Get R Documentation from a JSON schema.

Description

Note: this function doesn't support the full JSON schema specification, currently only the functionalities required by the templates of the package are implemented.

Usage

JSONSchToRd(JSONSch, title = NULL)

Arguments

Value

Character vector with R documentation for the specified JSON schema.

Supported JSON schema tags

• 'title' is used as Rd section header

• 'description' is included in the text

• parameters are extracted from the following 'properties' tag:

  • 'type': object type

  • 'doc': documentation for the parameter (custom JSON schema tag). This can contain any Roxygen tags, e.g.: ⁠\link[package]{function}⁠.

  • 'pattern' (optional): required value for the parameter

  • 'items' (optional): JSON schema for the different elements of an 'object'

  • 'minItems'/'maxItems' (optional): minimum/maximum number of elements in an 'array'

  • 'enum' (optional): set of possible values

  • 'const' (optional): fixed value for the parameter (a.k.a 'constant')

  If a parameter is required, it should be listed in the 'required' tag of the schema (outside of the 'properties' tag).

Author(s)

Laure Cougnaud

Add date of report running

Description

Add the today's date of when the report runs to the info of the metadata.

Usage

addDateOfReportRun(summaryInfo)

Arguments

Value

A matrix, same as input summaryInfo with an extra row with the date of today.

Add facet-panel to single plotly plot.

Description

Add facet-panel to single plotly plot.

Usage

addFacetPanel(
  pl,
  panelLab,
  panelWidth = 20,
  fontSize = 15,
  side = c("top", "right")
)

Arguments

Details

plot title clipping.

In case side = 'top', the plot title (eg. layout(title = "title")) will clip with the top panel.

Resolve this with the following configurations: (once all the subplots have already been combined) layout( # place the title at absolute top of the page title = list(text = "title", yref = "container", y = 1)) # If font size = 15 roughly equal to 20 pixels. margin = list(t = panelWidth + heightTitleTextInPixels)

Value

plotly object with the facet panel added.

Author(s)

lennart tuijnder

Helper function to add layer to scatter plot

Description

Helper function to add layer to scatter plot

Usage

addLayerToScatterPlot(
  gg,
  aesVar,
  pars,
  generalPars,
  layerFunction,
  useHandlers = FALSE
)

Arguments

Value

ggplot object

Author(s)

Adriaan Blommaert Laure Cougnaud

Add reference (horizontal/vertical/diagonal) lines to a clinical data plot

Description

Add reference (horizontal/vertical/diagonal) lines to a clinical data plot

Usage

addReferenceLinesClinDataPlot(
  gg,
  data,
  xVar,
  yVar,
  xLim = NULL,
  yLim = NULL,
  refLinePars = NULL,
  facetPars = NULL
)

Arguments

Value

Updated ggplot object.

Author(s)

Laure Cougnaud

Add selection box(es) to a plotly plot.

Description

Add selection box(es) to a plotly plot.

Usage

addSelectBtn(
  data,
  pl,
  selectVars = NULL,
  selectLab = NULL,
  labelVars = NULL,
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  keyVar
)

Arguments

Value

if selectVars is specified: a browsable object combining the select buttons and the plotly object.
Otherwise, the input plotly object.

Add a watermark to a plotly object.

Description

Add a watermark to a plotly object.

Usage

addWatermark(pl, watermark = NULL)

Arguments

Value

plotly object with a watermark (if specified)

See Also

Other watermark helpers: getWatermark()

Examples

watermark <- tempfile(pattern = "watermark", fileext = ".png")
getWatermark(file = watermark)
addWatermark(pl = plotly::plot_ly(), watermark = watermark)

Annotate a dataset.

Description

Standard annotation variables are available via the parameter annotType. Custom dataset/variables of interest are specified via the annotDataset/annotVar parameters.

Usage

annotateData(
  data,
  dataPath = ".",
  annotations,
  subjectVar = "USUBJID",
  verbose = FALSE,
  labelVars = NULL,
  labelData = "data"
)

Arguments

Value

Annotated data. If labelVars is specified, the output contains an extra attribute: 'labelVars' containing updated labelVars (accessible via: in attr(output, 'labelVars')).

Examples

library(clinUtils)

data(dataADaMCDISCP01)

dataLB <- dataADaMCDISCP01$ADLBC
dataDM <- dataADaMCDISCP01$ADSL
dataAE <- dataADaMCDISCP01$ADAE

labelVars <- attr(dataADaMCDISCP01, "labelVars")

# standard annotations:

# path to dataset should be specified via: 'pathData'
# annotateData(dataLB, annotations = "demographics", pathData = ...)

# add all variables in annotation data (if not already available)
head(annotateData(dataLB, annotations = list(data = dataDM)), 1)

# only variables of interest
head(annotateData(dataLB, annotations = list(data = dataDM, vars = c("ARM", "ETHNIC"))), 1)

# filter annotation dataset
dataAnnotated <- annotateData(dataLB, 
	annotations = list(
		data = dataDM, 
		vars = c("ARM", "ETHNIC"), 
		filters = list(var = "ARM", value = "Placebo")
	)
)
head(subset(dataAnnotated, ARM == "Placebo"), 1)
head(subset(dataAnnotated, is.na(ARM)), 1)

# worst-case scenario: add a new variable based on filtering condition
dataAE$AESEV <- factor(dataAE$AESEV, levels = c('MILD', "MODERATE", "SEVERE"))
dataAEWC <- annotateData(
	data = dataAE,
	annotations = list(
		vars = "WORSTINT", 
		# create new variable: 'WORSTINT' 
		# with TRUE if maximum toxicity grade per subject/test 
		# (if multiple, they are all retained)
		filters = list(
			var = "AESEV", 
			# max will take latest level in a factor 
			# (so 'MODERATE' if 'MILD'/'MODERATE' are available)
			valueFct = function(x) x[which.max(as.numeric(x))],
			varsBy = c("USUBJID", "AEDECOD"),
			keepNA = FALSE,
			varNew = "WORSTINT", 
			labelNew = "worst-case"
		)
	),
	labelVars = labelVars,
	verbose = TRUE
)
attr(dataAEWC, "labelVars")["WORSTINT"]

# add a new variable based on a combination of variables:
dataLB <- annotateData(dataLB, 
	annotations = list(vars = "HILORATIO", varFct = "A1HI / A1LO")
)

# add a new variable based on extraction of a existing variable
# Note: slash should be doubled when the function is specified as text
dataLB <- annotateData(dataLB, 
	annotations = list(vars = "PERIOD", varFct = "sub('.* Week (.+)', 'Week \\\\1', AVISIT)")
)

# multiple annotations:
dataAnnotated <- annotateData(dataLB, 
	annotations = list(
		list(data = dataDM, vars = c("ARM", "ETHNIC")),
		list(data = dataAE, vars = c("AESEV"))
	)
)
head(dataAnnotated, 1)

Barplot visualization of clinical data.

Description

Barplot visualization of clinical data.

Usage

barplotClinData(
  data,
  xVar,
  yVar,
  xLab = getLabelVar(xVar, labelVars = labelVars),
  xLabVar = NULL,
  yLab = getLabelVar(yVar, labelVars = labelVars),
  yLabVar = NULL,
  colorVar = NULL,
  colorLab = getLabelVar(colorVar, labelVars = labelVars),
  colorPalette = NULL,
  barmode = "group",
  title = paste(c(paste(yLab, "vs", xLab), titleExtra), collapse = "<br>"),
  titleExtra = NULL,
  caption = NULL,
  subtitle = NULL,
  labelVars = NULL,
  width = NULL,
  height = NULL,
  hoverVars,
  hoverLab,
  textVar = NULL,
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  selectVars = NULL,
  selectLab = getLabelVar(selectVars, labelVars = labelVars),
  watermark = NULL,
  verbose = FALSE
)

Arguments

<a href="./path-to-report">label</a>

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other visualizations of summary statistics for clinical data: boxplotClinData(), errorbarClinData(), plotCountClinData(), sunburstClinData(), treemapClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataAE <- dataADaMCDISCP01$ADAE
dataDM <- dataADaMCDISCP01$ADSL

## example of basic barplot:

# treemap takes as input table with counts

if (requireNamespace("inTextSummaryTable", quietly = TRUE)) {

# total counts: Safety Analysis Set (patients with start date for the first treatment)
dataTotal <- subset(dataDM, RFSTDTC != "")

# compute adverse event table

tableAE <- inTextSummaryTable::computeSummaryStatisticsTable(
	data = dataAE,
	rowVar = c("AEBODSYS", "AEDECOD"),
	rowOrder = "total",
	dataTotal = dataTotal,
	labelVars = labelVars,
	stats = inTextSummaryTable::getStats("count")
)

dataPlot <- subset(tableAE, AEDECOD != "Total")

dataPlot$n <- as.numeric(dataPlot$n)

# create plot
barplotClinData(
	data = dataPlot,
	xVar = "AEDECOD", 
	yVar = "n", yLab = "Number of patients with adverse events",
	labelVars = labelVars
)

# add number on top of the bars
barplotClinData(
	data = dataPlot,
	xVar = "AEDECOD", 
	yVar = "n", yLab = "Number of patients with adverse events",
	textVar = "n",
	labelVars = labelVars
)

# add a selection box
if(interactive()){
  barplotClinData(
    data = dataPlot,
    xVar = "AEDECOD", 
    yVar = "n", yLab = "Number of patients with adverse events",
    labelVars = labelVars,
    selectVars = "AEBODSYS"
  )
}

# display percentage of events per severity
tableAEBySeverity <- inTextSummaryTable::computeSummaryStatisticsTable(
	data = dataAE,
	rowVar = c("AEDECOD", "AESEV"),
	dataTotal = dataTotal,
	labelVars = labelVars,
	statsPerc = "statm",
	stats = inTextSummaryTable::getStats("%m"),
	dataTotalPerc = dataAE,
	rowVarTotalPerc = "AEDECOD"
)
barplotClinData(
	data = tableAEBySeverity,
	xVar = "AEDECOD", xLab = "Adverse event term",
	yVar = "statPercm", yLab = "Percentage of adverse events",
	labelVars = labelVars,
	colorVar = "AESEV", barmode = "stack",
	hoverVar = c("AEDECOD", "AESEV", "statN", "statm", "statPercm"),
	hoverLab = c(
		labelVars["AEDECOD"],
		labelVars["AESEV"],
		statN = "Number of patients",
		statm = "Number of events",
		statPercm = "Percentage of events"
	),
	textVar = "%m",
	# add subtitle
	subtitle = "Group: severity"
)

}

Boxplot interactive plot.

Description

Boxplot interactive plot.

Usage

boxplotClinData(
  data,
  xVar,
  yVar,
  xLab = getLabelVar(xVar, labelVars = labelVars),
  xLabVar = NULL,
  yLab = getLabelVar(yVar, labelVars = labelVars),
  yLabVar = NULL,
  colorVar = NULL,
  colorLab = getLabelVar(colorVar, labelVars = labelVars),
  colorPalette = NULL,
  facetVar = NULL,
  facetLab = getLabelVar(facetVar, labelVars = labelVars),
  ncol = 1L,
  title = paste(c(paste(yLab, "vs", xLab), titleExtra), collapse = "<br>"),
  titleExtra = NULL,
  subtitle = NULL,
  caption = NULL,
  labelVars = NULL,
  width = NULL,
  height = NULL,
  hoverVars,
  hoverLab,
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  idVar = "USUBJID",
  idLab = getLabelVar(idVar, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  watermark = NULL,
  verbose = FALSE
)

Arguments

<a href="./path-to-report">label</a>

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Lennart Tuijnder

See Also

Other visualizations of summary statistics for clinical data: barplotClinData(), errorbarClinData(), plotCountClinData(), sunburstClinData(), treemapClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

## example of basic boxplot:

data <- subset(dataADaMCDISCP01$ADVS, 
	PARAMCD == "DIABP" & ANL01FL == "Y" &
	AVISIT %in% c("Baseline", "Week 2", "Week 4", "Week 6", "Week 8")
)

## example of basic boxplot:

# With color var and facet:
boxplotClinData(
	data = data,
	xVar = "AVISIT", 
	yVar = "AVAL",
	colorVar = "TRTA",
	facetVar = "ATPT",
	title = "Diastolic Blood Pressure distribution by actual visit and analysis timepoint",
	yLab = "Actual value of the Diastolic Blood Pressure parameter (mmHg)",
	labelVars = labelVars
)

# Control number of facet columns:
boxplotClinData(
	data = data,
	xVar = "AVISIT", 
	yVar = "AVAL",
	colorVar = "TRTA",
	facetVar = "ATPT",
	ncol = 2,
	title = "Diastolic Blood Pressure distribution by actual visit and analysis timepoint",
	yLab = "Actual value of the Diastolic Blood Pressure parameter (mmHg)",
	labelVars = labelVars
)

# Facet or color is optional:
boxplotClinData(
	data = data,
	xVar = "AVISIT", 
	yVar = "AVAL",
	colorVar = "TRTA"
)

boxplotClinData(
	data = data,
	xVar = "AVISIT", 
	yVar = "AVAL",
	facetVar = "ATPT"
)

# add caption & subtitle
boxplotClinData(
	data = data,
	xVar = "AVISIT", 
	yVar = "AVAL",
	facetVar = "ATPT", ncol = 2,
	colorVar = "TRTA",
	title = "Diastolic Blood Pressure distribution",
	subtitle = "By actual visit and analysis timepoint",
	yLab = "Actual value of the Diastolic Blood Pressure parameter (mmHg)",
	caption = "Summary statistics are computed internally.",
	labelVars = labelVars
)

Build the book

Description

Build the book

Usage

buildBook(htmlFiles, verbose = TRUE)

Arguments

Value

String with path to the front page of the report.

Author(s)

Laure Cougnaud

Check availability of arguments in list

Description

Check availability of arguments in list

Usage

checkAvailabilityMetadata(paramsList, subListName)

Arguments

Value

The content of the sublist. If not available, returns "Not Available".

Check if a chapter is run internally in parallel or not.

Description

This is identified via the 'parallel' parameter from the config file. If this parameter is not available in the config file (or the parameters are imported with an error), the chapter is considered to not be run in parallel.

Usage

checkChapterParallel(
  configFile,
  configDir = file.path(inputDir, "config"),
  inputDir = "."
)

Arguments

Value

Logical, if TRUE, the chapter is run in parallel (FALSE if not available).

Check a configuration file (in _YAML_ format) based on a requirement file in JSON Schema format.

Description

Check a configuration file (in _YAML_ format) based on a requirement file in JSON Schema format.

Usage

checkConfigFile(configFile, configSpecFile, configDir = "./config")

Arguments

Value

No returned value, an error message is printed in the console if the configuration file doesn't comply to the specified specifications.

Author(s)

Laure Cougnaud

Check report titles

Description

Check uniqueness of report titles across the config files. If not unique titles are provided, an error is returned.

Usage

checkReportTitles(
  configFiles,
  configDir = file.path(inputDir, "config"),
  inputDir = "."
)

Arguments

Value

A named vector with the report titles and the corresponding config file

Author(s)

Michela Pasetto

See Also

Other clinical data reporting: forceParams(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Checks of config files template.

Description

Check if the templates specified in the input config files don't originate from multiple sources (e.g. custom and R package via the parameter templatePackage). If so, the corresponding config files are not considered.

Usage

checkTemplatesName(
  configFiles,
  configDir = file.path(inputDir, "config"),
  inputDir = "."
)

Arguments

Value

Updated configFiles

Author(s)

Laure Cougnaud

Check if the specified valueType parameter can be passed to the branchvalues of the plot_ly treemap/sunburst visualizations.

Description

If this parameter is set to 'total' and the sum of the counts of the the children nodes is not bigger than the parent node, an empty plot is created. In this case, this function set this parameter to: 'relative'.

Usage

checkValueType(data, vars, valueVar, valueType = "total", labelVars = NULL)

Arguments

Value

If the condition is fullfilled: updated valueType and warning; otherwise input valueType.

Author(s)

Laure Cougnaud

Common arguments for the functions of the clinDataReview package

Description

Common arguments for the functions of the clinDataReview package

Arguments

Value

No return value, used for the documentation of the functions of the package.

Common parameters for the clinical data reporting function

Description

Common parameters for the clinical data reporting function

Arguments

Value

No return value, used for the documentation of the clinical data reporting functions of the package.

Common arguments for the plotting functions summary statistics of the clinDataReview package

Description

Common arguments for the plotting functions summary statistics of the clinDataReview package

Arguments

<a href="./path-to-report">label</a>

Value

No return value, used for the documentation of the plotting functions of summary statistics of the package.

Rmarkdown templates for clinical data

Description

Template reports with standard visualizations/tables available in the package are described here.

Details

For each template, required parameters are indicated in bold.

Value

No return value, used for the documentation of the Rmarkdown template reports contained in the package.

Parameter type

Please note that the type mentioned below corresponds to the type in the config file (in YAML/JSON format).
The mapping to R data type is as followed:

• string: character vector of length 1

• integer: integer vector of length 1

• array: vector/list without names

• object: list with names

Clinical data template for a visualization of count data : countsVisualizationTemplate

This report compute counts of variable(s) of interest (with the inTextSummaryTable package) and visualize them with a treemap and/or sunburst.
The following parameters are available:

• template: string set to: 'countsVisualizationTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• dataFileName: string or array, name of the data file(s) of interest. If multiple files are specified, the data are combined by rows ('row bind'), with a column: 'DATASET' containing the name of the file (in upper case and without extension) the data originate from.

• dataProcessing: (optional) array, data processing parameters, passed to processData

• dataTotalFileName: (optional) string, filename of the total dataset

• dataTotalProcessing: (optional) array, data processing parameters for 'dataTotalFileName', passed to processData

• countVar: string, variable of data to count on

• parentVar: (optional) string or array, parent variable(s) of the counting variable, used for grouping

• colorVar: (optional) string or object, numeric variable(s) to consider for coloring, named by count/parent variable if different for each variable

• colorRange: (optional) array of number(s) of length: minimum 2, maximum 2, range of the color variable for the visualization

• loopingVar: (optional) array or string, data variable(s) to loop over. Each group of the variable(s) is displayed in a separated section of the report.

• loopingNMax: (optional) integer, maximum number of elements of loopingVar to include in the report

• loopingTotal: (optional) boolean, should the total also be computed by loopingVar (TRUE by default)?

• typePlot: (optional) array or string of string(s) among: 'sunburst', 'treemap'

• startup: (optional) array or string, R commands that should be run at the start of the report

Clinical data template to create a report division : divisionTemplate

This report includes a division, i.e. extra chapter, section of subsection in the report.
The following parameters are available:

• template: string set to: 'divisionTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• content: (optional) string, any content that should be included after the title

Clinical data template to create a listing : listingTemplate

This report displays a listing of the variables and data of interest, displayed in an interactive table. This table can contains comparison with a previous batch ('comparisonTable' parameters).
The following parameters are available:

• template: string set to: 'listingTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• dataFileName: string or array, name of the data file(s) of interest. If multiple files are specified, the data are combined by rows ('row bind'), with a column: 'DATASET' containing the name of the file (in upper case and without extension) the data originate from.

• dataProcessing: (optional) array, data processing parameters, passed to processData

• dataTotalFileName: (optional) string, filename of the total dataset

• dataTotalProcessing: (optional) array, data processing parameters for 'dataTotalFileName', passed to processData

• tableParams: (optional) object, parameters to create the table, passed to tableClinData

• comparisonTableType: (optional) string among: 'none', 'newData-diff-interactive', 'table-comparison-interactive' , output type of the comparison table

• comparisonTableParams: (optional) object, parameters for the comparison table, passed to compareTables

• loopingVar: (optional) array or string, data variable(s) to loop over. Each group of the variable(s) is displayed in a separated section of the report.

• loopingNMax: (optional) integer, maximum number of elements of loopingVar to include in the report

• listingDocx: (optional) boolean, export listing to Word

Clinical data template for the creation of patient profiles : patientProfilesTemplate

This report creates the specified patient profiles (with the patientProfilesVis package) by subject, and export them to a specified directory.
The following parameters are available:

• template: string set to: 'patientProfilesTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• createPatientProfiles: (optional) boolean, Should the patient profiles be created or only loaded from a previous execution?

• patientProfilesGeneralParams: (optional) object, set of parameters used for all patient profiles modules. These parameters are passed to all subjectProfile[]Plot functions.

• patientProfilesParams: array of object(s)
  The following parameters are available:

  • typePlot: string among: 'text', 'line', 'interval', 'event' , plot type, used to get the appropriate plot module function:

    • 'text': subjectProfileTextPlot

    • 'line': subjectProfileLinePlot

    • 'interval': subjectProfileIntervalPlot

    • 'event': subjectProfileEventPlot

  • dataFileName: string, name of the data file of interest

  • dataProcessing: (optional) array, data processing parameters, passed to processData

  • plotParams: object, parameters for the plotting function. Parameters depending on the dataset of interest can be specified as:
    [parameterName]: !r-lazy [dataI]

  , parameters for each patient profile module

• patientProfilesCreateReportParams: (optional) object, parameters for the creation of the patient profile report(s), passed to createSubjectProfileReport

• tableParams: (optional) object, parameters specifying a table containing data of interest and links to created patient profiles

• startup: (optional) array or string, R commands that should be run at the start of the report

Clinical data generic template for visualization : plotTemplate

This report visualizes input data with a function of the clinical data review package. The data can be compared to the data of a previous batch, in the table associated to the plot ('comparisonTable' parameters).Summary statistics can be computed optionally and included in the plot (see 'tableParams' parameter).
The following parameters are available:

• template: string set to: 'plotTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• dataFileName: string or array, name of the data file(s) of interest. If multiple files are specified, the data are combined by rows ('row bind'), with a column: 'DATASET' containing the name of the file (in upper case and without extension) the data originate from.

• dataProcessing: (optional) array, data processing parameters, passed to processData

• plotFunction: string among: 'timeProfileIntervalPlot', 'scatterplotClinData', 'boxplotClinData' , plotting function of the package to consider

• plotParams: object, parameters for the plotting function. Parameters depending on the dataset of interest can be specified as:
  [parameterName]: !r-lazy [dataI]

• tableParams: (optional) object, parameters for a summary table, passed to computeSummaryStatisticsTable
  Summary statistics are computed and merged as extra columns available for the plot data.

• tableProcessing: (optional) array, data processing parameters for the summary table, passed to processData

• comparisonTableType: (optional) string among: 'none', 'newData-diff' , output type of the comparison table. If specified, an additional column: 'Comparison Type' is included in the table attached to the plot.

• comparisonTableParams: (optional) object, parameters for the comparison table, passed to compareTables. If referenceVars is not specified, all variables displayed in the plot are used.

• loopingVar: (optional) array or string, data variable(s) to loop over. Each group of the variable(s) is displayed in a separated section of the report.

• loopingNMax: (optional) integer, maximum number of elements of loopingVar to include in the report

• startup: (optional) array or string, R commands that should be run at the start of the report

Clinical data template for visualization of summarized data : summaryPlotTemplate

This report summarizes the data of interest (with the inTextSummaryTable package) and visualize it with any clinical data review plot function.
The following parameters are available:

• template: string set to: 'summaryPlotTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• dataFileName: string or array, name of the data file(s) of interest. If multiple files are specified, the data are combined by rows ('row bind'), with a column: 'DATASET' containing the name of the file (in upper case and without extension) the data originate from.

• dataProcessing: (optional) array, data processing parameters, passed to processData

• dataTotalFileName: (optional) string, filename of the total dataset

• dataTotalProcessing: (optional) array, data processing parameters for 'dataTotalFileName', passed to processData

• tableParams: object, parameters to summarize the data in a table, passed to computeSummaryStatisticsTable

• tableProcessing: (optional) array, data processing parameters for the summary table, passed to processData

• plotFunction: string among: 'timeProfileIntervalPlot', 'scatterplotClinData', 'sunburstClinData', 'treemapClinData', 'barplotClinData', 'errorbarClinData' , plotting function to visualize summary data

• plotParams: object, parameters for the plotting function. Parameters depending on the dataset of interest can be specified as:
  [parameterName]: !r-lazy [summaryTableI]

• loopingVar: (optional) array or string, data variable(s) to loop over. Each group of the variable(s) is displayed in a separated section of the report.

• loopingNMax: (optional) integer, maximum number of elements of loopingVar to include in the report

• loopingTotal: (optional) boolean, should the total also be computed by loopingVar (TRUE by default)?

• startup: (optional) array or string, R commands that should be run at the start of the report

Clinical data template for a summary table of the data : summaryTableTemplate

This report summarizes the data of interest (with the inTextSummaryTable package). This table is displayed with an interactive table in the report, and exported to a docx file. This table can be compared to a summary table of a previous batch ('comparisonTable' parameters).
The following parameters are available:

• template: string set to: 'summaryTableTemplate.Rmd', name of the template report

• templatePackage: string set to: 'clinDataReview', package from which the template should be extracted

• reportTitle: string, header title

• reportTitleLevel: (optional) integer, header level, 1 by default (1: 'chapter', '2': 'section', '3': subsection, ...)

• parallel: (optional) boolean, does this chapter use parallel execution? If the entire report is run in parallel, this ensures that this specific chapter is created outside of the report parallelization.

• split_by: (optional) integer of length: minimum 0, maximum 7 or string among: 'none', 'chapter', 'section' , split the chapter at the specified level: 1 (for 'chapter'), 2 (for 'section') until 7. This overwrites the 'split_by' parameter defined in the output format of the report.

• dataFileName: string or array, name of the data file(s) of interest. If multiple files are specified, the data are combined by rows ('row bind'), with a column: 'DATASET' containing the name of the file (in upper case and without extension) the data originate from.

• dataProcessing: (optional) array, data processing parameters, passed to processData

• dataTotalFileName: (optional) string, filename of the total dataset

• dataTotalProcessing: (optional) array, data processing parameters for 'dataTotalFileName', passed to processData

• tableParams: object, parameters to summarize the data in a table, passed to computeSummaryStatisticsTable .Parameters depending on the dataset of interest can be specified as:
  [parameterName]: !r-lazy [dataI].

• tableParamsDocx: (optional) object, parameters to format the table to the docx format, passed to exportSummaryStatisticsTable

• tableParamsDT: (optional) object, parameters to format the table to the DT interactive table included in the report, passed to exportSummaryStatisticsTable

• comparisonTableType: (optional) string among: 'none', 'table-comparison-interactive', 'newData-diff', 'table-combine' , output type of the comparison table:

  • 'table-comparison-interactive': the summary statistics are computed for the old and new batch separately, and the statistics are compared

  • 'newData-diff': the differences at a record level between the new and old batch are first identified, and then summarized

  • 'table-combine': the summary tables are computed for the old and new batch and displayed next to each other in a combined table

• comparisonTableParams: (optional) object, parameters for the comparison table, passed to compareTables. By default, statistics variables are compared across row and column elements.

• loopingVar: (optional) array or string, data variable(s) to loop over. Each group of the variable(s) is displayed in a separated section of the report.

• loopingNMax: (optional) integer, maximum number of elements of loopingVar to include in the report

• loopingTotal: (optional) boolean, should the total also be computed by loopingVar (TRUE by default)?

Function to create collapsible HTML content

Description

Please note that the button is of class: 'hideshow', defined in the 'input.hideshow.js' js file included in the package.

Usage

collapseHtmlContent(input, title = "Click to show or hide")

Arguments

Value

tag object

Author(s)

Laure Cougnaud

Combine select box(es) and the plot

Description

Combine select box(es) and the plot

Usage

combineButtonsAndPlot(x)

Arguments

Value

x object:

• with the plot element containing a combination of the buttons and the plot

• without the buttons element

Convert the Md file for a specific chapter to html

Description

Convert the Md file for a specific chapter to html

Usage

convertMdToHtml(
  mdFile,
  configFile = NULL,
  indexPath = "index.Rmd",
  intermediateDir = "./interim",
  outputDir = "./report",
  setTitle = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Value

No returned value, the files in the intermediateDir are converted to HTML

Author(s)

Laure Cougnaud

Convert report format from .html to .aspx

Description

Report files generated as output of render_clinDataReviewReport are converted from .html to to .aspx format by changing extensions and cross-links of all files within the directory reportDir. This allows for deployment on SharePoint.

Usage

convertReportToAspx(reportDir = ".")

Arguments

Value

no return value, files in directory are modified

Count number of lines in a vector

Description

Count number of lines in a vector

Usage

countNLines(x)

Arguments

Value

Integer vector of length x with number of lines

Author(s)

Laure Cougnaud

Create the skeleton of a report

Description

Creates the skeleton of a report to start running the analyses.

Usage

createClinDataReviewReportSkeleton(dir = ".")

Arguments

Details

This function is meant to get familiar with the use of the package and the necessary files to create a report.

It will create a ready-to-use report with example data from the clinUtils package. After getting use to the file structure, the user can substitute the example data with custom data sets and add specific configuration files.

Value

The files to run a report are written in the specified directory. To run the report, the user can call the render_clinDataReviewReport.

Create an example metadata file

Description

Create an example of metadata file for the createClinDataReviewReportSkeleton.

Usage

createExampleMetadata(dir)

Arguments

Value

Nothing, the example metadata file is created in the specified directory.

Create the config file for the skeleton

Description

This function creates the main config file for the createClinDataReviewReportSkeleton with the directory where the data are stored.

Usage

createMainConfigSkeleton(dir, dirData)

Arguments

Value

No return value, a file _config.yml_ is created in the specified directory.

Create a output YAML file

Description

This file containing the contents of the output field of the YAML header of a Markdown file. It can be passed to the output_yaml parameter of the render function.

Usage

createOutputYaml(indexPath, outputDir)

Arguments

Value

String with file to the _output.yml file in a temporary folder.

Create link to patient profile

Description

Create a link to a patient profile directory (where the patient profile files are saved) by adding an extra column with the link in the data. The path to the patient profile is built as: [patientProfilePath]/subjectProfile-[subjectID].pdf, where '/' are replaced with '-' in the subject identifier (subjectVar).

Usage

createPatientProfileVar(
  data,
  patientProfilePath,
  subjectVar = "USUBJID",
  checkExist = TRUE
)

Arguments

Value

A data.frame with two extra columns: patientProfilePath and patientProfileLink with the path to the patient profile and an hyperlink to it, respectively.

Author(s)

Michela Pasetto

Examples

# Typical CDISC dataset contains universal subject ID (USUBJID)
data <- data.frame(USUBJID = c("subj1", "subj2", "subj3"))
dataWithPatientProfileVar <- createPatientProfileVar(
  data = data, 
  patientProfilePath = "pathProfiles", 
  checkExist = FALSE
)
# path and HTML link are included in the output dataset
head(dataWithPatientProfileVar[, c("USUBJID", "patientProfilePath", "patientProfileLink")])

Create a redirect page

Description

Create an html page that redirects to the "1-introduction.html" page of the clinical data report available in a directory. See output from render_clinDataReviewReport.

Usage

createRedirectPage(redirectPage = "report.html", dir = "report_dependencies")

Arguments

Value

The html file is created.

Create documentation for clinical data template reports available in the 'template' folder of the package.

Description

If a JSON schema file available, the information relative to the template is extracted from this file with the function JSONSchToRd.

Usage

createTemplateDoc(
  templatePath = system.file("template", package = "clinDataReview")
)

Arguments

Value

Character vector with Rd code containing description for all template documents.

Author(s)

Laure Cougnaud

References

JSON schema specification

Interactive plot of confidence interval/error interval of clinical data.

Description

This plot is designed to display summary statistics of a continuous variable with (confidence) intervals.
The intervals are either displayed:

• vertically if yErrorVar is specified

• horizontally if xErrorVar is specified

Error bars can visualized by group, via the color variable parameter.
Different symbols are set for each central point of the error bar via the shape variable parameter.

Usage

errorbarClinData(
  data,
  xVar,
  xLab = getLabelVar(xVar, labelVars = labelVars),
  xLabVar = NULL,
  yVar,
  yLab = getLabelVar(yVar, labelVars = labelVars),
  yLabVar = NULL,
  yErrorVar = NULL,
  yErrorLab = getLabelVar(yErrorVar, labelVars = labelVars),
  xErrorVar = NULL,
  xErrorLab = getLabelVar(xErrorVar, labelVars = labelVars),
  xLabVars = NULL,
  xAxisLab = paste(c(xLab, xErrorLab), collapse = " and "),
  yAxisLab = paste(c(yLab, yErrorLab), collapse = " and "),
  colorVar = NULL,
  colorLab = getLabelVar(colorVar, labelVars = labelVars),
  colorPalette = NULL,
  shapeVar = NULL,
  shapeLab = getLabelVar(shapeVar, labelVars = labelVars),
  shapePalette = NULL,
  size = 6,
  title = paste(c(paste(yAxisLab, "vs", xAxisLab), titleExtra), collapse = "<br>"),
  titleExtra = NULL,
  subtitle = NULL,
  caption = NULL,
  labelVars = NULL,
  mode = "markers",
  legendPosition = "bottom",
  width = NULL,
  height = NULL,
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  hoverVars,
  hoverLab,
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  selectVars = NULL,
  selectLab = getLabelVar(selectVars, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  watermark = NULL,
  verbose = FALSE
)

Arguments

<a href="./path-to-report">label</a>

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other visualizations of summary statistics for clinical data: barplotClinData(), boxplotClinData(), plotCountClinData(), sunburstClinData(), treemapClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

## Summary plot with vertical error bars

dataVSDIABP <- subset(dataADaMCDISCP01$ADVS, 
	PARAMCD == "DIABP" & ANL01FL == "Y" &
	AVISIT %in% c("Baseline", "Week 2", "Week 4", "Week 6", "Week 8")
)

# compute summary statistics by visit
if (requireNamespace("inTextSummaryTable", quietly = TRUE)) {
	
summaryTableVSDIABP <- inTextSummaryTable::computeSummaryStatisticsTable(
	data = dataVSDIABP,
	rowVar = c("AVISIT", "ATPT"),
	var = "AVAL",
	stats = inTextSummaryTable::getStats(c("n", "Mean", "SE")),
	labelVars = labelVars
)
dataPlot <- subset(summaryTableVSDIABP, !isTotal)

errorbarClinData(
	data = dataPlot,
	xVar = "AVISIT",
	colorVar = "ATPT",
	# use non-rounded statistics for the plot
	yVar = "statMean", yErrorVar = "statSE", 
	yLab = "Mean", yErrorLab = "Standard Error",
	# include lines connecting the error bars
	mode = "markers+lines",
	labelVars = labelVars
)

# add number of subjects in labels
dataPlot$nSubj <- with(dataPlot, paste0("N=", n))
errorbarClinData(
	data = dataPlot,
	xVar = "AVISIT", 
	xLabVars = c("AVISIT", "nSubj"),
	colorVar = "ATPT",
	yVar = "statMean", yLab = "Mean",
	yErrorVar = "statSE", yErrorLab = "Standard error",
	mode = "markers+lines",
	title = paste("Diastolic Blood Pressure summary profile by actual visit",
		"and analysis timepoint"),
	labelVars = labelVars
)

## Add a selection box
if(interactive()){
  summaryTable <- inTextSummaryTable::computeSummaryStatisticsTable(
    data = subset(dataADaMCDISCP01$ADVS, 
      ANL01FL == "Y" &
      AVISIT %in% c("Baseline", "Week 2", "Week 4", "Week 6", "Week 8")
    ),
    rowVar = c("PARAM", "AVISIT", "ATPT"),
    var = "AVAL",
    stats = inTextSummaryTable::getStats(c("Mean", "SE")),
    labelVars = labelVars
  )
  dataPlot <- subset(summaryTable, !isTotal)
  errorbarClinData(
    data = dataPlot,
    xVar = "AVISIT", 
    colorVar = "ATPT",
    yVar = "statMean", yLab = "Mean",
    yErrorVar = "statSE", yErrorLab = "Standard error",
    mode = "markers+lines",
    title = paste("Lab parameters summary profile by actual visit",
      "and analysis timepoint"),
    labelVars = labelVars,
    selectVars = "PARAM"
  )
}

## Summary plot with horizontal error bars

# Data of interest: ratio from baseline at week 16
dataLBW8 <- subset(dataADaMCDISCP01$ADLBC, grepl("Week 8", AVISIT))
# compute ratio from baseline
dataLBW8$R2BASE <- with(dataLBW8, AVAL/BASE) 
dataLBW8 <- subset(dataLBW8, !is.na(R2BASE))
# Order actual treatments
dataLBW8$TRTA <- with(dataLBW8, reorder(TRTA, TRTAN))

# compute summary statistics of the ratio per baseline per parameter
summaryTableLBW8 <- inTextSummaryTable::computeSummaryStatisticsTable(
	data = dataLBW8,
	var = "R2BASE",
	rowVar = "PARAM",
	colVar = "TRTA",
	stats = inTextSummaryTable::getStats(x = dataLBW8$R2BASE, type = c("n", "Median", "SD"))
)
dataPlot <- subset(summaryTableLBW8, !isTotal)
# extract direction of ratio
dataPlot$dir <- factor(
	ifelse(dataPlot$statMedian >= 1, "Increase", "Decrease"),
	levels = c("Decrease", "Increase")
)
# compute relative ratio (percentage above 1)
dataPlot$statMedianRelative <- with(dataPlot,
	ifelse(statMedian < 1, 1/statMedian, statMedian)
)
# order based on mean relative ratio across treatment arms
params <- names(sort(with(dataPlot, tapply(statMedianRelative, PARAM, mean))))
dataPlot$PARAM <- factor(dataPlot$PARAM, levels = params)
errorbarClinData(
	data = dataPlot,
	xVar = "statMedianRelative", xErrorVar = "statSD",
	xLab = "Median", xErrorLab = "Standard deviation",
	xAxisLab = "Relative ratio from baseline (Median +- SD)",
	yVar = "PARAM",
	colorVar = "TRTA",
	shapeVar = "dir", shapeLab = "Direction of ratio",
	shapePalette = c(`Decrease` = 25, `Increase` = 24),
	size = 10,
	labelVars = labelVars,
	title = "Summary ratio from baseline at week 8 by treatment"
)

}

Combine all session informations across all clinical data reports and export them into a dedicated Markdown document

Description

Combine all session informations across all clinical data reports and export them into a dedicated Markdown document

Usage

exportSessionInfoToMd(
  sessionInfos,
  intermediateDir = "interim",
  logFile = NULL,
  ...
)

Arguments

Value

String with path to Markdown file containing the session information, NULL if no session information(s) are provided.

Author(s)

Laure Cougnaud

Filter a dataset based on specified filters.

Description

A dataset can be filtered:

• on a specific value of interest

• on a function of a variable (valueFct parameter), e.g. maximum of the variable)

• to retain only non missing values of a variable (keepNA set to FALSE)

• by groups (varsBy parameter)

Note that by default, missing values in the filtering variable are retained (which differs from the default behaviour in R). To filter missing records, please set the keepNA parameter to FALSE.

Usage

filterData(
  data,
  filters,
  keepNA = TRUE,
  returnAll = FALSE,
  verbose = FALSE,
  labelVars = NULL,
  labelData = "data"
)

Arguments

Value

If returnAll

• is FALSE: data filtered with the specified filters

• is TRUE: data with the additional column: keep or varNew (if specified), containing TRUE for records which fulfill the specified condition(s) and FALSE otherwise.

The output contains the additional attribute: msg which contains a message describing the filtered records.

Author(s)

Laure Cougnaud

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataDM <- dataADaMCDISCP01$ADSL

## single filter

# filter with inclusion criteria:
filterData(
	data = dataDM, 
	filters = list(var = "SEX", value = "M"),
	# optional
	labelVars = labelVars, verbose = TRUE
)

# filter with non-inclusion criteria
filterData(
	data = dataDM, 
	filters = list(var = "SEX", value = "M", rev = TRUE), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# filter based on inequality operator
filterData(
	data = dataDM, 
	filters = list(var = "AGE", value = 75, op = "<="), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# missing values are retained by default!
dataDMNA <- dataDM
dataDMNA[1 : 2, "AGE"] <- NA
filterData(
	data = dataDMNA, 
	filters = list(var = "AGE", value = 75, op = "<="), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# filter missing values on variable
filterData(
	data = dataDMNA, 
	filters = list(var = "AGE", value = 75, op = "<=", keepNA = FALSE), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# retain only missing values
filterData(
	data = dataDMNA, 
	filters = list(var = "AGE", value = NA, keepNA = TRUE), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# filter missing values
filterData(
	data = dataDMNA, 
	filters = list(var = "AGE", keepNA = FALSE), 
	# optional
	labelVars = labelVars, verbose = TRUE
)


## multiple filters

# by default the records fulfilling all conditions are retained ('AND')
filterData(
	data = dataDM, 
	filters = list(
		list(var = "AGE", value = 75, op = "<="),
		list(var = "SEX", value = "M")
	), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# custom operator:
filterData(
	data = dataDM, 
	filters = list(
		list(var = "AGE", value = 75, op = "<="),
		"|",
		list(var = "SEX", value = "M")
	), 
	# optional
	labelVars = labelVars, verbose = TRUE
)

# filter by group

# only retain adverse event records with worst-case severity
dataAE <- dataADaMCDISCP01$ADAE
dataAE$AESEV <- factor(dataAE$AESEV, levels = c("MILD", "MODERATE", "SEVERE"))
dataAE$AESEVN <- as.numeric(dataAE$AESEV)
nrow(dataAE)
dataAEWorst <- filterData(
	data = dataAE,
	filters = list(
		var = "AESEVN",		
		valueFct = max,
		varsBy = c("USUBJID", "AEDECOD"),
		keepNA = FALSE
	),
	# optional
	labelVars = labelVars, verbose = TRUE
)
nrow(dataAEWorst)

# post-processing function
# keep subjects with at least one severe AE:
dataSubjectWithSevereAE <- filterData(
  data = dataAE,
  filters = list(
    var = "AESEV",		
    value = "SEVERE",
    varsBy = "USUBJID",
    postFct = any
  ),
  # optional
  labelVars = labelVars, verbose = TRUE
)

# for each laboratory parameter: keep only subjects which have at least one
# measurement classified as low or high
dataLB <- subset(dataADaMCDISCP01$ADLBC, !grepl("change", PARAM))
dataLBFiltered <- filterData(
  data = dataLB,
  filters = list(
    var = "LBNRIND",		
    value = c("LOW", "HIGH"),
    varsBy = c("PARAMCD", "USUBJID"),
    postFct = any
  ),
  # optional
  labelVars = labelVars, verbose = TRUE
)

Filter data for a single filter

Description

Filter data for a single filter

Usage

filterDataSingle(
  data,
  filters,
  keepNA = TRUE,
  returnAll = FALSE,
  labelVars = NULL,
  labelData = "data"
)

Arguments

Value

Updated data with attributes:

• 'labelVars': input labelVars with any new variables if labelNew is specified.

• 'msg': message describing the filtering process

• 'warn': warning describing the filtering process

Author(s)

Laure Cougnaud

Force the evaluation of the parameters from config file.

Description

This function is only useful if some parameters should be lazy-evaluated in the report. These parameters should have the class: r-lazy. A typical use case is a parameter that consists of a R expression depending on objects created in a template report (typically data).
Parameters are searched in the environment in which this function is called from.

Usage

forceParams(params)

Arguments

Value

Input parameter list, with object(s) of class r-lazy evaluated.

Author(s)

Laure Cougnaud

See Also

getParamsFromConfig

Other clinical data reporting: checkReportTitles(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Examples

data <- mtcars
params <- list(label = "Cars dataset", nrow = structure("nrow(data)", class = "r-lazy"))
str(params)
str(forceParams(params))

Format data for interactive plot for clinical data

Description

Format data for interactive plot for clinical data

Usage

formatDataForPlotClinData(
  data,
  hoverVars = NULL,
  hoverLab = getLabelVar(hoverVars, labelVars = labelVars),
  hoverByVar = NULL,
  keyVar = NULL,
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  labelVars = NULL
)

Arguments

Value

SharedData object containing the data, with an extra column: 'hover' with the combined info from hoverVars, and the key defined as keyVar and group as id.

Author(s)

Laure Cougnaud

Format hover text for use in plotly interactive plots. The labels are wrapped to multiple lines if exceed the width of the plotly hover box, e.g. in case labels for points with same x/y coordinates overlap, and corresponding labels are truncated.

Description

Format hover text for use in plotly interactive plots. The labels are wrapped to multiple lines if exceed the width of the plotly hover box, e.g. in case labels for points with same x/y coordinates overlap, and corresponding labels are truncated.

Usage

formatHoverText(x, label, width = 50)

Arguments

Value

String with formatted hover label.

Author(s)

Laure Cougnaud

Format the info on paths from metadata

Description

Format the info on paths from metadata

Usage

formatPathDateInfoMetadata(summaryInfo, namesInfo)

Arguments

Value

A kable object, to be printed.

Format interactive plot, with possibility to download patient profiles on a click event.

Description

Format interactive plot, with possibility to download patient profiles on a click event.

Usage

formatPlotlyClinData(
  pl,
  data,
  idVar = "USUBJID",
  pathVar = NULL,
  pathDownload = TRUE,
  idFromDataPlot = FALSE,
  idVarPlot = "key",
  labelVarPlot = NULL,
  highlightOn = "plotly_click",
  highlightOff = "plotly_doubleclick",
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  selectVars = NULL,
  selectLab = getLabelVar(selectVars, labelVars = labelVars),
  keyVar = NULL,
  keyHighlightBox = FALSE,
  labelVars = NULL,
  verbose = FALSE
)

Arguments

Value

Updated plotly object.

Author(s)

Laure Cougnaud

Format data to a hierarchical data, in the format as required by the plotly sunburst and treemap.

Description

Note that new variables are created for each variable of interest (the variables are not overwritten) to avoid issues with cases where the value in the child and parent variables are the same.

Usage

formatToHierarchicalData(data, vars, valueVar)

Arguments

Value

Updated data.frame with vars in hierarchical format, with extra attributes (in 'metadat'):

• 'varID': String with column of output containing ID of specific element.
  This is a combination from the specified vars, or 'Overall' for the grand total.

• 'varParent': String with column of output containing ID of the parent element

• 'varLabel': String with column of output containing the label to display.
  This is usually the name of the child element.

Author(s)

Laure Cougnaud

Get axis label

Description

Get axis label

Usage

getAxisLab(axisVar, axisLab, labVar = NULL, data, labelVars)

Arguments

Value

String with label for the axis.

Set different variables for the x-axis labels

Description

Set different variables for the x-axis labels

Usage

getAxisLabs(data, var, labVars)

Arguments

Value

Named character vector. The names are the position in the x-axis, the values are the new labels.

Author(s)

Laure Cougnaud

Get axis limits for a ggplot plot from the input dataset.

Description

Get axis limits for a ggplot plot from the input dataset.

Usage

getAxisLimPlot(
  data,
  xVar,
  yVar,
  xLim = NULL,
  yLim = NULL,
  facetPars = NULL,
  refLineData = NULL
)

Arguments

Value

Data.frame with limits of the:

• x-axis: 'xmin'/'xmax'

• y-axis: 'ymin'/'ymax'

for each element of the facetting variable (if any).

Extract data for the reference lines

Description

This function especially extracts the data if an aesthetic variable is specified in the reference line parameters.

Usage

getDataReferenceLines(refLinePars, data, facetPars = NULL)

Arguments

Value

List of data for the lines

Author(s)

Laure Cougnaud

Get plot dimensions

Description

Get plot dimensions

Usage

getDimGgplot(gg)

Arguments

Value

Numeric vector with number of rows ('nrow') and columns ('ncol') of the plot

Author(s)

Laure Cougnaud

Get extra directory(ies) required for the clinical data review report

Description

By default, the 'figures', 'tables' and patient profiles folders (patientProfilePath parameter in the general config file, if specified) in the input directory are considered.

Usage

getExtraDirs(inputDir = ".", configDir = file.path(inputDir, "config"))

Arguments

Value

Character vector with extra directories required by the report

Author(s)

Laure Cougnaud

Get facetting variables from facet parameters.

Description

Get facetting variables from facet parameters.

Usage

getFacetVars(facetPars = list())

Arguments

Value

Character vector with facetting variable

Author(s)

Laure Cougnaud

Get function code

Description

Get function code

Usage

getFctCode(fct)

Arguments

Value

String with function code

Get the names of the ggplot function to use for the reference lines

Description

Get the names of the ggplot function to use for the reference lines

Usage

getFctTypeReferenceLines(refLinePars)

Arguments

Value

List of type of each reference lines, among: 'vline', 'hline' and 'abline'.

Author(s)

Laure Cougnaud

Get HTML toc

Description

Get HTML toc

Usage

getHTMLToc(toc)

Arguments

Value

Character vector with HTML toc

Get height of labels: title, subtitle or caption

Description

Get height of labels: title, subtitle or caption

Usage

getHeightLab(lab)

Arguments

Value

Integer with height in pixels for this element.

Author(s)

Laure Cougnaud

Get index of the line containing the HTML title in a vector of HTML strings

Description

Get index of the line containing the HTML title in a vector of HTML strings

Usage

getIndexHTMLTitle(x)

Arguments

Value

Integer vector with index of the title in the vector x

Get interim res file

Description

Get interim res file

Usage

getInterimResFile(intermediateDir = "./interim", mdFile)

Arguments

Value

String with path to the file with intermediate results.

Add jitter to the variable of the plot, based on the different groups of a grouping variable

Description

Add jitter to the variable of the plot, based on the different groups of a grouping variable

Usage

getJitterVar(data, var, byVar)

Arguments

Value

Numeric vector of length nrow(data) containing the jittered variable.

Author(s)

Laure Cougnaud

Get Javascript custom scripts required for specific clinical data functionalities.

Description

Get Javascript custom scripts required for specific clinical data functionalities.

Usage

getJsDepClinDataReview(
  type = c("collapsibleButton", "patientProfiles"),
  dep = NULL
)

Arguments

Value

List of htmlDependency. To include this dependency in a report e.g. generated with rmarkdown, these can be passed to the: extra_dependencies parameter of the output_format specific function, e.g.: rmarkdown::render(..., output_format = rmarkdown::html_document(extra_dependencies = dep))

Author(s)

Laure Cougnaud

Get path of the HTML file corresponding to a specific config file.

Description

The name of the Markdown file is based on:

• for the general config.yml file: the basename of the specified indexPath

• for other config file (each sub-report): the name of the config file, after removal of the 'config-' part.

Usage

getMdFromConfig(
  configFiles,
  indexPath = "index.Rmd",
  intermediateDir = "./interim"
)

Arguments

Value

String with path to the HTML file

Author(s)

Laure Cougnaud

Get Markdown header, for creation in Rmarkdown.

Description

The depth is extracted:

1. from the settings if a specified depth is provided in the 'rmd_file_depth' for the current knitted file

2. level parameter otherwise

Usage

getMdHeader(title, level = 1)

Arguments

Value

String with Markdown header, to be included in R within cat.

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Read metadata file

Description

Read the metadata file from a yaml format. This function checks for existance of the metadata file and its content. In particular, within the yaml file matches the following strings:

• path: Path to the data. More than one path is allowed.

• dateTime: Date and time, usually of the SDTM data creation. When printing the metadata in Rmd document, there is the possibility to add the date and time of the report generation. See knit_print.clinDataReviewMetadata.

• datasetInfo: General information about the data sets.

Usage

getMetadata(filePath, namesInfo)

Arguments

Details

Note that the input names do not necessarly have to match the exact names. For instance, the user can also write "dataTimeMySDTMData", and the function will parse for existance of the string "dataTime".

Value

A list of:

• summaryInfo Information extracted from the inputs path, and dateTime.

• datasetInfo Information extracted from datasetInfo.

Examples

# Create temporary yaml file
tmpdir <- tempdir()
library(yaml)

tmpYamlFile <- tempfile(
    pattern = "file", tmpdir = tempdir(), fileext = ".yml"
)
listArgs <- list(
    pathSDTMs = "path/To/SDTM",
    pathSDTMReformat = "path/To/SDTMReformat",
    dateTime = "20200101",
    datasetInfo = list(
        list(
            column1 = "ex.xpt",
            column2 = "20200101"
        ),
        list(
            column1 = "sl.xpt",
            column2 = "20200101",
            column3 = "OK"
        )
    )
)
write_yaml(
    listArgs,
    file = tmpYamlFile
)

# Run metadata
# Note: the 'datasetInfo' can also contain empty elements
getMetadata(filePath = tmpYamlFile)

Get parameter of function used for reference lines

Description

Get parameter of function used for reference lines

Usage

getParFctReferenceLines(type)

Arguments

Value

Character vector with parameter names of the functions

Author(s)

Laure Cougnaud

Get parameters from a config file

Description

Please note that the information from this config file and the general config file: config.yml are considered.
In case parameters are defined both in the general and specific config files, the parameter from the general config file is ignored.

Usage

getParamsFromConfig(
  configFile,
  configDir = file.path(inputDir, "config"),
  inputDir = "."
)

Arguments

Value

List with parameters from the specified configFile and the general config file: config.yml.
There are two specific handlers:

• parameters tagged with '[param] !r [value]' are evaluated in R, and their evaluated value is returned

• parameters tagged with '[param] !r-lazy [value]' are imported as character, and need to be further process with forceParams inside the report.

Parameters with YAML type 'r-lazy' are imported as character, with this additional class.

Author(s)

Laure Cougnaud

See Also

forceParams

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Get path ('href') property from hyperlink(s).

Description

Get path ('href') property from hyperlink(s).

Usage

getPathHyperlink(x)

Arguments

Value

Character vector of length x containing only the hyperlinks.

Author(s)

Laure Cougnaud

Get path of template clinical data report

Description

Get path of template clinical data report

Usage

getPathTemplate(file, package = "clinDataReview")

Arguments

Value

String with path to the template in the installed clinDataReview package

Author(s)

Laure Cougnaud

Examples

pathDivisionTemplate <- getPathTemplate("divisionTemplate.Rmd") # get path template in the package
tmp <- file.copy(from = pathDivisionTemplate, to = tempdir()) # copy to temporary dir (for example)
params <- list(reportTitle = "Adverse events")
rmarkdown::render(file.path(tempdir(), basename(pathDivisionTemplate))) # run file 

Extract variables displayed in the attached table, for each available plotting function of the clinDataReview package.

Description

This function is used in each plotting function of the package to extract the variable(s) displayed in the table associated to the plot and their associated labels.
This can also be used in the template reports, e.g. to extract reference variable(s) for the comparison table functionality in the plot template report.
The following framework is used:

• if variables to be displayed in the table (tableVars) are not specified:
  all variables displayed in the plot are selected, based on the plot arguments.
  For example: the variables displayed in the x and y axis and for coloring are extracted for the scatterplotClinData plotting function.
  Label for these variable(s) are extracted from the associated parameter (e.g. xLab for xVar and so on) or the general parameter for the variable labels (labelVars) if not specified.

• if variables to be displayed in the table (tableVars) are specified:
  these variable(s) are returned.
  The associated label(s) are extracted from the associated parameter (tableLab) or the general parameter for the variable labels (labelVars) if not specified.

For the functions: plotCountClinData, treemapClinData, sunburstClinData: value to represent are included in the table and colored with a bar.

Usage

getPlotTableVars(plotFunction, plotArgs)

Arguments

Value

Character vector with variable to include in the table, with extra attributes (passed to tableClinData):

• 'tableLab': Named character vector with labels for the table variables

• 'tablePars' : extra table parameters, only included if specified as input or specified internally.

labels and the table parameters .

Author(s)

Laure Cougnaud

Get margins and positions of specific elements for a clinical data plot

Description

The elements are positioned as following:

• on top of the plot

  1. title

  2. subtitle

  3. legend, if positioned on top of the plot

  4. facet title

• at the bottom of the plot

  1. label for the x-axis

  2. legend, if positioned on the bottom of the plot

  3. caption

Margins are computed based on the presence of these elements.
Only one line is counted for the legend, as plotly will extend the margin if necessary for the legend (for bottom legend).

Usage

getPositionAndMargins(
  title = NULL,
  subtitle = NULL,
  xLab = NULL,
  caption = NULL,
  facet = FALSE,
  includeLegend = TRUE,
  legendPosition = "right"
)

Arguments

Value

List with:

• 'margin': List with bottom ('t') and top ('t') margins in pixels

• 'position': List with position of the following plot elements:

  • on top of the plot: subtitle and legend (if positioned at the top).
    The position is defined as the distance in pixels from the top of the plotting area to the bottom of the element (yanchor = 'bottom')

  • at the bottom of the plot: caption, xLab and legend (if positioned at the bottom).
    The position is defined as the distance in pixels from the bottom of the plotting area to the top of the element (yanchor = 'top')
    Especially, the legend should be positioned with anchor 'top' such as the margins are automatically expanded if the legend contains multiple rows.

Author(s)

Laure Cougnaud

Get dimensions for a clinical data plot

Description

This function set sensitive defaults dimensions for a plot in the package. This includes:

• setting a default width for a figure to fit in a standard clinical data review report

• increasing the figure height:

  • for facetted plot, ensuring that each facet is relatively squared

  • if a caption, subtitle, title, title for the x-axis are specified

  • if a legend is set at the bottom or the top of the plot

• increasing the figure width if a legend is set at the left or the right of the plot

Usage

getSizePlot(
  width = NULL,
  height = NULL,
  gg = NULL,
  nrow = 1L,
  ncol = 1L,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  xLab = NULL,
  facet = FALSE,
  includeLegend = TRUE,
  legendPosition = "right",
  y = NULL
)

Arguments

Value

Numeric vector with width ('width') and height ('height') of the plot in pixels.

Author(s)

Laure Cougnaud

Get TOC numbering

Description

Get TOC numbering

Usage

getTocNumbering(levels)

Arguments

Value

Character vector with section numbers

Author(s)

Laure Cougnaud

Get a watermark, to be included in a clinical visualization

Description

Get a watermark, to be included in a clinical visualization

Usage

getWatermark(
  label = "EXPLORATORY",
  color = "lightgrey",
  fontsize = 20,
  file = NULL,
  width = 800,
  height = 500
)

Arguments

Value

The file with the watermark (invisibly).

Author(s)

Laure Cougnaud, Kirsten van Hoorde

See Also

Other watermark helpers: addWatermark()

Examples

# export a watermark to a file
watermark <- tempfile(pattern = "watermark", fileext = ".png")
getWatermark(file = watermark)

Clinical data format for bookdown report.

Description

This function is only meant to set sensitive defaults for gitbook.
gitbook can be used instead.

Usage

gitbook_clinDataReview_report(
  logo = NULL,
  logoHeight = "60px",
  split_by = "section+number",
  config = list(sharing = NULL, toc = list(collapse = "section")),
  extra_dependencies = NULL,
  css = NULL,
  ...
)

Arguments

Value

R Markdown output format to pass to render_book.

Author(s)

Laure Cougnaud

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), getParamsFromConfig(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Clinical data format for rmarkdown report.

Description

This function only kept for back-compatibility, html_document can be used instead.

Usage

html_clinDataReview_report(extra_dependencies = NULL, ...)

Arguments

Value

R Markdown output format to pass to render.

Author(s)

Laure Cougnaud

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport(), render_clinDataReviewReport()

Include output from clinical data, or list of such outputs in a Rmarkdown report, with an appropriate title.

Description

Include output from clinical data, or list of such outputs in a Rmarkdown report, with an appropriate title.

Usage

knitPrintClinDataReview(list, sep = ".", level = 1)

Arguments

Value

No returned value, the plots are included in the report. If a element in the list are empty (NULL), these elements (and the associated sections) are not included in the report.

Author(s)

Laure Cougnaud

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), postProcessReport(), render_clinDataReviewReport()

Print clinDataReviewTable object in a knitted document (e.g. Rmarkdown document).

Description

Print clinDataReviewTable object in a knitted document (e.g. Rmarkdown document).

Usage

## S3 method for class 'clinDataReview'
knit_print(x, ...)

Arguments

Author(s)

Laure Cougnaud

Print metadata file in the clinical data report

Description

This function receives the metadata information from getMetadata and prints them in a format for an Rmd report. In general, any list could be called as long as it is composed by two elements:

• summaryInfo: an R object.

• datasetInfo: a data.frame or a matrix.

The first (summaryInfo) is printed as kable object and the second (datasetInfo) is printed as hide/show html button with the function collapseHtmlContent.

Usage

## S3 method for class 'clinDataReviewMetadata'
knit_print(x, options = list(), ...)

Arguments

Value

Nothing. The tables are ready to be printed in Rmd.

html code to include metadata in a report

Set layout for a clinical data plot.

Description

Set layout for a clinical data plot.

Usage

layoutClinData(
  xLab = NULL,
  yLab = NULL,
  title = NULL,
  caption = NULL,
  subtitle = NULL,
  includeLegend = FALSE,
  legendPosition = "right",
  facet = FALSE,
  nrow = 1L,
  ncol = 1L,
  width,
  height,
  watermark = NULL,
  ...
)

Arguments

Value

The updated plotly object

Author(s)

Laure Cougnaud

Merge multiple session information

Description

Merge multiple session information

Usage

## S3 method for class 'sessionInfo'
merge(...)

Arguments

Value

sessionInfo with combined information

Author(s)

Laure Cougnaud

Move skeleton files from the package to a directory

Description

This function moves the files used to create the skeleton from the package to a specified directory.

Usage

moveSkeletonFiles(dir)

Arguments

Value

Nothing, the files are available in the specified directory.

Move data sets from clinUtils

Description

Move SDTM data sets available in clinUtils into a specified local directory.

Usage

moveXpt(dir)

Arguments

Value

Nothing, the data are saved in the dedicated location.

Interactive plot of 'count' data

Description

Note: the table and plot are not (yet) linked.

Usage

plotCountClinData(
  data,
  vars,
  varsLab = getLabelVar(vars, labelVars = labelVars),
  valueVar,
  valueLab = getLabelVar(valueVar, labelVars = labelVars),
  colorVar = NULL,
  colorLab = getLabelVar(valueVar, labelVars = labelVars),
  colorPalette = getOption("clinDataReview.colors"),
  colorRange = NULL,
  valueType = "total",
  title = paste(c(paste(valueLab, "by", paste(varsLab, collapse = " and ")), titleExtra),
    collapse = "<br>"),
  titleExtra = NULL,
  subtitle = NULL,
  caption = NULL,
  labelVars = NULL,
  width = NULL,
  height = NULL,
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  hoverVars = c(vars, valueVar, colorVar),
  hoverLab = getLabelVar(hoverVars, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  verbose = FALSE,
  typePlot = c("sunburst", "treemap"),
  watermark = NULL
)

Arguments

<a href="./path-to-report">label</a>

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other visualizations of summary statistics for clinical data: barplotClinData(), boxplotClinData(), errorbarClinData(), sunburstClinData(), treemapClinData()

Convert clinical data Markdown files to HTML

Description

Convert clinical data Markdown files to HTML

Usage

postProcessReport(
  inputDir = ".",
  configDir = file.path(inputDir, "config"),
  indexPath = file.path(inputDir, "index.Rmd"),
  extraDirs = getExtraDirs(inputDir = inputDir, configDir = configDir),
  outputDir = "./report",
  intermediateDir = "./interim",
  mdFiles = NULL,
  nCores = 1,
  logFile = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

String with path to the front page of the report.

Author(s)

Laure Cougnaud

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), render_clinDataReviewReport()

Print a clinDataReview object in the console

Description

Print a clinDataReview object in the console

Usage

## S3 method for class 'clinDataReview'
print(x, ...)

Arguments

Value

No returned value, the object is printed into the console.

Process a dataset.

Description

This function is intended to automate all data processing steps for use in the 'clinDataReview' reports using config files.

Usage

processData(data, processing, labelVars = NULL, ...)

Arguments

Value

Data.frame with processed data, with extra attribute: labelVars.

Author(s)

Laure Cougnaud

Examples

library(clinUtils)

data(dataADaMCDISCP01)

dataLB <- dataADaMCDISCP01$ADLBC

# filter and annotate data
processData(
  data = dataLB,
  processing = list(
    list(filter = list(var = "ANL01FL", value = "Y")),
    list(annotate = list(vars = "ANRIND", varFct = 'factor(ANRIND, levels = c("L", "N", "H"))'))
  )
)

## multiple filtering steps:

# If these are specified in the same 'filter' step condition, these are considered independently,
# and the selected records combined with an 'AND' operator.
# Example: consider only records:
# - with analysis flag AND
# - from subject with high/low measurement (for all records) for each parameter
processData(
  data = dataLB,
  processing = list(
    list(filter = list(
      list(var = "ANL01FL", value = "Y"),
      list(var = "ANRIND", value = c("L", "H"), 
           postFct = any, varsBy = c("USUBJID", "PARAM"))
    )
    )
  )
)

# a custom operator to combine the selected records can be specified
# Example: consider only records:
# - with analysis flag OR
# - from subject with high/low measurement (for all records) for each parameter
processData(
  data = dataLB,
  processing = list(
    list(filter = list(
      list(var = "ANL01FL", value = "Y"),
      "|",
      list(var = "ANRIND", value = c("L", "H"), 
           postFct = any, varsBy = c("USUBJID", "PARAM"))
    )
    )
  )
)

# If the filtering conditions are specified in different filtering steps, these are
# considered sequentially.
# Example: 
# 1) consider only analysis records and 
# 2) from these records, consider only subject with high/low measurement for 
# each parameter
processData(
  data = dataLB,
  processing = list(
    list(filter = list(var = "ANL01FL", value = "Y")),
    list(filter = list(var = "ANRIND", value = c("L", "H"), 
      postFct = any, varsBy = c("USUBJID", "PARAM")))
  )
)
# Note for this particular 

Rename variable names of metadata info

Description

Rename variable names referring to the paths and the date.

Usage

renamePathDateInfoMetadata(summaryInfo, namesInfo)

Arguments

Value

A matrix, same as input summaryInfo with renamed variable names.

Render one chapter of a clinical report, based on a configuration file

Description

Render one chapter of a clinical report, based on a configuration file

Usage

renderChapter(
  configFile,
  configGeneralParams = getParamsFromConfig(configFile = "config.yml", configDir =
    configDir, inputDir = inputDir),
  configDir = file.path(inputDir, "config"),
  indexPath = file.path(inputDir, "index.Rmd"),
  inputDir = ".",
  intermediateDir = "./interim",
  logFile = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

No output file, the Markdown report for the chapter and the knit_meta object is available in the intermediateDir directory.
If the input parameters are not correctly extracted, NULL is returned.

Render a rmarkdown file, possibly in a new R session

Description

This has the possibility to save output in a log file, and saving also session information.

Usage

renderFile(input, encoding = "UTF-8", params = NULL, logFile = NULL, ...)

Arguments

Details

Note: this function is inspired from xfun::Rscript_call

Value

Output of the function with additional attribute: 'sessionInfo' containing the details of the session information. If the report fails, an error message is returned.

Author(s)

Laure Cougnaud

Render a clinical data review report.

Description

Render a clinical data review report.

Usage

render_clinDataReviewReport(
  configFiles = NULL,
  configDir = file.path(inputDir, "config"),
  logFile = NULL,
  indexPath = file.path(inputDir, "index.Rmd"),
  inputDir = ".",
  outputDir = "./report",
  intermediateDir = "./interim",
  extraDirs = getExtraDirs(inputDir = inputDir, configDir = configDir),
  quiet = FALSE,
  verbose = TRUE,
  nCores = 1
)

Arguments

Value

String with path to the front page of the report.

Process

This function is based on the render_book function, with the extra functionalities:

• specification of chapter-specific input parameters, specified in YAML configuration files

• (optional) creation of each chapter in parallel if nCores > 1. In that case, all chapters are run in parallel, excepted the chapter(s) run internally in parallel (config file with parallel set to 'TRUE').

• (optional) split of each chapter into html file specific for each chapter, by specifying the split_by parameter in the chapter-specific config file

This consists of:

1. importing the general config file ('config'.yml) to identify each report of interest ('config' tag)

2. for each report of interest:

   • loading the report specific parameters from the associated 'config' file (see the getParamsFromConfig function)

   • if the template should be extracted from a specified package (templatePackage tag), this template is copied to the current directory. Please note that if a file with same name is available in the working directory, this file will be overwritten.

   • running the report ('template' tag) with the associated parameters in a new R session for reproducibility, to obtain the associated Markdown file.
     This step is parallelized across the different config files, if the nCores parameter is specified.

3. checking if the associated Markdown and rds file (list of Js dependencies) are available in intermediateDir

4. split each chapter into separated Markdown documents, based on the split_by parameter (specified at the report or config level)

5. conversion of each Markdown document to an HTML document.
   This step is parallelized across the different Markdown documents, if the nCores parameter is specified.

6. build the book:

   1. creation of a common TOC for the book

   2. inclusion of the TOC in each Markdown file

   3. update of the section number in each chapter

   4. inclusion of the section number in each HTML file name

If the execution of a specific report fails with error, a warning message is triggered. A report containing only the specified title is created, to ensure output consistency (especially html file numbering) in case the report succeeds.

Available template report

see ? `clinDataReview-templates` for a list of clinical data template report available in the package.

Extension to chapter-specific split

The bookdown 'split_by' parameter is extended, to support:

• chapter-specific split, specified in the configuration file of the specific chapter, via the split_by parameter

• specification as a number (if specified within a config file), e.g. '0' for no split, 1' for chapter, '2' for section, '3' for subsection, ...

• split at section level higher than 2 (until 7) (if specified within a config file)

Author(s)

Laure Cougnaud

See Also

Other clinical data reporting: checkReportTitles(), forceParams(), getMdHeader(), getParamsFromConfig(), gitbook_clinDataReview_report(), html_clinDataReview_report(), knitPrintClinDataReview(), postProcessReport()

Scatterplot of variables of interest for clinical data visualization.

Description

The parameters for this visualization are based on ggplot2 (aesthetic, scale, ...), parameter specification, unlike the other visualizations of the package.

Usage

scatterplotClinData(
  data,
  xVar,
  yVar,
  xLab = getLabelVar(var = xVar, labelVars = labelVars),
  xLabVar = NULL,
  yLab = getLabelVar(var = yVar, labelVars = labelVars),
  yLabVar = NULL,
  aesPointVar = list(),
  pointPars = list(),
  aesLineVar = list(),
  linePars = list(),
  lineInclude = length(aesLineVar) > 0,
  aesSmoothVar = list(),
  smoothPars = list(),
  smoothInclude = length(c(aesSmoothVar, smoothPars)) > 0,
  aesLab,
  xTrans = "identity",
  yTrans = "identity",
  xPars = list(),
  yPars = list(),
  xLabVars = NULL,
  yLim = NULL,
  xLim = NULL,
  yLimExpandData = TRUE,
  xLimExpandData = TRUE,
  title = paste(c(paste(yLab, "vs", xLab), titleExtra), collapse = "<br>"),
  titleExtra = NULL,
  caption = NULL,
  subtitle = NULL,
  facetPars = list(),
  facetType = c("wrap", "grid"),
  scalePars = list(),
  themePars = list(legend.position = "bottom"),
  refLinePars = NULL,
  labelVars = NULL,
  width = NULL,
  height = NULL,
  hoverVars,
  hoverLab,
  idVar = "USUBJID",
  idLab = getLabelVar(idVar, labelVars = labelVars),
  idHighlightBox = FALSE,
  pathVar = NULL,
  pathExpand = FALSE,
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  selectVars = NULL,
  selectLab = getLabelVar(selectVars, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  watermark = NULL,
  verbose = FALSE
)

Arguments

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other Clinical data visualization of individual profiles.: timeProfileIntervalPlot()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataLB <- dataADaMCDISCP01$ADLBC
dataDM <- dataADaMCDISCP01$ADSL
dataLB <- annotateData(dataLB, annotations = list(data = dataDM))
# subset of the data for the example
dataLB <- subset(dataLB, VISIT %in% c("SCREENING 1", "WEEK 2", "WEEK 8"))

## time profile

dataPlot <- subset(dataLB, PARAMCD == "ALT")

# with relative day
scatterplotClinData(
	data = dataPlot, 
	xVar = "ADY",
	yVar = "LBSTRESN",
	aesPointVar = list(color = "TRTP", fill = "TRTP"),
	aesLineVar = list(group = "USUBJID", color = "TRTP"),
	labelVars = labelVars
)

# with actual visit
dataPlot$AVISIT <- with(dataPlot, reorder(trimws(AVISIT), AVISITN))
scatterplotClinData(
	data = dataPlot, 
	xVar = "AVISIT",
	yVar = "LBSTRESN",
	aesPointVar = list(color = "TRTP", fill = "TRTP"),
	aesLineVar = list(group = "USUBJID", color = "TRTP"),
	labelVars = labelVars
)

# add number of subjects below each visit
	
if (requireNamespace("inTextSummaryTable", quietly = TRUE)) {

# compute number of subjects by visit
summaryTable <- inTextSummaryTable::computeSummaryStatisticsTable(
	dataPlot,
	rowVar = "AVISIT",
	stats = "n"
)
# add it in the data
dataPlot <- merge(dataPlot, summaryTable[, c("AVISIT", "n")], all.x = TRUE)
dataPlot$n <- paste0("N=", dataPlot$n)

scatterplotClinData(
	data = dataPlot, 
	xVar = "AVISIT", xLabVars = c("AVISIT", "n"),
	yVar = "LBSTRESN",
	aesPointVar = list(color = "TRTP", fill = "TRTP"),
	aesLineVar = list(group = "USUBJID", color = "TRTP"),
	labelVars = labelVars
)

}

## pairwise comparison plot of two parameters of interest:

# format data long -> wide format (one column per lab param)
dataPlot <- subset(dataLB, PARAMCD %in% c("ALT", "AST"))
dataPlot <- stats::aggregate(
	LBSTRESN ~ USUBJID + VISIT + VISITNUM + PARAMCD, 
	data = dataPlot,
	FUN = mean
)
dataPlotWide <- stats::reshape(
	data = dataPlot,
	timevar = "PARAMCD", idvar = c("USUBJID", "VISIT", "VISITNUM"),
	direction = "wide"
)
colnames(dataPlotWide) <- sub("^LBSTRESN.", "", colnames(dataPlotWide))
# scatterplot per visit
scatterplotClinData(
	data = dataPlotWide, 
	xVar = "ALT", yVar = "AST",
	aesPointVar = list(color = "USUBJID", fill = "USUBJID"),
	themePars = list(legend.position = "none"),
	facetPars = list(facets = "VISIT"),
	labelVars = labelVars,
	subtitle = "Visualization is split by visit",
	caption = "Points are colored by subject ID"
)

# scatterplot with all visits, link subjects
xLab <- getLabelParamcd(paramcd = "ALT", data = dataLB, 
	paramcdVar = "PARAMCD", paramVar = "PARAM")
yLab <- getLabelParamcd(paramcd = "AST", data = dataLB, 
	paramcdVar = "PARAMCD", paramVar = "PARAM")
scatterplotClinData(
	data = dataPlotWide,
	xVar = "ALT", yVar = "AST",
	xLab = xLab,
	yLab = yLab,
	aesPointVar = list(color = "VISIT", fill = "VISIT"),
	aesLineVar = list(group = "USUBJID"),
	labelVars = labelVars
)

# scatterplot of different visits versus baseline

# add baseline as extra column:
dataPlot <- subset(dataLB, PARAMCD == "ALT")
dataPlotBL <- subset(dataPlot, VISIT == "SCREENING 1")
dataPlotBL <- dataPlotBL[with(dataPlotBL, order(USUBJID, -ADY)), ]
dataPlotBL <- dataPlotBL[!duplicated(dataPlotBL$USUBJID), ]
dataPlot$LBSTRESNBL <- dataPlot[match(dataPlot$USUBJID, dataPlotBL$USUBJID), "LBSTRESN"]

# sort visits:
dataPlot$VISIT <- with(dataPlot, reorder(VISIT, VISITNUM))

xLab <- paste(labelVars["LBSTRESN"], "for last screening visit")
yLab <- paste(labelVars["LBSTRESN"], "at visit X")
paramLab <- getLabelParamcd(paramcd = "ALT", data = dataLB, 
	paramcdVar = "PARAMCD", paramVar = "PARAM")
scatterplotClinData(
	data = dataPlot, 
	xVar = "LBSTRESNBL", xLab = xLab,
	yVar = "LBSTRESN", yLab = yLab,
	aesPointVar = list(color = "USUBJID", fill = "USUBJID"),
	aesLineVar = list(group = "USUBJID", color = "USUBJID"),
	hoverVars = c("USUBJID", "VISIT", "ADY", "LBSTRESN"),
	labelVars = labelVars,
	facetPars = list(facets = "VISIT"),
	themePars = list(legend.position = "none"),
	title = paste("Comparison of actual value of", 
		paramLab,
		"at each visit versus baseline"
	),
	refLinePars = list(
		list(slope = 1, intercept = 0, linetype = 1, color = "black", 
			label = FALSE),
		list(yintercept = "A1LO", linetype = 2, color = "blue"),
		list(yintercept = "A1HI", linetype = 2, color = "purple", 
			label = "Reference Range Upper Limit")
	)
)


## scatterplot with smoothing layer

data <- data.frame(
  USUBJID = c(rep('subj1', 20), rep('subj2', 20)),
  time = rep( 1:20 , 2 ),
  response =  c(1:20, 50:31) + runif(min =-3, max = +3, 40),
  treat =  rep(c('trA', 'trB'), 20),
  stringsAsFactors = FALSE
)

# smoothing per subject
scatterplotClinData(
  data = data,
  xVar = "time", yVar = "response",
  aesPointVar = list(color = "treat"),
  aesLineVar = list(group = 'USUBJID'),
  linePars = list(linetype='dotted'),
  aesSmoothVar = list(color='USUBJID', group='USUBJID'), 
  smoothPars =  list(alpha=0.5, size=0.3 , se=TRUE, color = 'black')
)


# plot smoothing over subjects
scatterplotClinData(
  data = data,
  xVar = "time", yVar = "response",
  aesPointVar = list(color = "treat"),
  aesLineVar = list(group = 'USUBJID'),
  linePars = list(linetype='dotted'),
  aesSmoothVar = list(), 
  smoothPars =  list(alpha=0.5, size=0.3 , se=TRUE, color = 'black')
)

# add a selection box
if(interactive()){
  dataPlot <- subset(dataLB, PARAMCD == "ALT")
  dataPlot$TRTA <- with(dataPlot, reorder(TRTA, TRTAN))
  scatterplotClinData(
    data = dataPlot, 
    xVar = "ADY",
    yVar = "LBSTRESN",
    aesPointVar = list(fill = "TRTA", color = "TRTA"),
    aesLineVar = list(group = "USUBJID", color = "TRTA"),
    selectVars = "TRTA",
    labelVars = labelVars
  )
}

Set facetting layout for 'wrap' facetting.

Description

By default, the number of columns is 2.

Usage

setFacetLayoutWrap(data, facetPars = list())

Arguments

Value

Updated facetPars.

Author(s)

Laure Cougnaud

Get standard palette for the staticScatterplotClinData function.

Description

Get standard palette for the staticScatterplotClinData function.

Usage

setPaletteStaticScatterplotClinData(data, var, aes, scalePars, geomAes, ...)

Arguments

Value

List with: scalePars and geomAes, each of those potentially updated with default palette(s).

Author(s)

Laure Cougnaud

Split a chapter based on the 'split_by' parameter.

Description

Split a chapter based on the 'split_by' parameter.

Usage

splitChapter(
  configFile = NULL,
  configDir = "./config",
  mdFile = NULL,
  indexPath = "index.Rmd",
  intermediateDir = "./interim",
  outputDir = "./report",
  verbose = TRUE
)

Arguments

Value

No return value, the Markdown files are split as specified.

Extension to chapter-specific split

The bookdown 'split_by' parameter is extended, to support:

• chapter-specific split, specified in the configuration file of the specific chapter, via the split_by parameter

• specification as a number (if specified within a config file), e.g. '0' for no split, 1' for chapter, '2' for section, '3' for subsection, ...

• split at section level higher than 2 (until 7) (if specified within a config file)

Author(s)

Laure Cougnaud

Scatterplot of variables of interest for clinical data visualization

Description

Scatterplot of variables of interest for clinical data visualization

Usage

staticScatterplotClinData(
  data,
  xVar,
  yVar,
  xLab = getLabelVar(xVar, labelVars = labelVars),
  yLab = getLabelVar(yVar, labelVars = labelVars),
  aesPointVar = list(),
  pointPars = list(),
  aesLineVar = list(),
  linePars = list(),
  lineInclude = length(c(aesLineVar, linePars)) > 0,
  aesSmoothVar = list(),
  smoothPars = list(),
  smoothInclude = length(c(aesSmoothVar, smoothPars)) > 0,
  aesLab,
  xTrans = "identity",
  yTrans = "identity",
  xPars = list(),
  yPars = list(),
  xLabVars = NULL,
  yLim = NULL,
  xLim = NULL,
  yLimExpandData = TRUE,
  xLimExpandData = TRUE,
  title = paste(c(paste(yLab, "vs", xLab), titleExtra), collapse = "<br>"),
  titleExtra = NULL,
  facetPars = list(),
  facetType = c("wrap", "grid"),
  scalePars = list(),
  themePars = list(legend.position = "bottom"),
  refLinePars = NULL,
  labelVars = NULL,
  hoverVars = NULL,
  geomType = c("point", "col")
)

Arguments

Value

ggplot object

Author(s)

Laure Cougnaud, Adriaan Blommaert

Sunburst interactive plot.

Description

Note: the table and plot are not (yet) linked.

Usage

sunburstClinData(...)

Arguments

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other visualizations of summary statistics for clinical data: barplotClinData(), boxplotClinData(), errorbarClinData(), plotCountClinData(), treemapClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataAE <- dataADaMCDISCP01$ADAE
dataDM <- dataADaMCDISCP01$ADSL

## example of basic sunburst:

# sunburst takes as input table with counts
if (requireNamespace("inTextSummaryTable", quietly = TRUE)) {

# total counts: Safety Analysis Set (patients with start date for the first treatment)
dataTotal <- subset(dataDM, RFSTDTC != "")

# compute adverse event table
tableAE <- inTextSummaryTable::getSummaryStatisticsTable(
		
	data = dataAE,
	rowVar = c("AESOC", "AEDECOD"),
	dataTotal = dataTotal,
	rowOrder = "total",
	labelVars = labelVars,
	stats = inTextSummaryTable::getStats("count"),
	
	# plotly treemap requires records (rows) for each group
	rowVarTotalInclude = "AEDECOD",
	outputType = "data.frame-base"

)

dataSunburst <- tableAE

dataSunburst$n <- as.numeric(dataSunburst$n)

# create plot
sunburstClinData(
	data = dataSunburst,
	vars = c("AESOC", "AEDECOD"),
	valueVar = "n",
    valueLab = "Number of patients with adverse events"
)

## example where sum(counts) of child = counts of parent

# counts of patients per arm/site
tableDM <- inTextSummaryTable::getSummaryStatisticsTable(
	data = dataDM,
	rowVar = c("ARM", "SITEID"),
	labelVars = labelVars,
	# plotly treemap requires records (rows) for each group
	rowVarTotalInclude = "SITEID",
	rowTotalInclude = TRUE,
	outputType = "data.frame-base"
)
tableDM$statN <- as.numeric(tableDM$statN)

# create the plot
sunburstClinData(
	data = tableDM,
	vars = c("ARM", "SITEID"),
	valueVar = "statN", valueLab = "Counts of patients",
	valueType = "total",
	caption = "The sectors are colored by category.",
	subtitle = "Group: treatment and site"
)

}

Create a 'clinical data table', associated to a plot.

Description

Interactive table is created, with the possibility to have clickeable link to patient-specific report, and included within a button.

Usage

tableClinData(
  data,
  idVar = "USUBJID",
  idLab = getLabelVar(idVar, labelVars = labelVars),
  keyVar = NULL,
  keyLab = getLabelVar(keyVar, labelVars = labelVars),
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  pathExpand = FALSE,
  tableVars = colnames(data),
  tableLab = getLabelVar(tableVars, labelVars = labelVars),
  tableButton = TRUE,
  tablePars = list(),
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  labelVars = NULL,
  verbose = FALSE
)

Arguments

<a href="./path-to-report">label</a>

Value

datatable

Author(s)

Laure Cougnaud

Visualize time intervals across subjects/parameters.

Description

Visualize time intervals across subjects/parameters.

Usage

timeProfileIntervalPlot(
  data,
  paramVar,
  paramLab = getLabelVar(paramVar, labelVars = labelVars),
  paramVarSep = " - ",
  paramGroupVar = NULL,
  timeStartVar,
  timeStartLab = getLabelVar(timeStartVar, labelVars = labelVars),
  timeEndVar,
  timeEndLab = getLabelVar(timeEndVar, labelVars = labelVars),
  timeStartShapeVar = NULL,
  timeStartShapeLab = getLabelVar(timeStartShapeVar, labelVars = labelVars),
  timeEndShapeVar = NULL,
  timeEndShapeLab = getLabelVar(timeEndShapeVar, labelVars = labelVars),
  shapePalette = NULL,
  colorVar = NULL,
  colorLab = getLabelVar(colorVar, labelVars = labelVars),
  colorPalette = NULL,
  alpha = 1,
  yLab = NULL,
  xLab = paste(c(timeStartLab, timeEndLab), collapse = " and "),
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  labelVars = NULL,
  width = 800,
  height = NULL,
  hoverVars,
  hoverLab,
  idVar = "USUBJID",
  idLab = getLabelVar(idVar, labelVars = labelVars),
  pathVar = NULL,
  pathLab = getLabelVar(pathVar, labelVars = labelVars),
  id = paste0("plotClinData", sample.int(n = 1000, size = 1)),
  selectVars = NULL,
  selectLab = getLabelVar(selectVars, labelVars = labelVars),
  table = FALSE,
  tableVars,
  tableLab,
  tableButton = TRUE,
  tablePars = list(),
  watermark = NULL,
  verbose = FALSE
)

Arguments

<a href="./path-to-report">label</a>

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other Clinical data visualization of individual profiles.: scatterplotClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataAE <- dataADaMCDISCP01$ADAE

# basic plot
timeProfileIntervalPlot(
	data = dataAE,
	paramVar = "USUBJID",
	# time-variables
	timeStartVar = "ASTDY",
	timeEndVar = "ASTDY",
	# colored by severity
	colorVar = "AESEV",
	labelVars = labelVars
)

# add caption & subtitle
timeProfileIntervalPlot(
	data = dataAE,
	paramVar = "USUBJID",
	timeStartVar = "ASTDY",
	timeEndVar = "ASTDY",
	colorVar = "AESEV",
	labelVars = labelVars,
	title = "Adverse events",
	subtitle = "Time intervals",
	caption = "Day is relative to the study baseline"
)

# add a selection box
if(interactive()){
  timeProfileIntervalPlot(
    data = dataAE,
    paramVar = "USUBJID",
    # time-variables
    timeStartVar = "ASTDY",
    timeEndVar = "ASTDY",
    # colored by severity
    colorVar = "AESEV",
    labelVars = labelVars,
    selectVars = "AEDECOD"
  )
}

Transform data.

Description

Transform data from long to wide format. This function converts formats with the stats::reshape function.

Usage

transformData(data, transformations, verbose = FALSE, labelVars = NULL)

Arguments

Value

A data.frame in wide format.

Author(s)

Laure Cougnaud

Treemap interactive plot.

Description

Note: the table and plot are not (yet) linked.

Usage

treemapClinData(...)

Arguments

Value

Either:

• if a table is requested: a clinDataReview object, a.k.a a list with the 'plot' (plotly object) and 'table' (datatable object)

• otherwise: a plotly object

Author(s)

Laure Cougnaud

See Also

Other visualizations of summary statistics for clinical data: barplotClinData(), boxplotClinData(), errorbarClinData(), plotCountClinData(), sunburstClinData()

Examples

library(clinUtils)

data(dataADaMCDISCP01)
labelVars <- attr(dataADaMCDISCP01, "labelVars")

dataDM <- dataADaMCDISCP01$ADSL
dataAE <- dataADaMCDISCP01$ADAE

library(plyr)

## basic treemap:

# treemap takes as input table with counts
if (requireNamespace("inTextSummaryTable", quietly = TRUE)) {

# total counts: Safety Analysis Set (patients with start date for the first treatment)
dataTotal <- subset(dataDM, RFSTDTC != "")

# compute adverse event table
tableAE <- inTextSummaryTable::getSummaryStatisticsTable(
		
	data = dataAE,
	rowVar = c("AESOC", "AEDECOD"),
	dataTotal = dataTotal,
	rowOrder = "total",
	labelVars = labelVars,
	stats = inTextSummaryTable::getStats("count"),
	
	# plotly treemap requires records (rows) for each group
	rowVarTotalInclude = "AEDECOD",
	outputType = "data.frame-base"

)

dataPlot <- tableAE

dataPlot$n <- as.numeric(dataPlot$n)

# create plot
treemapClinData(
	data = dataPlot,
	vars = c("AESOC", "AEDECOD"),
	valueVar = "n",
    valueLab = "Number of patients with adverse events"
)

## treemap with coloring

# extract worst-case scenario
dataAE$AESEVN <- as.numeric(factor(dataAE$AESEV, levels = c("MILD", "MODERATE", "SEVERE")))
if(any(is.na(dataAE$AESEVN)))
	stop("Severity should be filled for all subjects.")

dataAEWC <- ddply(dataAE, c("AESOC", "AEDECOD", "USUBJID"), function(x){
	x[which.max(x$AESEVN), ]
})
dataTotalRow <- list(AEDECOD = 
	ddply(dataAEWC, c("AESOC", "USUBJID"), function(x){
		x[which.max(x$AESEVN), ]
	})
)


# compute adverse event table
tableAE <- inTextSummaryTable::getSummaryStatisticsTable(
		
	data = dataAEWC,
	rowVar = c("AESOC", "AEDECOD"),
	var = "AESEVN",
	dataTotal = dataTotal,
	rowOrder = "total",
	labelVars = labelVars,
		
	# plotly treemap requires records (rows) for each group
	rowVarTotalInclude = "AEDECOD",
	dataTotalRow = dataTotalRow,
	outputType = "data.frame-base"

)

dataPlot <- tableAE

dataPlot$statN <- as.numeric(dataPlot$statN)
dataPlot$statMean <- as.numeric(dataPlot$statMean)

# create plot
treemapClinData(
	data = dataPlot,
	vars = c("AESOC", "AEDECOD"),
	valueVar = "statN", valueLab = "Number of patients with adverse events",
	colorVar = "statMean", colorLab = "Mean severity"
)

}

Get formula for a specific variable, to be used in aesthetic specification in plot_ly.

Description

Get formula for a specific variable, to be used in aesthetic specification in plot_ly.

Usage

varToFm(var)

Arguments

Value

as.formula

Author(s)

Laure Cougnaud

Zip the clinical data report

Description

Create a zip folder of clinical data reports with a redirect page. The clinical data report out of the render_clinDataReviewReport is copied into a new folder. A redirect html page is created to enable the user to navigate the report without needing to look into the new directory.

Usage

zipClinDataReview(
  reportDir = "report",
  newDir = "report_dependencies",
  redirectPage = "report.html",
  zipFolder = "report.zip"
)

Arguments

Value

The zip folder is created in the specified location.