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require(tlf)
#> Loading required package: tlf

1. Introduction

1.1. Objectives

The aim of this vignette is to document and illustrate the typical workflow needed for the production of plots using the tlf-library.

1.2. Libraries

The main purpose of the tlf-library is to standardize the production of ggplot objects from data produced by the OSPSuiteR package. As such, tlf-library requires that the ggplot2 package be installed.

1.3. tlf typical workflow

The suggested workflow for producing any kind of plot with the tlf-library is illustrated in the figure below.

The standard workflow then proceeds as follows:

Step 0 - Data gathering. Gather the data into tidy data.frame format.

Step 1 - Data pre-processing Pre-process the data using AggregationSummary class.

Step 2 - Data grouping Use the GroupMapping class to specify groupings according to which the data will be captioned in figure legends.

Step 3 - Data mapping. Use the DataMapping class to select the independent and dependent variables of the processed data as well as the aesthetics that will be used to differentiate between the groupings of the data that were specified in step 2.

Step 4 - Plot configuration Set the PlotConfiguration object which will define settings of the plot such as axis labeling, font sizes, and watermarks.

Step 5 - Plot generation Create a ggplot object from the above classes using the dedicated plotting function.

Steps 1, 2, 3, and 4 are not mandatory. If they are skipped, tlf-library uses default settings in lieu of the objects created otherwise. Additionally, the PlotConfiguration object and the DataMapping object can be created independently. Sections 2 to 4 will focus on AggregationSummary, DataMapping, and PlotConfiguration.

1.4. Naming Conventions

In this package, it was chosen to use specific names for functions and classes referring to specific plots. The naming convention for classes is <Plot Name><Class> and for function <function><Plot Name>. Below presents the table of specific classes and functions that are created using this convention:

DataMapping PlotConfiguration plot
PKRatio PKRatioDataMapping PKRatioPlotConfiguration plotPKRatio
DDIRatio DDIRatioDataMapping DDIRatioPlotConfiguration plotDDIRatio
IndividualIdProfile IndividualIdProfileDataMapping IndividualIdProfilePlotConfiguration plotIndividualIdProfile
ObsVsPred ObsVsPredDataMapping ObsVsPredPlotConfiguration plotObsVsPred
Histogram HistogramDataMapping HistogramPlotConfiguration plotHistogram
BoxWhisker BoxWhiskerDataMapping BoxWhiskerPlotConfiguration plotBoxWhisker

2. Data pre-processing: AggregationSummary class

2.1. Data format

The workflow assumes that the data to be plotted has been gathered in the form of a tidy dataframe. In a tidy format dataframe, each measurement, such as a simulation result or an experimental observation, is described entirely in one row. The columns of the data.frame are limited to no more than the independent variable columns of the measurement (for example, time and IndividualId) and the dependent variable columns (in this case Organism|VenousBlood|Volume), which hold the value of the measurement. Since no additional columns are allowed, two dependent variables that have differing sets of independent variables should each have their own tidy dataframes.

In the sequel, we will use a dataset derived from the OSPSuiteR package: testData. Let’s look at a few rows to get a sense of the data:

IndividualId Gender Race Population Name Organism|Age Organism|Weight
0 Male Caucasian pop_10 14.06889 54.04230
1 Male Caucasian pop_10 23.41955 61.29773
2 Male Caucasian pop_10 24.89981 44.39078
3 Male Caucasian pop_10 30.45043 53.61099
4 Male Caucasian pop_10 22.96949 42.98250
5 Female Caucasian pop_10 37.71187 50.49205

2.2. MetaData

A metaData variable associated with the data can be used to define additional information such as the dimension and unit of each column in the data.frame. The lower limit of quantification of a IndividualId profile can also be stored in the metaData. The format of metaData is currently expected to be a list on each variable of lists showing unit and dimension.

Variable Dimension Unit
IndividualId
Gender
Race
Population Name
Organism|Age Age yrs
Organism|Weight Mass kg
Organism|BMI kg/m2
Organism|Gestational age Age week(s)
Organism|Height Length dm
Organism|Hematocrit Volume l
Organism|VenousBlood|Volume Volume l
Organism|ArterialBlood|Volume Volume l
Organism|Bone|Specific blood flow rate Flow l/min
Organism|Bone|Volume Volume l
Organism|Brain|Volume Volume l
Compound
Dose Mass mg

2.3. Aggregation

A common processing of the data is its aggregation. The aggregation consists in splitting the data into subsets, then computing summary statistics for each, and returning the result in a convenient form. Visual predictive checks are typical plots where such method is useful.

The AggregationSummary class is a helper class that simplifies the use of aggregation methods on the data. The R6 class AggregationSummary automates the computation of multiple summary statistics of the raw data produced at Step 0. The output of this optional data pre-processing step is a dataframe with a column for each summary statistic. This dataframe can be input into the subsequent steps of the workflow. The user also has the option of generating metaData for each of the summary statistics evaluated.

To illustrate the functions of this class for the example of the dataframe testData, let’s suppose that for each individual in the IndividualId column, the minimum and the mean value of the simulated Organism|VenousBlood|Volume column is to be computed for each gender in the Gender column. The AggregationSummary class works in 3 steps:

  1. Three sets of columns are selected from the input dataframe data: an independent variable set called xColumnNames (in this case, the IndividualId column in testData), a grouping variables set called groupingColumnNames (the Gender column in testData) and a dependent variables set called yColumnNames (the Organism|VenousBlood|Volume column in testData).

  2. For each value of the independent variable xColumnNames, the rows of the dataframe are aggregated into groups defined by unique combinations of the elements in the grouping variable columns groupingColumnNames.

  3. Summary statistics (in this case, the minimum and the mean) for the yColumnNames variables in each group are evaluated. The functions for computing the the summary statistics are specified when initializing an AggregationSummary, via aggregationFunctionsVector. User-specified descriptive names of these functions are supplied via the vector of strings named aggregationFunctionNames. The units and dimensions of the outputs of these functions are supplied via the vectors of strings named aggregationUnitsVector and aggregationDimensionsVector, respectively.

For this example, the AggregationSummary object aggSummary is instantiated as follows:

aggSummary <- AggregationSummary$new(
  data = testData,
  metaData = testMetaData,
  xColumnNames = "IndividualId",
  groupingColumnNames = "Gender",
  yColumnNames = "Organism|VenousBlood|Volume",
  aggregationFunctionsVector = c(min, mean),
  aggregationFunctionNames = c(
    "Simulated Min",
    "Simulated Mean"
  ),
  aggregationUnitsVector = c("l", "l"),
  aggregationDimensionsVector = c(
    "Volume",
    "Volume"
  )
)

The dataframe that holds the summary statistics of the aggregated rows is stored in the dfHelper property of the resulting aggSummary object. Since two functions (min and mean) were specified in aggregationFunctionsVector, the dataframe aggSummary$dfHelper has, in addition to the xColumnNamesand groupingColumnNames columns, two additional columns named Simulated Min and Simulated Mean, which were the names specified in aggregationFunctionNames.

head(aggSummary$dfHelper)
IndividualId Gender Simulated Min Simulated Mean
5 Female 0.6186527 0.6186527
6 Female 0.6700546 0.6700546
7 Female 0.8003464 0.8003464
8 Female 0.6001890 0.6001890
9 Female 0.7350718 0.7350718
0 Male 0.8767134 0.8767134

The metaData corresponding to the columns of the resulting dataframes are lists that are stored together in a list with the metaData of the xColumnNamesand groupingColumnNames columns. The metaData for the new aggSummary$dfHelper dataframe is stored as the metaDataHelper property of the aggSummary object. For this example, the two metaData lists corresponding to the Simulated Min and Simulated Mean columns are also are labeled Simulated Min and Simulated Mean. The contents of the list aggSummary$metaDataHelper are:

# Currently issue with metaData of Gender
aggSummary$metaDataHelper[[2]] <- NULL
aggMetaData <- data.frame(
  "unit" = sapply(aggSummary$metaDataHelper, function(x) {
    x$unit
  }),
  "dimension" = sapply(aggSummary$metaDataHelper, function(x) {
    x$dimension
  })
)

knitr::kable(aggMetaData)
unit dimension
IndividualId
Simulated Min l Volume
Simulated Mean l Volume

3. Mapping and grouping of data: DataMapping class

The role of the DataMapping class is to provide a user-friendly interface to indicate what data should be plotted. In most cases, this class needs to be initialized to map what variables are x and y, and which IndividualIds variable(s) will group the data. Thus, the most common input are x and y; however, for more advanced plots, input such as groupMapping may be used often. For advanced plots, subclasses are derived from DataMapping, they use unique input and default related to the advanced plot to make it easier to use them.

3.1. GroupMapping

3.1.1. Grouping class

An R6 class called Grouping can be used to group the data into subsets that, in the final plots, are to be distinguished both aesthetically and in legend captions. In addition, these subsets can be listed under descriptive legend titles.

As an example, a Grouping object called grouping1 can be used to specify that the data in a tidy data.frame should be grouped by both “Compound” and “Dose”:

# Grouping by variable names:
grouping1 <- Grouping$new(c("Compound", "Dose"))

With this minimal input, a legend associated with this grouping will have the default title “Compound-Dose”. On the other hand, a custom title for this grouping and its legend can be supplied by the user with the optional label input:

# Grouping by variable names and overwriting the default label:
grouping2 <- Grouping$new(group = c("Compound", "Dose"), label = "Compound & Dose")

In the above two examples, default captions are constructed by hyphenating the compound type and the dose amount for each row. Alternatively, the captions can be customized by the user by supplying a dataframe with the custom captions to the group input of the Grouping object constructor. The format of this dataframe is such that the rightmost column contains the desired captions, the name of this rightmost column is the default legend title for this grouping, and the remaining columns define the combinations of row entries that are to receive each caption in the rightmost column. To illustrate this method, the following dataframe mappingDataFrame is used to assign captions based on entries in the “Dose” and “Compound” columns. For example, the caption “6mg of Aspirin” is assigned to any row in which the “Dose” entry is 6 and the “Compound” entry is “Aspirin”.

# Grouping using a data.frame:
mappingDataFrame <- data.frame(
  Compound = c("Aspirin", "Aspirin", "Sugar", "Sugar"),
  Dose = c(6, 3, 6, 3),
  "Compound & Dose" = c(
    "6mg of Aspirin",
    "3mg of Aspirin",
    "6mg of Sugar",
    "3mg of Sugar"
  ),
  check.names = FALSE
)
knitr::kable(mappingDataFrame)
Compound Dose Compound & Dose
Aspirin 6 6mg of Aspirin
Aspirin 3 3mg of Aspirin
Sugar 6 6mg of Sugar
Sugar 3 3mg of Sugar
grouping3 <- Grouping$new(group = mappingDataFrame)

The default title of the legend that results from this grouping is the name of the rightmost column, which is “Compound & Dose”. Note that the check.names option should be set to FALSE when creating the dataframe mappingDataFrame, since the legend title contains spaces in this instance. This legend title can be overridden to be another string by using the label input of the object constructor, as in the case of grouping2 above.

The three Grouping objects, grouping1, grouping2, and grouping3 respectively yield the last three columns of the following dataframe:

# Apply the mapping to get the grouping captions:
groupingsDataFrame <- data.frame(
  testData$IndividualId,
  testData$Dose,
  testData$Compound,
  grouping1$getCaptions(testData),
  grouping2$getCaptions(testData),
  grouping3$getCaptions(testData)
)

names(groupingsDataFrame) <- c(
  "IndividualId", "Dose", "Compound",
  grouping1$label, grouping2$label, grouping3$label
)

# Show results for all groupings:
knitr::kable(groupingsDataFrame)
IndividualId Dose Compound Compound-Dose Compound & Dose Compound & Dose
0 6 Aspirin Aspirin-6 Aspirin-6 6mg of Aspirin
1 3 Aspirin Aspirin-3 Aspirin-3 3mg of Aspirin
2 6 Aspirin Aspirin-6 Aspirin-6 6mg of Aspirin
3 3 Sugar Sugar-3 Sugar-3 3mg of Sugar
4 6 Sugar Sugar-6 Sugar-6 6mg of Sugar
5 3 Aspirin Aspirin-3 Aspirin-3 3mg of Aspirin
6 6 Aspirin Aspirin-6 Aspirin-6 6mg of Aspirin
7 3 Sugar Sugar-3 Sugar-3 3mg of Sugar
8 6 Sugar Sugar-6 Sugar-6 6mg of Sugar
9 3 Sugar Sugar-3 Sugar-3 3mg of Sugar

A dataframe can also be used to create a Grouping object that subsets the data based on whether a numeric grouping variable satisfies an specific inequality. For example, individuals in testData can be grouped according to whether or not their age exceeds 6 years by first defining the following dataframe:

# Grouping using a data.frame:
binningDataFrame <- data.frame(
  Age = I(list(c(0, 6), c(7, 100))),
  "Age Range" = c(
    "Age 6 or lower",
    "Above age 6"
  ),
  check.names = FALSE
)

Then creating a new grouping:

grouping4 <- Grouping$new(group = binningDataFrame)

This new Grouping object grouping4 yields the following captions

# Apply the mapping to get the grouping captions:
testData$Age <- testData$`Organism|Age`
binnedGroupingsDataFrame <- data.frame(
  testData$IndividualId,
  testData$Age,
  grouping4$getCaptions(testData)
)

names(binnedGroupingsDataFrame) <- c("IndividualId", "Age", grouping4$label)

# Show results for all groupings:
knitr::kable(binnedGroupingsDataFrame)
IndividualId Age Age Range
0 14.06889 Above age 6
1 23.41955 Above age 6
2 24.89981 Above age 6
3 30.45043 Above age 6
4 22.96949 Above age 6
5 37.71187 Above age 6
6 50.12875 Above age 6
7 32.53951 Above age 6
8 26.86401 Above age 6
9 45.97137 Above age 6

3.1.2. GroupMapping class

An additional R6 class called GroupMapping maps Grouping objects to aesthetic parameters such as color or linetype. To distinguish between “Compound” and “Dose” groups by color and to use the captions and legend title specified in grouping2, the following groupings object groups1 is constructed:

# Map groups to aesthtic properties
groups1 <- GroupMapping$new(color = grouping2)

A GroupMapping object groups2 can also be constructed more quickly by directly associating an aesthetic, such as color, to a vector of dataframe column names:

# Map groups to aesthtic properties
groups2 <- GroupMapping$new(color = c("Compound", "Dose"))

or to a Grouping object directly:

# Map groups to aesthetic properties
groups3 <- GroupMapping$new(color = Grouping$new(
  group = c("Compound", "Dose"),
  label = c("Compound & Dose")
))

3.2. DataMapping

The R6 class XYGDataMapping extracts the maps the x, y, and grouping variables of data according to the aesthetics specified in an input GroupMapping object. This mapping is carried out by an internal function of this class named checkMapData which checks if the variables indicated the GroupMapping are included in the data. This method then returns a simplified dataframe with the variables defined by the dataMapping.

When no GroupMapping object is supplied upon construction of a XYGDataMapping object, the function checkMapData returns a dataframe with x and y. A dummy variable named aesDefault is added to the data.frame, its sole purpose is to allow modifications of aesthetic properties after the creation of the ggplot object (not possible otherwise).

tpMapping <- XYGDataMapping$new(x = "IndividualId", y = "Organism|VenousBlood|Volume")
knitr::kable(tpMapping$checkMapData(
  data = testData,
  metaData = IndividualIdProfileMetaData
))
IndividualId Organism|VenousBlood|Volume legendLabels
0 0.8767134
1 0.8130964
2 0.8054172
3 0.8048924
4 0.6127810
5 0.6186527
6 0.6700546
7 0.8003464
8 0.6001890
9 0.7350718

When a GroupMapping object is supplied upon construction of the XYGDataMapping object, each x,y pair is associated with a group that can be used to distinguish the pair aesthetically in the final plot:

# Re-use the variable groups previously defined
tpMapping <- XYGDataMapping$new(
  x = "IndividualId", y = "Organism|VenousBlood|Volume",
  groupMapping = groups1
)

knitr::kable(tpMapping$checkMapData(data = testData))
IndividualId Organism|VenousBlood|Volume Compound & Dose legendLabels
0 0.8767134 Aspirin-6 Aspirin-6
1 0.8130964 Aspirin-3 Aspirin-3
2 0.8054172 Aspirin-6 Aspirin-6
3 0.8048924 Sugar-3 Sugar-3
4 0.6127810 Sugar-6 Sugar-6
5 0.6186527 Aspirin-3 Aspirin-3
6 0.6700546 Aspirin-6 Aspirin-6
7 0.8003464 Sugar-3 Sugar-3
8 0.6001890 Sugar-6 Sugar-6
9 0.7350718 Sugar-3 Sugar-3

A feature of XYGDataMapping class is that, in addition to specifying a y column, the user may also supply ymin and ymax columns that can represent the boundaries of error bars. If only ymin and ymax are input when constructing the XYGDataMapping object, with y left undefined or NULL, the default profile that will ultimately be plotted is a range plot. If y, ymin and ymax are all input, the default plot will be a IndividualId profile plot with an error bar.