Check if specified variables are in the data. If they are not, they are removed from specified variables and a message is printed.

Description

Check if specified variables are in the data. If they are not, they are removed from specified variables and a message is printed.

Usage

checkVarInData(var, data, label)

Arguments

Value

var present in data, NULL if empty

Colors of 'clinUtils'

Description

Default color palette is the color-blind viridis palette. See documentation of viridis.

Usage

clinColors(n, alpha = 1, begin = 0, end = 1, direction = 1, option = "D")

Arguments

Value

The viridis function.

Linetypes of 'clinUtils'

Description

A set of default linetypes are available as a vector.

Usage

clinLinetypes

Format

An object of class character of length 6.

Value

A character vector of linetypes.

Shapes of 'clinUtils'

Description

A set of default shapes are available as named vector.

Usage

clinShapes

Format

An object of class integer of length 24.

Value

A numeric vector of shapes.

Shapes of 'clinUtils' as text

Description

A set of default shapes are available as character vector.

Usage

clinShapesText

Format

An object of class character of length 24.

Value

A character vector of shapes.

Parameters for all palette functions for clinical visualizations.

Description

Parameters for all palette functions for clinical visualizations.

Arguments

Value

Specific palettes used in clinUtils.

Color palette for a standard CDISC Normal/Reference Range Indicator.

Description

Color palette for a standard CDISC Normal/Reference Range Indicator.

Usage

colorPaletteNRIND

Format

A named character vector with color for typical Normal Reference Range Indicator variable:

• "LOW": orange

• "NORMAL": green4

• "HIGH": orange

• "ABNORMAL": red

• "UNKNOWN" or 'NA': grey

• "NA": grey

Get differences between two data.frames

Description

Get differences between two data.frames

Usage

compareDiff(
  newData,
  oldData,
  referenceVars = intersect(colnames(newData), colnames(oldData)),
  changeableVars = NULL
)

Arguments

Value

Object of class 'diff.data', i.e. a data.frame with columns:

• 'Comparison type': type of difference between the old and new data, either:

  • 'Change': records present both in new and old data, based on the reference variables, but with difference(s) in changeable vars

  • 'Addition': records with reference variables present in new but not in old data

  • 'Removal': records with reference variables present in old but not in new data

• 'Version': 'Previous' or 'Current' depending if record represents content from old or new data respectively

• referenceVars

• changeableVars

Identification of the differences between datasets

To identify the differences between datasets, the following steps are followed:

1. removal of records identical between the old and new dataset (will be considered as 'Identical' later on)

2. records with a reference value present in the old dataset but not in the new dataset are considered 'Removal'

3. records with a reference value present in the new dataset but not in the old dataset are considered 'Addition'

4. records with reference value present both in the new and old dataset, after filtering of identical records and with difference in the changeable variables are considered 'Change'

Author(s)

Laure Cougnaud

Compare tables

Description

Compare tables

Usage

compareTables(
  newData,
  oldData,
  referenceVars = intersect(colnames(newData), colnames(oldData)),
  changeableVars = NULL,
  outputType = c("table-comparison", "newData-diff", "oldData-diff",
    "table-comparison-interactive", "newData-diff-interactive",
    "oldData-diff-interactive"),
  ...
)

Arguments

Value

One of the output types specified in outputType. By default, all outputs are returned. If multiple output types are specified, a list of those are returned (named by output type).

Identification of the differences between datasets

To identify the differences between datasets, the following steps are followed:

1. removal of records identical between the old and new dataset (will be considered as 'Identical' later on)

2. records with a reference value present in the old dataset but not in the new dataset are considered 'Removal'

3. records with a reference value present in the new dataset but not in the old dataset are considered 'Addition'

4. records with reference value present both in the new and old dataset, after filtering of identical records and with difference in the changeable variables are considered 'Change'

Author(s)

Laure Cougnaud, Michela Pasetto

Examples

## Example 1
# In this case the referenceVar 'a' is the same
# the comparison highlights only as change in the variables 'c' and 'd' 
newData <- data.frame(
	"a" = c(1, 2, 3, 4),
	"b" = c(5, 6, 7, 8),
	"c" = rep(1, 4),
	"d" = rep(2, 4)
)
oldData <- data.frame(
	"a" = c(1, 2, 3, 4),
	"b" = c(3, 4, 7, 8),
	"c" = rep(2, 4),
	"d" = rep(1, 4)
)
compareTables(
	newData = newData,
	oldData = oldData,
	referenceVars = "a",
	changeableVars = c("c", "d")
)

## Example 2
# In this case the referenceVar 'a' changes in the last two rows
# the comparison highlights as change the second and third rows in the variables 'c' and 'd'
# whereas the last rows are additions/removals with respect to the reference 'a'
newData <- data.frame(
		"a" = c(7, 1, 2, 3, 4),
		"b" = c(2, 1, 6, 7, 8),
		"c" = rep(1, 5),
		"d" = rep(2, 5)
)
oldData <- data.frame(
		"a" = c(7, 1, 2, 5, 6),
		"b" = c(2, 3, 4, 7, 8),
		"c" = c(1, rep(2, 4)),
		"d" = c(2, rep(1, 4))
)
compareTables(
		newData = newData,
		oldData = oldData,
		referenceVars = "a",
		changeableVars = c("c", "d")
)

## Example 3
# In this case the referenceVar 'a' is the same
# also the variable 'c' is the same and it's the only changeable var evaluated
newData <- data.frame(
		"a" = c(1, 2, 3, 4),
		"b" = c(5, 6, 7, 8),
		"c" = rep(1, 4),
		"d" = rep(2, 4)
)
oldData <- data.frame(
		"a" = c(1, 2, 3, 4),
		"b" = c(3, 4, 7, 8),
		"c" = rep(1, 4),
		"d" = rep(1, 4)
)
compareTables(
		newData = newData,
		oldData = oldData,
		referenceVars = "a",
		changeableVars = "c"
)

## Not run: # due to time constraint in CRAN

## In case only a specific output should be returned:
newData <- data.frame(
	"a" = c(7, 1, 2, 3, 4),
	"b" = c(2, 1, 6, 7, 8),
	"c" = rep(1, 5),
	"d" = rep(2, 5)
)
oldData <- data.frame(
	"a" = c(7, 1, 2, 5, 6),
	"b" = c(2, 3, 4, 7, 8),
	"c" = c(1, rep(2, 4)),
	"d" = c(2, rep(1, 4))
)

# get only the differences between datasets:

# as a data.frame
compareTables(newData = newData, oldData = oldData, 
	referenceVars = "a", changeableVars = c("c", "d"), 
	outputType = "table-comparison")

# as an interactive DataTable
compareTables(newData = newData, oldData = oldData, 
	referenceVars = "a", changeableVars = c("c", "d"), 
	outputType = "table-comparison-interactive"
)
# only the new data
compareTables(
    newData = newData, oldData = oldData, 
	referenceVars = "a", changeableVars = c("c", "d"), 
	outputType = "newData-diff"
)
# only the new data in interactive mode
compareTables(
    newData = newData, oldData = oldData, 
    referenceVars = "a", changeableVars = c("c", "d"), 
    outputType = "newData-diff-interactive"
)
# only the new data in static and interactive mode
compareTables(
    newData = newData, oldData = oldData, 
    referenceVars = "a", changeableVars = c("c", "d"), 
    outputType = c("newData-diff", "newData-diff-interactive")
)
# only the old data
compareTables(newData = newData, oldData = oldData, 
	referenceVars = "a", changeableVars = c("c", "d"), 
	outputType = "oldData-diff"
)
# only the old data in interactive mode
compareTables(
    newData = newData, oldData = oldData, 
    referenceVars = "a", changeableVars = c("c", "d"), 
    outputType = "oldData-diff-interactive"
)
# only the old data in static and interactive mode
compareTables(
    newData = newData, oldData = oldData, 
    referenceVars = "a", changeableVars = c("c", "d"), 
    outputType = c("oldData-diff", "oldData-diff-interactive")
)


## End(Not run)
## no changeable vars

newData <- data.frame(
	"a" = c(7, 1, 2, 3, 4),
	"b" = c(2, 1, 6, 7, 8),
	"c" = rep(1, 5),
	"d" = rep(2, 5)
)
oldData <- data.frame(
	"a" = c(7, 1, 2, 5, 6),
	"b" = c(2, 3, 4, 7, 8),
	"c" = c(1, rep(2, 4)),
	"d" = c(2, rep(1, 4))
)

compareTables(newData = newData, oldData = oldData, 
	referenceVars = "a"
)

## duplicated records

# in case there are multiple records for the same reference variables,
# identical records are flagged as 'Identity' and reported in the table
# reporting differences; and the different record are flagged as 'Change', 'Addition' or 'Removal'
newData <- data.frame(
	"a" = c(7, 7),
	"b" = c(1, 2),
	"c" = c(1, 2),
	"d" = c(2, 3)
)
oldData <- data.frame(
	"a" = c(7, 7, 7),
	"b" = c(3, 4, 5),
	"c" = c(1, 3, 5),
	"d" = c(2, 4, 6)
)
compareTables(
	newData = newData, oldData = oldData, 
	referenceVars = "a", changeableVars = c("c", "d"), 
)

## with labels in the interactive format, see ? getClinDT

## Not run: # due to time constraint in CRAN
	
newData <- data.frame(
	"a" = c(7, 1, 2, 3, 4),
	"b" = c(2, 1, 6, 7, 8),
	"c" = rep(1, 5),
	"d" = rep(2, 5)
)
oldData <- data.frame(
	"a" = c(7, 1, 2, 5, 6),
	"b" = c(2, 3, 4, 7, 8),
	"c" = c(1, rep(2, 4)),
	"d" = c(2, rep(1, 4))
)
compareTables(
	newData = newData,
	oldData = oldData,
	referenceVars = "a",
	changeableVars = c("c", "d"),
	# parameters passed to datatable
	colnames = c(
		"My reference variable" = "a", 
		"Changeable variable c" = "c",
		"Changeable variable d" = "d"
	)
)


## End(Not run)

General parameters used for the comparison table functionality

Description

General parameters used for the comparison table functionality

Arguments

Value

The comparison of the two input tables.

Convert to data.table

Description

Convert a data frame into a data.table object.

Usage

convertToDatatable(data)

Arguments

Value

A data.table object.

Convert character vector to date/time object

Description

Convert character vector to date/time object

Usage

convertToDateTime(
  x,
  format = c("%Y-%m-%dT%H:%M", "%Y-%m-%d"),
  colName = NULL,
  verbose = TRUE
)

Arguments

Value

Vector of class POSIXct

Author(s)

Laure Cougnaud

Example of ADaM datasets from the CDISC original Pilot 01 study

Description

This contains a subset of the CDISC Pilot 01 study dataset for:

• a selected subset of subjects

• a selected subset of domains:

  • subject-level ('adsl')

  • adverse event ('adae')

  • laboratory chemistry data('adlbc')

  • vital signs ('advs')

  • concomitant medications ('adcm')

  • efficacy:

    • ADAS-COG Data ('adqsadas'), containing one of the primary endpoint: ADAS-Cog (11)
      (Alzheimer's Disease Assessment Scale - Cognitive Subscale

    • CIBIC+ questionnaire data ('adqscibc'), containing one of the primary endpoint: CIBIC+
      (Video-referenced Clinician's Interview-based Impression of Change)

    • NPI-X Item data ('adqsnpix'), containing the secondary endpoint: NPI-X
      (Mean Revised Neuropsychiatric Inventory)

  • pharmacokinetic parameters ('adpp')
    Please note that this dataset contains different sets of subjects than the other example datasets.

This dataset was created following the ADaM Version 2.0 standard. This dataset contains the 'Modified and augmented version of cdiscpilot01' dataset.

Format

List of data.frames containing the ADaM dataset for each selected domain.
Labels for the different variables across datasets is available via the labelVars attribute.

Author(s)

Laure Cougnaud

Source

Original (and entire) datasets are available in:
https://github.com/phuse-org/phuse-scripts/tree/master/data/adam/cdisc See in particular the define.xml file for further description of the datasets and variables name.

See Also

loadDataADaMSDTM

Example of SDTM datasets from the CDISC original Pilot 01 study

Description

This contains a subset of the CDISC original Pilot 01 study dataset for:

• a selected subset of subjects

• a selected subset of domains:

  • adverse event ('ae')

  • concomitant medications ('cm')

  • disposition ('ds')

  • demographics ('dm')

  • treatment exposure ('ex')

  • laboratory ('lb')

  • medical history ('mh')

  • questionnaire ('qs') - only:

    • ADAS-Cog (11) primary endpoint (QSTESTCD == 'ACTOT')

    • CIBIC+ primary endpoint (QSTESTCD == 'CIBIC')

    • NPI-X Item secondary endpoint (QSTESTCD == 'NPTOT')

  • demographics supplemental dataset ('suppdm')

  • subject visits ('sv')

  • vital signs ('vs')

This dataset was created following the SDTM Version 2 standard.

Format

List of data.frames containing the SDTM dataset for each selected domain.
Labels for the different variables across datasets is available via the labelVars attribute.

Author(s)

Laure Cougnaud

Source

Original (and entire) datasets are available in:
https://github.com/phuse-org/phuse-scripts/tree/master/data/sdtm/cdiscpilot01 See in particular the define.xml file for further description of the datasets and variables name.

See Also

loadDataADaMSDTM

Export the 'diff.data' object from compareDiff to a user-friendly format

Description

Export the 'diff.data' object from compareDiff to a user-friendly format

Usage

exportDiffData(
  diffData,
  newDataDiff,
  oldDataDiff,
  referenceVars = attr(diffData, "referenceVars"),
  changeableVars = attr(diffData, "changeableVars"),
  to = "DT",
  ...
)

Arguments

Value

Depending on the to parameter:

• 'DT': a datatable with the difference between datasets, with:

  • highlighting depending on the difference between datasets:

    • 'Addition' in green

    • 'Removal' in yellow

    • 'Change' in lightblue

    • 'Identical' are not highlighted

  • records only present in the old dataset are displayed in italic

Format a variable in a datatable as a barplot.

Description

Format a variable in a datatable as a barplot.

Usage

formatDTBarVar(
  tableDT,
  data,
  barVar = NULL,
  barColorThr = NULL,
  barRange = NULL,
  getCol = function(x) x
)

Arguments

Value

Updated tableDT

Author(s)

Laure Cougnaud

Format details comparison

Description

Format details comparison

Usage

formatDetailsComparison(
  diffData,
  referenceVars = attr(diffData, "referenceVars"),
  changeableVars = attr(diffData, "changeableVars")
)

Arguments

Value

diffData with extra columns: '[].diff' for the referenceVars and changeableVars columns, and attributes: 'colsDiff' as a named vector with mapping with input variables (names) and corresponding diff variables.

Author(s)

Laure Cougnaud

Concatenate and format text strings to a label

Description

This function concatenates and formats text strings to a label e.g to use for chunk and table/figures

Usage

formatLabel(...)

Arguments

Value

String with chunk label

Author(s)

Laure Cougnaud

Concatenate and format text strings to a chunk label

Description

Concatenate and format text strings to a chunk label

Usage

formatLabelChunk(...)

Arguments

Value

String with chunk label

Author(s)

Laure Cougnaud

Format a variable with long labels

Description

This function formats a variable with long labels by wrapping its elements into multiple lines.

Usage

formatLongLabel(x, width = 20)

Arguments

Value

Vector with formatted labels

Author(s)

Laure Cougnaud longLabel <- "This is a very long description of the variable in the dataset" cat(longLabel) cat(formatLongLabel(longLabel))

Concatenate and format text strings to a label of a table

Description

This function concatenates and formats text strings to a label of a table for bookdown package

Usage

formatTableLabel(...)

Arguments

Value

String with chunk label

Author(s)

Laure Cougnaud

Format parameter variable to be displayed in the labels of a plot

Description

The following workflow is used:

1. format the variable as a factor

2. wrap it across multiple lines if needed

3. sort (its levels) according to a grouping variable

Usage

formatVarForPlotLabel(
  data,
  paramVar = NULL,
  paramGroupVar = NULL,
  revert = FALSE,
  width = 20
)

Arguments

Value

Vector with re-formatted paramVar, NULL if empty

Author(s)

Laure Cougnaud library(ggplot2) data(dataADaMCDISCP01) dataAE <- dataADaMCDISCP01$ADAE

# by default, groups are sorted alphabetically in ggplot2 (from bottom to top for an histogram) ggplot(data = dataAE, aes(y = AEDECOD, fill = AEBODSYS)) + geom_histogram(stat="count")

# by default: labels are set to a new line if more than 20 characters: dataAE$AEDECOD <- formatVarForPlotLabel(data = dataAE, paramVar = "AEDECOD") levels(dataAE$AEDECOD) ggplot(data = dataAE, aes(y = AEDECOD, fill = AEBODSYS)) + geom_histogram(stat="count")

# revert order of the variable dataAE$AEDECOD <- formatVarForPlotLabel(data = dataAE, paramVar = "AEDECOD", revert = TRUE) levels(dataAE$AEDECOD) ggplot(data = dataAE, aes(y = AEDECOD, fill = AEBODSYS)) + geom_histogram(stat="count")

# group based on body system dataAE$AEDECOD <- formatVarForPlotLabel(data = dataAE, paramVar = "AEDECOD", paramGroupVar = "AEBODSYS") ggplot(data = dataAE, aes(y = AEDECOD, fill = AEBODSYS)) + geom_histogram(stat="count")

Create an interactive table to display clinical data

Description

This function converts a data.frame from R into a datatable object with sensitive defaults.
Extra functionalities are available to:

• have columns or cells of interest that are collapsible/expandable (see expandVar/expandIdx)

• group rows based on a variable (see rowGroupVar)

• display a variable as barplot (with specified range of threshold) (see barVar)

• hide variable(s) (see nonVisibleVar)

Usage

getClinDT(
  data,
  nonVisibleVar = NULL,
  nonVisible = NULL,
  percVar = NULL,
  barVar = NULL,
  barColorThr = NULL,
  barRange = NULL,
  filter = "top",
  searchBox = FALSE,
  pageLength,
  fixedColumns = NULL,
  columnsWidth = NULL,
  options = list(),
  expandVar = NULL,
  expandIdx = NULL,
  escape = TRUE,
  rowGroup = NULL,
  rowGroupVar = NULL,
  vAlign = "top",
  callback = NULL,
  buttons = getClinDTButtons(),
  scrollX = TRUE,
  file = NULL,
  verbose = TRUE,
  ...
)

Arguments

Value

A datatable object.

Author(s)

Laure Cougnaud

Examples

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

# example of simple adverse event table
dataAE <- dataADaMCDISCP01$ADAE
subjectsSafety <- subset(dataADaMCDISCP01$ADSL, SAFFL == "Y")$USUBJID

# compute counts of subjects presenting each AE
tableAE <- stats::aggregate(
	USUBJID ~ AESOC:AEDECOD,
	data = dataAE, 
	FUN = function(usubjid)	length(unique(usubjid))
)
colnames(tableAE)[colnames(tableAE) == "USUBJID"] <- "N"
# and percentages
tableAE$perc <- round(tableAE$N/length(subjectsSafety)*100, 3)
# sort records in decreasing percentage
tableAE <- tableAE[order(tableAE$perc, decreasing = TRUE), ]

# extract new variables labels
tableAELabels <- getLabelVar(
	var = colnames(tableAE),
	labelVars = labelVars,
	label = c(N = '# subjects', perc = "% subjects")
)
# 'colnames' for DT should be specified as c('new name' = 'old name', ...)
tableAELabelsDT <- setNames(names(tableAELabels), tableAELabels)

## create table with bar

# default:
getClinDT(
	data = tableAE,
	barVar = "perc",
	colnames = tableAELabelsDT
)

# specify range for the bar
getClinDT(
	data = tableAE,
	filter = "none",
	barVar = "perc",
	barRange = c(0, 100),
	colnames = tableAELabelsDT
)

# change color according to threshold
getClinDT(
	data = tableAE,
	filter = "none",
	barVar = "perc",
	barColorThr = seq(from = 0, to = 100, by = 25),
	colnames = tableAELabelsDT
)

## group per system organ class (and decreasing N):
tableAESOC <- aggregate(N ~ AESOC, data = tableAE, FUN = sum)
tableAE$AESOC <- factor(tableAE$AESOC,
	levels = tableAESOC[order(tableAESOC$N, decreasing = FALSE), "AESOC"]
)
tableAE <- tableAE[order(tableAE$AESOC, tableAE$perc, decreasing = TRUE), ]
	
getClinDT(
	data = tableAE,
	filter = "none",
	barVar = "perc",
	barRange = c(0, 100),
	colnames = tableAELabelsDT,
	rowGroupVar = "AESOC",
	pageLength = Inf
)

# expand the subject ID column, will
# be accessible when clicking on the '+' button
# Format URL correctly with: 'escape',
# please note that indexing starts at 0!
getClinDT(
	data = tableAE,
	barVar = "perc",
	colnames = tableAELabelsDT,
	expandVar = "USUBJID", 
	escape = grep("USUBJID", colnames(tableAE))-1
)

# fix size for columns
getClinDT(
	data = tableAE,
	colnames = tableAELabelsDT,
	fixedColumns = list(leftColumns = 1),
	columnsWidth = c(0.1, 0.7, 0.1, 0.1),
	width = "350px" # change dimension table
)

## Not run: # due to time constraint in CRAN

# change default buttons
getClinDT(
	data = tableAE,
	colnames = tableAELabelsDT,
	# remove general filter
	filter = "none",
	# custom set of buttons
	buttons = getClinDTButtons(type = c("csv", "excel", "pdf"))
)
# add button to select columns
getClinDT(
	data = tableAE,
	colnames = tableAELabelsDT,
	# custom set of buttons
	buttons = getClinDTButtons(typeExtra = "colvis")
)
# export pdf in landscape format
buttons <- getClinDTButtons(
	opts = list(pdf = list(orientation = "landscape"))
)
getClinDT(
	data = tableAE,
	colnames = tableAELabelsDT,
	# custom set of buttons
	buttons = buttons
)

# hide the first column:
getClinDT(
	data = tableAE,
	nonVisibleVar = "AESOC"
)

# with specific caption
library(htmltools)
caption <- tags$caption(
	"Number of subjects with adverse events grouped by system organ class.",
	br(), 
	paste(
		"Percentages are based on the total number of patients having",
		"received a first study treatment."
	)
)
getClinDT(
	data = tableAE,
	filter = "none",
	barVar = "perc",
	barRange = c(0, 100),
	pageLength = Inf,
	colnames = tableAELabelsDT,
	rowGroupVar = "AESOC",
	caption = caption
)


## End(Not run)

Get a default set of buttons to be included in the interactive table for clinical data.

Description

Get a default set of buttons to be included in the interactive table for clinical data.

Usage

getClinDTButtons(
  type = c("copy", "csv", "excel", "pdf", "print"),
  typeExtra = NULL,
  opts = NULL
)

Arguments

Details

The 'colvis' button doesn't display the non visible columns.
These are defined internally with:

options = list(
  columnDefs = list(
    list(targets = [X], className = 'noVis')
  )
)

with [X] the index of the column(s) in Javascript notation (starting from 0)

Value

Nested list with default buttons to be passed on to 'buttons' option in the getClinDT.

Author(s)

Laure Cougnaud

Get a color palette for clinical visualizations.

Description

Get a color palette of specified length, either from a vector of names for the palette, or from a specified length.

Usage

getColorPalette(n = NULL, x = NULL, includeNA = FALSE, palette = clinColors)

Arguments

Value

Vector of colors, named with the elements in x if x is specified.

Author(s)

Laure Cougnaud and Michela Pasetto

Examples

# extract longest palette available
getColorPalette(n = 11)
# extract palette for a vector
getColorPalette(x = paste('treatment', 1:4))
# possibility to include missing values:
getColorPalette(x = c(NA_character_, "group1"), includeNA = FALSE)
getColorPalette(x = c(NA_character_, "group1"), includeNA = TRUE)
# change default settings
getColorPalette(n = 3, palette = c("red", "green", "grey"))

Get label for a parameter code

Description

This function gets the labele for a parameter code extracted from the 'PARAM' column.

Usage

getLabelParamcd(paramcd, data, paramcdVar = "PARAMCD", paramVar = "PARAM")

Arguments

Value

Named character vector with label for parameter code or paramcd if label is missing.

Author(s)

Laure Cougnaud

Examples

# for ADaM
data(dataADaMCDISCP01)
getLabelParamcd(paramcd = "CHOL", data = dataADaMCDISCP01$ADLBC)
# for SDTM
data(dataSDTMCDISCP01)
getLabelParamcd(
  paramcd = "ALB", 
  data = dataSDTMCDISCP01$LB, 
  paramcdVar = "LBTESTCD", 
  paramVar = "LBTEST"
)

Get label for a variable of the dataset

Description

The label is extracted either (in this order):

1. if label is specified: from this label based on names, or directly from this label if label and var are of length 1 (if available)

2. if labelVars is specified: from the specified vector of labels, based on names (if available)

3. if data is specified: from the 'label' attribute of the corresponding column in data (if available)

If the label is not available, the input variable is returned.

Usage

getLabelVar(var, data = NULL, labelVars = NULL, label = NULL)

Arguments

Value

Named character vector with label, var is no label is available

Author(s)

Laure Cougnaud

Examples

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

# (upon reading the data with haven: attributes should directly available in each column)
getLabelVar(data = dataADaMCDISCP01, var = "AEREL")
		
# but if the data as data.frame is subsetted, label is lost 	
# so better to use 'labelVars':
getLabelVar(var = "AEREL", labelVars = labelVars)

Get label of the variables in SAS dataset(s)

Description

Get label of the variables in SAS dataset(s)

Usage

getLabelVars(data, labelVars = NULL)

Arguments

Value

Named vector with variable labels.

Author(s)

Laure Cougnaud

Examples

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

# extract label for all variables from specified datasets:
getLabelVars(data = dataADaMCDISCP01[c("ADLBC", "ADVS")], labelVars = labelVars)

# extracted from specified labelVars, e.g. to specify custom label for specific variable(s)
labelVarsCustom <- getLabelVars(
  data = dataADaMCDISCP01, 
  labelVars = c(USUBJID = "Subject identifier for my study")
)
labelVarsCustom["USUBJID"]

Get a linetype palette for clinical visualizations.

Description

Get a linetype palette of specified length, either from a vector of names for the palette, or from a specified length.

Usage

getLinetypePalette(
  n = NULL,
  x = NULL,
  includeNA = FALSE,
  palette = clinLinetypes
)

Arguments

Details

Note that 7 unique symbols are available at maximum (replicated if necessary).

Value

Vector with linetypes, named with the elements in x if x is specified.

Author(s)

Laure Cougnaud and Michela Pasetto

Examples

# extract longest linetype palette available
getLinetypePalette(n = 6)
# extract palette for a vector
getLinetypePalette(x = paste('treatment', 1:4))
# include missing
getLinetypePalette(x = c(NA_character_, "group1"), includeNA = TRUE)
getLinetypePalette(x = c(NA_character_, "group1"), includeNA = FALSE)
# set custom linetypes
lty <- getColorPalette(n = 3, palette = c("twodash", "dashed"))

Get standard palette for typical CDISC variables.

Description

The extraction of the palette elements is case-insensitive.

Usage

getPaletteCDISC(x, var, type, palette = NULL)

Arguments

Details

The order of the palette depends on the type of the input variable (x):

• if a factor is specified, the palette is ordered based on its levels

• if a character vector is specified, the elements from the internal standard palette are used first, the remaining elements are then sorted alphabetically.

Value

Named vector with palette.

Author(s)

Laure Cougnaud

Examples

## palette for reference range indicator variables

xRIND <- c("LOW", "HIGH", "NORMAL", "NORMAL", "NORMAL", "ABNORMAL")

# get standard palette
getPaletteCDISC(x = xRIND, var = "NRIND", type = "shape")
getPaletteCDISC(x = xRIND, var = "NRIND", type = "color")

# in case extra categories are specified:
xRIND <- c(xRIND, "High Panic")
# the symbols are set to numeric symbols
getPaletteCDISC(xRIND, var = "NRIND", type = "shape")
# use shapePalette to specify symbols for extra categories
getPaletteCDISC(xRIND, var = "NRIND", type = "shape", palette = c("High Panic" = "\u2666"))

# palette is case-insensitive
xRIND <- c("Low", "High", "Normal", "Normal", "Normal")
getPaletteCDISC(xRIND, var = "NRIND", type = "shape")

Get additions/removals

Description

Get only additions and removals from two data sets (data.table objects). The additions/removals are extracted as x vs y. This function assumes that the objects x and y don't share identical rows.

Usage

getSetDiff(x, y, referenceVars)

Arguments

Value

A data.table object with the additions/removals with respect of the comparison between x vs y.

Get a shape palette for clinical visualizations.

Description

Get a shape palette of specified length, either from a vector of names for the palette, or from a specified length.

Usage

getShapePalette(
  n = NULL,
  x = NULL,
  includeNA = FALSE,
  asText = FALSE,
  palette = if (asText) {
     clinShapesText
 } else {
     clinShapes
 }
)

Arguments

Details

Note that 19 unique symbols are available at maximum (replicated if necessary).

Value

Vector of shapes, named with the elements in x if x is specified.

Author(s)

Laure Cougnaud and Michela Pasetto

Examples

#' extract longest shape palette available
getShapePalette(n = 19)
# extract palette for a vector
getShapePalette(x = paste('treatment', 1:4))
# include missing
getShapePalette(x = c(NA_character_, "group1"), includeNA = TRUE)
getShapePalette(x = c(NA_character_, "group1"), includeNA = FALSE)
# change default settings
getShapePalette(x = paste('treatment', 1:3), palette = c("circle", "triangle"))
# get symbols as 'text' (e.g. to be combined with Unicode in ggplot2)
getShapePalette(x = paste('treatment', 1:4), asText = TRUE)

Include a list of objects in a knitr document

Description

Each object is included (internally) in a separated chunk, so different chunk options can be set for each object.

Usage

knitPrintListObjects(
  xList,
  generalLabel = "objectsList",
  labels = paste0(generalLabel, seq_along(xList)),
  titles = NULL,
  titleLevel = 2,
  printObject = FALSE,
  ...
)

Arguments

Details

This function should be called within a chunk with the following option: results = 'asis'.

Value

No returned value, a text is printed with chunk content

Author(s)

Laure Cougnaud

Examples

## Not run: 
	
# Note: the following code should be included 
# within a chunk of a knitr (e.g. RMarkdown) document
# to include a list of objects in the Rmarkdown output

# list of flextable objects
library(flextable)
listTables <- list(flextable(iris), flextable(cars))
knitPrintListObjects(
	xList = listTables,
	titles = c("Iris dataset", "Cars dataset")
)


## End(Not run)

Include a list of plots in a knitr document

Description

Each plot is included (internally) in a separated chunk, so different chunk options can be set for each plot.
For example, plots can be created with different figure height or width (see examples).

Usage

knitPrintListPlots(
  plotsList,
  generalLabel = "plotsList",
  type = c("ggplot2", "plotly"),
  ...
)

Arguments

Details

This function should be called within a chunk with the following option: results = 'asis'.
Note that a (one-level) list of plotly plots can also be included directly via htmltools::tagList(listPlots), but without the possibility to have different chunk option for each plot.

Value

No returned value, a text is printed with chunk content

Author(s)

Laure Cougnaud

Examples

## Not run: 

# Note: the following code should be included 
# within a chunk of a knitr (e.g. RMarkdown) document
# to include a list of figures in the Rmarkdown output
data(iris)

## Static plots

library(ggplot2)
plotsListStatic <- list(
	point = ggplot(data = cars, aes(x = speed, y = dist)) + geom_point(),
	line = ggplot(data = cars, aes(x = speed, y = dist)) + geom_line()
)
# with general label (used to name exported figure)
knitPrintListPlots(
	plotsList = plotsListStatic, 
	generalLabel = "scatter-cars"
)
# with label for each plot (used to name exported figure)
knitPrintListPlots(
	plotsList = plotsListStatic, 
	labels = names(plotsListStatic)
)
# with section header (header of level 1 in Markdown)
knitPrintListPlots(
	plotsList = plotsListStatic, 
	titles = names(plotsListStatic), 
	titleLevel = 3
)
# with caption for each figure
knitPrintListPlots(
	plotsList = plotsListStatic, 
	fig.cap = names(plotsListStatic)
)
	
# specify dimension for each figure
knitPrintListPlots(
	plotsList = plotsListStatic, 
	# first plot has width of 3, second of 6
	fig.width = c(3, 6), 
	# both plots have a height of 6
	fig.height = 6
)

## Interactive plots

library(plotly)
plotsListInteractive <- list(
	point = plot_ly(data = cars, x = ~speed, y = ~dist, type = "scatter", mode = "marker"),
	line = plot_ly(data = cars, x = ~speed, y = ~dist, type = "scatter", mode = "line")
)

# with titles
knitPrintListPlots(
	plotsList = plotsListInteractive, 
	type = "plotly", 
	titles = names(plotsListInteractive), 
	titleLevel = 3
)


## End(Not run)

Load data from ADaM/SDTM file(s).

Description

Load data set in SAS format ('sas7bdat' or 'xpt') into R data.frames, from files or raw vector data.

Usage

loadDataADaMSDTM(
  files,
  data,
  convertToDate = FALSE,
  dateVars = "DTC$",
  verbose = TRUE,
  encoding = "UTF-8",
  ...
)

Arguments

Details

While creating the R data.frames, if date/time variables are present, those are converted into to R date/time class (see convertToDateTime) function.

The labels of the ADaM/SDTM data sets are attached as attributes of the R data.frame.

Value

List of data.frame with data of each ADAM file (if not empty), with special attributes:

• 'labelVars': named vector with label of the variables

• 'label': named vector with label of the datasets

Each data.frame contains an additional column called 'dataset' specifying the name of the files it was read from.

Author(s)

Laure Cougnaud

Examples

## Not run: 
dataFromSAS7bdat <- loadDataADaMSDTM(files = "ae.sas7bdat")
attr(dataFromSAS7bdat, "labelVars") # column labels
dataFromXpt <- loadDataADaMSDTM(files = c("ae.xpt", "dm.xpt"))
attr(dataFromXpt, "labelVars") # column labels

## End(Not run)

Merge the 'diff.data' object from compareDiff with the original newData or oldData.

Description

The newData/oldData are merged with diffData based on the columns of diffData excepted 'Comparison type' and 'Version'.

Usage

mergeDiffWithData(diffData, newData, oldData)

Arguments

Value

The newData or oldData (as a data frame object) with the extra column 'Comparison type' specifying the type of change, either:

• 'Change': record present in both dataset based on the reference variables, but with changes in the changeable variables

• 'Addition': records present in new but not in old data

• 'Removal': records present in old but not in new data

• 'Identical': records identical in the old and new datasets (on both the reference and changeable variables)

Custom merge of difference data with input data

Description

Custom merge (left join) of difference data with some input data

Usage

mergeInputDiff(diffData, inputData, typeData, colsBy)

Arguments

Value

A data.table object. The inputData is joined with the columns Comparison type and Version from the diffData argument.

Function for reordering columns

Description

Function for reordering columns

Usage

reorderColumns(data, vars)

Arguments

Value

The same data.frame specified in data, with ordered columns.

Examples

someData <- data.frame(
	"Col1" = c(1, 2),
	"Col2" = c(2, 3),
	"Col3" = c(3, 4)
	)
reorderColumns(
	data = someData,
	vars = c("Col3" = 1)
	)

Round a number with 'rounding up' strategy for rounding off a 5

Description

This function rounds a number for a specified number of digits. It rounds off to the highest number for a 5. The default R round function rounds to the 'even digit' in case of rounding off a 5 (see 'Details' section in ? round). This function instead rounds up to the nearest number for a 5. It mimics a similar rounding strategy used in SAS. See examples for the difference between round and 'roundHalfUp' below.

Usage

roundHalfUp(x, digits = 0)

Arguments

Value

Rounded numeric vector.

Author(s)

stackoverflow question 6461209

Examples

# numbers are rounded to the closest even number in case of .5 
# with the round 'base' function
round(0.45, 1)
# 'roundHalfUp' always round to the next highest number in case of .5
roundHalfUp(0.45, 1)
# rounding is the same for uneven number:
round(0.55, 1)
roundHalfUp(0.55)
# other examples
round(1.456e-2, digits = 3)
round(1.456e-2, digits = 2)
round(1.456e-2, digits = 1)

Round a number with 'round-up' strategy for rounding off a 5 in text format

Description

This function rounds numbers with a 'round-up' strategy for rounding off a 5. The function rounds for a specified number of digits and format number to a: 'xxx.xxx' text.

Usage

roundHalfUpTextFormat(x, digits = 0)

Arguments

Details

The following workflow is used:

1. numbers are rounded with the roundHalfUp function, see the ? roundHalfUp for more details on the rounding strategy

2. round numbers are formatted to character in the format: 'xxx.xxx' with pads leading zeros

Value

A character vector with the rounded number. NA values are returned as 'NA' as string.

Author(s)

Laure Cougnaud and Michela Pasetto

See Also

roundHalfUp for the rounding customization.

Examples

# number of digits higher than number of decimal
roundHalfUpTextFormat(x = c(0.345, 0.567, -0.98), digits = 2)
# number of digits lower than number of decimal
roundHalfUpTextFormat(x = c(0.345, 0.567, -0.98), digits = 0)
# by default, 'digits' is 0!
roundHalfUpTextFormat(x = c(0.345, 0.567, -0.98))
# padding zeros
roundHalfUpTextFormat(1.23, 10)

Shape palette for a standard CDISC Normal/Reference Range Indicator.

Description

These symbols should be supported in Windows and Linux.

Usage

shapePaletteNRIND

Format

A named character vector with shape symbol for typical Normal Reference Range Indicator variable:

• "LOW": filled down-pointing arrow (25)

• "NORMAL": filled circle (21)

• "HIGH": filled up-pointing arrow (24)

• "ABNORMAL": diamond (18)

• "UNKNOWN" or 'NA': cross (3)

• "NA": cross (3)

Capitalize the first letter of a word/sentence.

Description

This implementation is inspired from the help of the toupper function.

Usage

simpleCap(x, onlyFirst = TRUE, rev = FALSE)

Arguments

Value

Character vector with first letter capitalized

Author(s)

author of the 'toupper' function?

See Also

link[tools]{toTitleCase}

Examples

# capitalize only the first word of the sentence
simpleCap(x = "this is the caption of my figure.")
# capitalize all words
simpleCap(x = "this is the caption of my figure.", onlyFirst = FALSE)
# opposite: set the first letter of the first word to lower case
simpleCap(x = "This is the caption of my figure.", rev = TRUE)