and Y axis Several arguments in most Pmetrics plots map to plotly list elements that control the formatting of the x- and y-axes. A full list of these elements can be viewed by typing schema() into the R console and expanding layout ▶ layoutAttributes ▶ xaxis / yaxis. • log When TRUE, maps to yaxis = list(type = "log") and sets the y axis to log scale. • xlim and ylim If specified as vectors of length two, e.g. xlim = c(0, 100), map to xaxis = list(range = xlim) and the same for the y axis. • xlab and ylab If specified as character vectors of length 1, e.g. xlab = "Time", map to xaxis = list(title = list(text = xlab)) and the same for the y axis. However, unlike the simpler log, xlim, and ylim arguments, xlab and ylab can also be specified as lists to control the formatting of the axes labels. The lists follow the plotly conventions: • text The name of the axis • font The formatting of the label, itself a list: • color Color of the text • family The HTML font family, e.g. “Arial” or “Times New Roman” • size Size in points If you only specify a list for xlab it will be applied to ylab. To format them independently, specify a list for both. NPex$data$plot(xlab = "Time (h)", ylab = "Rifapentine (mg/L)") NPex$data$plot(xlab = list(text = "Time (h)", font = list(color = "slategrey", family = "Times New Roman", size = 18))) #notice that ylab is also formatted in the same way For greater control over the axes, e.g. tick placement, coloring, etc., the “…” argument in plots is coded to pass arguments to the xaxis and yaxis lists. This allows access to any attribute. If axis attributes are passed through … in a named list, e.g. xaxis = list(linecolor = "red"), then they will be specific to that axis. However, if the attributes are passed through … directly, without inclusion in an xaxis or yaxis list, they will apply to both the axes, e.g. $plot(...,linecolor = “red”) will color the xaxis and yaxis lines red.

NPex$data$plot(xaxis = list(linecolor = "firebrick", ticks = "inside"))
#only affects x axis since formatting within xaxis list
NPex$data$plot(linecolor = "dodgerblue", ticks = "outside")
#formatting affects both axes since arguments are outside xaxis or yaxis lists

### Title

All Pmetrics plotly plots have a title argument which maps to the attributes controlling the text and formatting of the plot title. A full list of these elements can be viewed by typing schema() into the R console and expanding layout ▶ layoutAttributes ▶ title. These are the same as for the axes titles, i.e. text and font, plus additional attributes to control the positioning of the title. The most common will be x to control the horizontal placement from 0 (left) to 1 (right) of the plot, and y to control the vertical placement from 0 (bottom) to 1 (top). The default for x is 0.5 (middle) and for y it is “auto”, which puts the bottom of the title text onto the vertical center of the top margin.

By default the title text is oriented around the x value automatically (“auto”) via the xanchor attribute, but this could also be “left”, “right”, or “center” to make the text begin at, end at, or span the x value, respectively. Analogous behavior can be controlled by the yanchor attribute for which part of the text lines up with the y value: “top”, “middle”, or “bottom”. The default is “auto”.

NPex$data$plot(title = list(text = "Rifapentine", yanchor = "top"), ylab = "Concentration (mg/L)", xlab = "Time (h)")

## Shapes

Shapes in plotly are part of the layout and detailed by typing schema() into the R console and expanding layout ▶ layoutAttributes ▶ shapes ▶ shape. Oddly, there is no native ability in R plotly to add shapes to an existing plot, so Pmetrics has a custom function add_shapes which does the job.

The workflow is to first create a plot. All Pmetrics functions return the plot object so you can either assign the plot call to a variable, e.g. p <- plot(...) or use the current plot via plotly’s last_plot function, which is the default value for add_shapes.

Since reference lines are by far the most common shape to add, Pmetrics has a helper function called ab_line, which is very similar to the workflow in base R that uses the abline function to add a line to the current plot.

NPex$data$plot() %>%
add_shapes(ab_line(h = 5, line = list(color = "green", width = 3)))

Note the use of the pipe operator %>% to pipe the plot directly into the add_shapes function. This is not mandatory, since add_shapes by default takes the current plot as its target. If you have a previously saved plot in a variable, you can add a shape any time by supplying that variable to add_shapes, for example add_shapes(p, ab_line(h = 5)).

It is possible to add other shapes, but it will require more manual effort as there are no Pmetrics helper functions to add circles, rectangles, or paths. Here’s an example of adding a circle.

NPex$data$plot() %>%
add_shapes(shape = list(type = "circle", x0 = 122, x1 = 132,
y0 = 15, y1 = 22))

The values for x0 and x1 determine the left and right extent, and for y0 and y1 the bottom and top of the circle. Coordinates are absolute, unless you include xref = "paper" and/or yref = "paper" An additional line list can control formatting.

## Annotations

Here plotly does provide two functions to add annotations to plots as labels or as floating text.

plotly::add_annotations() will add labels to data, allowing for connection between the data point and the label by an arrow. The options are detailed by typing ?add_annotations into the R console. However, rather than following the hyperlink there to get additional information on options, you can get more help by typing schema() into the R console and expanding layout ▶ layoutAttributes ▶ annotations ▶ items ▶annotation.

Similarly, plotly::add_text() adds labels/text to a plot, but without the connecting line. The options are detailed by typing ?add_text into the R console. You can get more options by typing schema() into the R console and expanding traces ▶ scatter ▶ attributes ▶ text, textfont, and textposition. The others (textpositionsrc, textsrc, texttemplate, textemplatesrc) are more obscure and not needed.

Annotations with connecting arrows…

NPex$data$plot() %>%
add_annotations(text = ~id) #the tilde tells plotly to use the contents of the id column

Text without arrows…

NPex$data$plot() %>%
add_text(text = ~id, textposition = "top right",
textfont = list(color = "green", size = 12))

A single text item…

NPex$data$plot() %>%
add_text(x = 125, y = 2, text = "<b>Limit of quantification</b>", textfont = list(size = 10)) %>%
add_shapes(shape = ab_line(h = 1.8))

Notice how the tidyverse pipe %>% lets you string together commands. Tidyverse is a powerful set of libraries for R that permit methodical, advanced data manipulation. You can load it with library(tidyverse). In the plot below, we return to add_annotations to demonstrate that it gives you more control over the formatting. Generally, this function is likely the more useful to add text pop ups or to label data. Notice the use of the html tags to surround the text and make it bold. You can also use <i></i> for italics, but plotly does not support <u></u> for underline.

NPex$data$plot() %>%
add_annotations(x = 125, y = 2, text = "<i>Limit of quantification</i>", font = list(size = 10), showarrow = F, bgcolor = "antiquewhite", bordercolor = "black") %>%
add_shapes(shape = ab_line(h = 1.8))`

## Exporting

### Images

Although the plotly team is working on methods to export plots to images via scripting, they require installation of additional components and are not yet stable. However, exporting a plotly plot to a static image can be accomplished in two ways.

The first is to select the Export button from the Viewer window in Rstudio when the desired plot is displayed. This gives the option to save the plot as a .png, .jpeg, or .tiff. You cannot save as a .pdf with this method.

The second option is to click the camera icon that appears in the top right of a plotly plot when you mouse over the plot.

This gives you the option to download and save the file as a .png.

### PDFs

To export to a .pdf, open the plot in a browser.

From there you should be able to export the image as .pdf file.