---
title: "Visualizations with `DataCombined`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Visualizations with `DataCombined`}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
# Evaluate the runtime chunks only when the OSPSuite .NET runtime initialised
# successfully (native libraries + .NET). On machines without a working runtime
# the code is shown but not executed, so the vignette still renders.
.ospRuntimeAvailable <- isTRUE(tryCatch(
  requireNamespace("ospsuite", quietly = TRUE) &&
    ospsuite::getOSPSuiteSetting("initialized"),
  error = function(e) FALSE
))
knitr::opts_chunk$set(
  eval = .ospRuntimeAvailable,
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  comment = "#>",
  out.width = "100%",
  fig.height = 6,
  fig.width = 8,
  fig.showtext = TRUE
)
```

## Introduction

You have already seen how the `DataCombined` class can be utilized to store observed and/or simulated data (if not, read [Working with `DataCombined` class](data-combined.html)).

Let's first create a `DataCombined` object, which we will use to demonstrate different visualizations available.

```{r}
library(ospsuite)

# simulated data
simFilePath <- system.file("extdata", "Aciclovir.pkml", package = "ospsuite")
sim <- loadSimulation(simFilePath)
simResults <- runSimulations(sim)[[1]]
outputPath <- "Organism|PeripheralVenousBlood|Aciclovir|Plasma (Peripheral Venous Blood)"

# observed data
obsData <- lapply(
  c("ObsDataAciclovir_1.pkml", "ObsDataAciclovir_2.pkml", "ObsDataAciclovir_3.pkml"),
  function(x) loadDataSetFromPKML(system.file("extdata", x, package = "ospsuite"))
)
names(obsData) <- lapply(obsData, function(x) x$name)

myDataCombined <- DataCombined$new()

myDataCombined$addSimulationResults(
  simulationResults = simResults,
  quantitiesOrPaths = outputPath,
  groups = "Aciclovir PVB"
)

myDataCombined$addDataSets(
  obsData$`Vergin 1995.Iv`,
  groups = "Aciclovir PVB"
)
```

## Time profile plots

Time profile plots visualize measured or simulated values against time and help 
assess if the observed data (represented by symbols and error bars) match the 
simulated data (represented by lines).

```{r}
plotIndividualTimeProfile(myDataCombined)
```

## Observed versus simulated scatter plot 

Observed versus simulated plots allow to assess how far simulated results 
are from observed values.

```{r}
plotObservedVsSimulated(myDataCombined)
```

The identity line represents perfect correspondence of simulated values with the 
observed ones. By default, a "two-fold" range is marked by the dashed lines. 
The "x-fold" range is defined as values that are `x`-fold higher and `1/x`-fold 
lower than the observed ones. The user can specify multiple ranges by the `foldDistance` 
argument.

```{r}
plotObservedVsSimulated(myDataCombined, foldDistance = c(1.3, 2))
```

## Residuals versus time or vs simulated scatter plot

Residual plots show if there is a systematic bias in simulated values either in 
high-concentration or low-concentration regions, or, alternatively, in early or 
late time periods.

```{r}
plotResidualsVsSimulated(myDataCombined)
```


```{r}
plotResidualsVsTime(myDataCombined)
```


Residuals of log values can be visualized with the `scaling` argument.

```{r}
plotResidualsVsTime(myDataCombined, scaling = "log")
```

## Customizing plots

The look and feel for plots can be customized using the `DefaultPlotConfiguration` class, which provides various class members that can be used to modify the *appearance* of the plot.

```{r}
myPlotConfiguration <- DefaultPlotConfiguration$new()

# Define x units
myPlotConfiguration$xUnit <- ospUnits$Time$s
# Define y units
myPlotConfiguration$yUnit <- ospUnits$`Concentration [mass]`$`µg/l`
# Change y axis scaling to logarithmic
myPlotConfiguration$yAxisScale <- tlf::Scaling$log

myPlotConfiguration$title <- "Example: Customizing a Plot"
myPlotConfiguration$subtitle <- "Using DefaultPlotConfiguration class"
myPlotConfiguration$caption <- "Source: Aciclovir data"

myPlotConfiguration$legendPosition <- tlf::LegendPositions$outsideRight
```

This configuration class can be passed to all plotting functions:

```{r}
plotIndividualTimeProfile(myDataCombined, myPlotConfiguration)
```

## Creating multi-panel plots

Each of the `plotXXX()` returns a `ggplot2` object. Lets create different plots 
from the same `DataCombined` and store them as variables.

```{r}
indivProfile <- plotIndividualTimeProfile(myDataCombined, myPlotConfiguration)
obsVsSim <- plotObservedVsSimulated(myDataCombined, myPlotConfiguration)
resVsSim <- plotResidualsVsSimulated(myDataCombined)
resVsTime <- plotResidualsVsTime(myDataCombined)
```

These plots can be combined into a multi-panel figure using the `PlotGridConfiguration` 
and then used with `plotGrid()` function to create a figure.

```{r,  fig.width=10, fig.asp = 1}
plotGridConfiguration <- PlotGridConfiguration$new()
plotGridConfiguration$tagLevels <- "a"
plotGridConfiguration$title <- "Multiple plots in one figure"

plotGridConfiguration$addPlots(plots = list(indivProfile, obsVsSim, resVsSim, resVsTime))

plotGrid(plotGridConfiguration)
```

The function will try to arrange the panels such that the number of rows equals 
to the number of colums. You can also specify the number of rows or columns through 
the `PlotGridConfiguration`:

```{r,  fig.width=10, fig.asp = 1.5}
plotGridConfiguration$nColumns <- 1

plotGrid(plotGridConfiguration)
```

Check out the documentation of the `PlotGridConfiguration` class for the list of 
supported properties.

## Saving plots

All plotting functions return `ggplot` objects that can be further modified.

To save a plot to a file, use the `ExportConfiguration` object. You can edit 
various properties of the export, including the resolution, file format, 
or file name.

```{r save-plots, eval=FALSE}
# Create new export configuration
exportConfiguration <- tlf::ExportConfiguration$new()
# Define the path to the folder where the file will be stored
exportConfiguration$path <- "../OutputFigures"
# Define the name of the file
exportConfiguration$name <- "MultiPanelPlot"
# Resolution
exportConfiguration$dpi <- 600

# Store the plot into a variable and export it to a file
plotObject <- plotIndividualTimeProfile(myDataCombined)

exportConfiguration$savePlot(plotObject)
```

## Implementation details

All plotting functions in `{ospsuite}` make use of the `{tlf}` library to prepare visualizations. To know more about this library, see its [website](https://www.open-systems-pharmacology.org/TLF-Library/).