1 Introduction

This package clinUtils contains functionalities to facilitate the analysis of clinical datasets in R.

library(clinUtils)

# packages required for the examples in the vignette
library(ggplot2)
library(pander)
library(htmltools)
library(plyr)

Please note that the interactive outputs (table/visualization) in this vignette are only included if Pandoc is available (pre-requisite for R Markdown).

2 Data pre-processing

2.1 Data import

The haven R package enables to load SAS datasets in sas7bdat and xpt formats.

The function loadDataADaMSDTM is a wrapper to import multiple SAS datasets in sas7bdat or xpt format at once. It returns a list of dataset (once by domain). The variable labels are combined across datasets and available in a dedicated attribute.

pathExampleDatasets <- list.files(
    path = system.file("extdata", "cdiscpilot01", "SDTM", package = "clinUtils"), 
    pattern = "*.xpt", 
    full.names = TRUE
)

data <- loadDataADaMSDTM(files = pathExampleDatasets)
# A list is returned, each separated file is accessible via data$[fileName]
pander(head(data$DM, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	SUBJID	RFSTDTC	RFENDTC
CDISCPILOT01	DM	01-701-1148	1148	2013-08-23	2014-02-20
CDISCPILOT01	DM	01-701-1192	1192	2012-07-22	2013-01-20

Table continues below
RFXSTDTC	RFXENDTC	RFICDTC	RFPENDTC	DTHDTC	DTHFL	SITEID
2013-08-23	2014-02-20		2014-02-20T15:07			701
2012-07-22	2013-01-20		2013-01-20T15:47			701

Table continues below
AGE	AGEU	SEX	RACE	ETHNIC	ARMCD
57	YEARS	M	WHITE	NOT HISPANIC OR LATINO	Xan_Hi
80	YEARS	F	WHITE	NOT HISPANIC OR LATINO	Xan_Lo

Table continues below
ARM	ACTARMCD	ACTARM	COUNTRY	DMDTC
Xanomeline High Dose	Xan_Hi	Xanomeline High Dose	USA	2013-08-14
Xanomeline Low Dose	Xan_Lo	Xanomeline Low Dose	USA	2012-07-08

DMDY	DATASET
-9	DM
-14	DM

pander(head(data$LB, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	LBSEQ	LBTESTCD	LBTEST	LBCAT
CDISCPILOT01	LB	01-701-1148	1	ALB	Albumin	CHEMISTRY
CDISCPILOT01	LB	01-701-1148	38	ALB	Albumin	CHEMISTRY

Table continues below
LBORRES	LBORRESU	LBORNRLO	LBORNRHI	LBSTRESC	LBSTRESN	LBSTRESU
4.1	g/dL	3.3	4.9	41	41	g/L
3.9	g/dL	3.3	4.9	39	39	g/L

Table continues below
LBSTNRLO	LBSTNRHI	LBNRIND	LBBLFL	VISITNUM	VISIT	VISITDY
33	49	NORMAL	Y	1	SCREENING 1	-7
33	49	NORMAL		4	WEEK 2	14

LBDTC	LBDY	DATASET
2013-08-14T13:04	-9	LB
2013-09-05T15:43	14	LB

pander(head(data$AE, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	AESEQ	AESPID
CDISCPILOT01	AE	01-701-1148	10	E27
CDISCPILOT01	AE	01-701-1148	1	E20

Table continues below
AETERM	AELLT	AELLTCD
ACTINIC KERATOSIS	ACTINIC KERATOSIS	NA
APPLICATION SITE ERYTHEMA	APPLICATION SITE REDNESS	NA

Table continues below
AEDECOD	AEPTCD	AEHLT	AEHLTCD	AEHLGT	AEHLGTCD
ACTINIC KERATOSIS	NA	HLT_0609	NA	HLGT_0490	NA
APPLICATION SITE ERYTHEMA	NA	HLT_0617	NA	HLGT_0152	NA

Table continues below
AEBODSYS	AEBDSYCD	AESOC
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	NA	SKIN AND SUBCUTANEOUS TISSUE DISORDERS
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	NA	GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

Table continues below
AESOCCD	AESEV	AESER	AEACN	AEREL	AEOUT
NA	MILD	N		NONE	NOT RECOVERED/NOT RESOLVED
NA	MILD	N		PROBABLE	NOT RECOVERED/NOT RESOLVED

Table continues below
AESCAN	AESCONG	AESDISAB	AESDTH	AESHOSP	AESLIFE	AESOD	AEDTC
N	N	N	N	N	N	N	2014-02-20
N	N	N	N	N	N	N	2013-09-05

AESTDTC	AEENDTC	AESTDY	AEENDY	DATASET
2014-02-12		174	NA	AE
2013-08-25		3	NA	AE

# Access labels for all variables
labelVars <- attr(data, "labelVars")
head(labelVars)

##                               STUDYID                                DOMAIN 
##                    "Study Identifier"                 "Domain Abbreviation" 
##                               USUBJID                                 AESEQ 
##           "Unique Subject Identifier"                     "Sequence Number" 
##                                AESPID                                AETERM 
##          "Sponsor-Defined Identifier" "Reported Term for the Adverse Event"

# Access label for a particular variable: 
labelVars["USUBJID"]

##                     USUBJID 
## "Unique Subject Identifier"

2.2 Example datasets

To demonstrate the functionalities of this package, a subset of the datasets from the CDISC Pilot 01 study is available in the package as dataset.

2.2.1 ADaM

# load data
data(dataADaMCDISCP01)
dataADaM <- dataADaMCDISCP01
names(dataADaM)

## [1] "ADAE"     "ADCM"     "ADLBC"    "ADPP"     "ADQSADAS" "ADQSCIBC" "ADSL"    
## [8] "ADVS"

pander(head(dataADaM$ADSL, 2))

Table continues below
STUDYID	USUBJID	SUBJID	SITEID	SITEGR1	ARM
CDISCPILOT01	01-701-1148	1148	701	701	Xanomeline High Dose
CDISCPILOT01	01-701-1192	1192	701	701	Xanomeline Low Dose

Table continues below
TRT01P	TRT01PN	TRT01A	TRT01AN	TRTSDT
Xanomeline High Dose	81	Xanomeline High Dose	81	2013-08-23
Xanomeline Low Dose	54	Xanomeline Low Dose	54	2012-07-22

Table continues below
TRTEDT	TRTDUR	AVGDD	CUMDOSE	AGE	AGEGR1	AGEGR1N	AGEU	RACE
2014-02-20	182	77.1	14040	57	<65	1	YEARS	WHITE
2013-01-20	183	54	9882	80	65-80	2	YEARS	WHITE

Table continues below
RACEN	SEX	ETHNIC	SAFFL	ITTFL	EFFFL	COMP8FL
1	M	NOT HISPANIC OR LATINO	Y	Y	Y	Y
1	F	NOT HISPANIC OR LATINO	Y	Y	Y	Y

Table continues below
COMP16FL	COMP24FL	DISCONFL	DSRAEFL	DTHFL	BMIBL	BMIBLGR1	HEIGHTBL
Y	Y				28.3	25-<30	175.3
Y	Y				27.8	25-<30	151.1

Table continues below
WEIGHTBL	EDUCLVL	DISONSDT	DURDIS	DURDSGR1	VISIT1DT	RFSTDTC
87.1	15	2010-12-12	32.1	>=12	2013-08-14	2013-08-23
63.5	12	2009-03-15	39.8	>=12	2012-07-08	2012-07-22

RFENDTC	VISNUMEN	RFENDT	DCDECOD	DCREASCD	MMSETOT	DATASET
2014-02-20	12	2014-02-20	COMPLETED	Completed	21	ADSL
2013-01-20	12	2013-01-20	COMPLETED	Completed	23	ADSL

pander(head(dataADaM$ADLBC, 2))

Table continues below
STUDYID	SUBJID	USUBJID	TRTP	TRTPN
CDISCPILOT01	1148	01-701-1148	Xanomeline High Dose	81
CDISCPILOT01	1148	01-701-1148	Xanomeline High Dose	81

Table continues below
TRTA	TRTAN	TRTSDT	TRTEDT	AGE	AGEGR1
Xanomeline High Dose	81	2013-08-23	2014-02-20	57	<65
Xanomeline High Dose	81	2013-08-23	2014-02-20	57	<65

Table continues below
AGEGR1N	RACE	RACEN	SEX	COMP24FL	DSRAEFL	SAFFL	AVISIT
1	WHITE	1	M	Y		Y	Baseline
1	WHITE	1	M	Y		Y	Baseline

Table continues below
AVISITN	ADY	ADT	VISIT	VISITNUM	PARAM
0	-9	2013-08-14	SCREENING 1	1	Sodium (mmol/L)
0	-9	2013-08-14	SCREENING 1	1	Potassium (mmol/L)

Table continues below
PARAMCD	PARAMN	PARCAT1	AVAL	BASE	CHG	A1LO	A1HI	R2A1LO	R2A1HI
SODIUM	18	CHEM	139	139	NA	132	147	1.053	0.9456
K	19	CHEM	4	4	NA	3.4	5.4	1.176	0.7407

Table continues below
BR2A1LO	BR2A1HI	ANL01FL	ALBTRVAL	ANRIND	BNRIND	ABLFL	AENTMTFL
1.053	0.9456		81.5	N	N	Y
1.176	0.7407		4.1	N	N	Y

LBSEQ	LBNRIND	LBSTRESN	DATASET
26	NORMAL	139	ADLBC
19	NORMAL	4	ADLBC

pander(head(dataADaM$ADAE, 2))

Table continues below
STUDYID	SITEID	USUBJID	TRTA	TRTAN	AGE
CDISCPILOT01	701	01-701-1148	Xanomeline High Dose	81	57
CDISCPILOT01	701	01-701-1148	Xanomeline High Dose	81	57

Table continues below
AGEGR1	AGEGR1N	RACE	RACEN	SEX	SAFFL	TRTSDT	TRTEDT
<65	1	WHITE	1	M	Y	2013-08-23	2014-02-20
<65	1	WHITE	1	M	Y	2013-08-23	2014-02-20

Table continues below
ASTDT	ASTDTF	ASTDY	AENDT	AENDY	ADURN	ADURU
2013-08-25		3	NA	NA	NA
2013-08-25		3	NA	NA	NA

Table continues below
AETERM	AELLT	AELLTCD
APPLICATION SITE ERYTHEMA	APPLICATION SITE REDNESS	NA
APPLICATION SITE PRURITUS	APPLICATION SITE ITCHING	NA

Table continues below
AEDECOD	AEPTCD	AEHLT	AEHLTCD	AEHLGT	AEHLGTCD
APPLICATION SITE ERYTHEMA	NA	HLT_0617	NA	HLGT_0152	NA
APPLICATION SITE PRURITUS	NA	HLT_0317	NA	HLGT_0338	NA

Table continues below
AEBODSYS	AESOC	AESOCCD
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	NA
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	NA

Table continues below
AESEV	AESER	AESCAN	AESCONG	AESDISAB	AESDTH	AESHOSP	AESLIFE
MILD	N	N	N	N	N	N	N
MILD	N	N	N	N	N	N	N

Table continues below
AESOD	AEREL	AEACN	AEOUT	AESEQ	TRTEMFL
N	PROBABLE		NOT RECOVERED/NOT RESOLVED	1	Y
N	PROBABLE		NOT RECOVERED/NOT RESOLVED	2	Y

Table continues below
AOCCFL	AOCCSFL	AOCCPFL	AOCC02FL	AOCC03FL	AOCC04FL
Y	Y	Y
		Y

CQ01NAM	AOCC01FL	DATASET
DERMATOLOGIC EVENTS	Y	ADAE
DERMATOLOGIC EVENTS		ADAE

# and variable labels
labelVarsADaM <- attr(dataADaM, "labelVars")
head(labelVarsADaM)

##                     STUDYID                      SITEID 
##          "Study Identifier"     "Study Site Identifier" 
##                     USUBJID                        TRTA 
## "Unique Subject Identifier"          "Actual Treatment" 
##                       TRTAN                         AGE 
##      "Actual Treatment (N)"                       "Age"

2.2.2 SDTM

# load data
data(dataSDTMCDISCP01)
dataSDTM <- dataSDTMCDISCP01
names(dataSDTM)

## [1] "AE" "CM" "DM" "EX" "LB" "MH" "SV" "VS"

pander(head(dataSDTM$DM, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	SUBJID	RFSTDTC	RFENDTC
CDISCPILOT01	DM	01-701-1148	1148	2013-08-23	2014-02-20
CDISCPILOT01	DM	01-701-1192	1192	2012-07-22	2013-01-20

Table continues below
RFXSTDTC	RFXENDTC	RFICDTC	RFPENDTC	DTHDTC	DTHFL	SITEID
2013-08-23	2014-02-20		2014-02-20T15:07			701
2012-07-22	2013-01-20		2013-01-20T15:47			701

Table continues below
AGE	AGEU	SEX	RACE	ETHNIC	ARMCD
57	YEARS	M	WHITE	NOT HISPANIC OR LATINO	Xan_Hi
80	YEARS	F	WHITE	NOT HISPANIC OR LATINO	Xan_Lo

Table continues below
ARM	ACTARMCD	ACTARM	COUNTRY	DMDTC
Xanomeline High Dose	Xan_Hi	Xanomeline High Dose	USA	2013-08-14
Xanomeline Low Dose	Xan_Lo	Xanomeline Low Dose	USA	2012-07-08

DMDY	DATASET
-9	DM
-14	DM

pander(head(dataSDTM$LB, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	LBSEQ	LBTESTCD	LBTEST	LBCAT
CDISCPILOT01	LB	01-701-1148	1	ALB	Albumin	CHEMISTRY
CDISCPILOT01	LB	01-701-1148	38	ALB	Albumin	CHEMISTRY

Table continues below
LBORRES	LBORRESU	LBORNRLO	LBORNRHI	LBSTRESC	LBSTRESN	LBSTRESU
4.1	g/dL	3.3	4.9	41	41	g/L
3.9	g/dL	3.3	4.9	39	39	g/L

Table continues below
LBSTNRLO	LBSTNRHI	LBNRIND	LBBLFL	VISITNUM	VISIT	VISITDY
33	49	NORMAL	Y	1	SCREENING 1	-7
33	49	NORMAL		4	WEEK 2	14

LBDTC	LBDY	DATASET
2013-08-14T13:04	-9	LB
2013-09-05T15:43	14	LB

pander(head(dataSDTM$AE, 2))

Table continues below
STUDYID	DOMAIN	USUBJID	AESEQ	AESPID
CDISCPILOT01	AE	01-701-1148	10	E27
CDISCPILOT01	AE	01-701-1148	1	E20

Table continues below
AETERM	AELLT	AELLTCD
ACTINIC KERATOSIS	ACTINIC KERATOSIS	NA
APPLICATION SITE ERYTHEMA	APPLICATION SITE REDNESS	NA

Table continues below
AEDECOD	AEPTCD	AEHLT	AEHLTCD	AEHLGT	AEHLGTCD
ACTINIC KERATOSIS	NA	HLT_0609	NA	HLGT_0490	NA
APPLICATION SITE ERYTHEMA	NA	HLT_0617	NA	HLGT_0152	NA

Table continues below
AEBODSYS	AEBDSYCD	AESOC
SKIN AND SUBCUTANEOUS TISSUE DISORDERS	NA	SKIN AND SUBCUTANEOUS TISSUE DISORDERS
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS	NA	GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

Table continues below
AESOCCD	AESEV	AESER	AEACN	AEREL	AEOUT
NA	MILD	N		NONE	NOT RECOVERED/NOT RESOLVED
NA	MILD	N		PROBABLE	NOT RECOVERED/NOT RESOLVED

Table continues below
AESCAN	AESCONG	AESDISAB	AESDTH	AESHOSP	AESLIFE	AESOD	AEDTC
N	N	N	N	N	N	N	2014-02-20
N	N	N	N	N	N	N	2013-09-05

AESTDTC	AEENDTC	AESTDY	AEENDY	DATASET
2014-02-12		174	NA	AE
2013-08-25		3	NA	AE

# and variable labels
labelVarsSDTM <- attr(dataSDTM, "labelVars")
head(labelVarsSDTM)

##                               STUDYID                                DOMAIN 
##                    "Study Identifier"                 "Domain Abbreviation" 
##                               USUBJID                                 AESEQ 
##           "Unique Subject Identifier"                     "Sequence Number" 
##                                AESPID                                AETERM 
##          "Sponsor-Defined Identifier" "Reported Term for the Adverse Event"

2.3 Variable labels

Typical data stored in SAS contains the label for each variable available in the dataset.

The function getLabelVar extracts the label for the specified variable(s), ensuring that the variable code is used if no label is specified.

# variable label is extracted from 'labelVars'
getLabelVar(var = "AEDECOD", labelVars = labelVars)

##                   AEDECOD 
## "Dictionary-Derived Term"

This function also supports extraction of variable labels from the ‘label’ attribute of the column, as in the tibble as returned by the read_sas/read_xpt functions from the haven package.

2.4 Get parameter label from its parameter code

In typical basic CDISC dataset (a.k.a BDS: Basic Data Structure), as laboratory, vital signs datasets, variables are available to store parameter and parameter code (PARAM/PARAMCD in ADaM).

The function getLabelParamcd get the label from specific parameter(s) code.

Variables with parameter name and code are based by default on standard ADaM CDISC parameter variables (PARAM/PARAMCD).

# For ADaM dataset
getLabelParamcd(paramcd = "CHOL", data = dataADaM$ADLB)

##                   CHOL 
## "Cholesterol (mmol/L)"

getLabelParamcd(paramcd = "BILI", data = dataADaM$ADLB)

##                 BILI 
## "Bilirubin (umol/L)"

# For SDTM dataset
getLabelParamcd(paramcd = "CHOL", data = dataSDTM$LB, paramcdVar = "LBTESTCD", paramVar = "LBTEST")

##          CHOL 
## "Cholesterol"

getLabelParamcd(paramcd = "BILI", data = dataSDTM$LB, paramcdVar = "LBTESTCD", paramVar = "LBTEST")

##        BILI 
## "Bilirubin"

3 Visualizations

3.1 Palette for CDISC variables

Palette for typical CDISC-variable(s) are available in the package.

3.1.1 Normal reference range indicators

Meaningful colors and symbols for a Normal Reference Range Indicator CDISC variable (-NRIND) are extracted via the colorPaletteNRIND and shapePaletteNRIND respectively:

print(colorPaletteNRIND)

##      LOW   NORMAL     HIGH ABNORMAL  UNKNOWN       NA 
## "orange" "green4" "orange"    "red"   "grey"   "grey"

print(shapePaletteNRIND)

##      LOW   NORMAL     HIGH ABNORMAL  UNKNOWN       NA 
##       25       21       24       18        3        3

plot(
    x = seq_along(colorPaletteNRIND),
    col = colorPaletteNRIND, 
    bg = colorPaletteNRIND, 
    pch = shapePaletteNRIND
)
text(
    x = seq_along(colorPaletteNRIND),
    labels = names(colorPaletteNRIND), pos = 3
)
title("Palette for CDISC normal reference range indicator")

The getPaletteCDISC function extracts such palette for a specified variable.

This retains only the categories available in the variable, and ensures that extra symbols are extracted in case non standard categories are available in the data.

dataPlot <- subset(dataSDTM$LB, LBTEST == "Leukocytes")

colorPalette <- getPaletteCDISC(x = dataPlot$LBNRIND, var = "NRIND", type = "color")
print(colorPalette)

##      LOW   NORMAL     HIGH 
## "orange" "green4" "orange"

shapePalette <- getPaletteCDISC(x = dataPlot$LBNRIND, var = "NRIND", type = "shape")
print(shapePalette)

##    LOW NORMAL   HIGH 
##     25     21     24

# visualize profile over time
gg <- ggplot(data = dataPlot) +
    geom_point(aes(x = LBDY, y = LBSTRESN, 
            color = LBNRIND, fill = LBNRIND, shape = LBNRIND)) +
    ggtitle("Evolution of Leukocytes actual value over time")
print(gg)

# use 'standard' symbols/colors
# ('limits' is only required if the categories are not already ordered in LBNRIND)
gg + 
    scale_color_manual(values = colorPalette, limits = names(colorPalette)) +
    scale_fill_manual(values = colorPalette, limits = names(colorPalette)) +
    scale_shape_manual(values = shapePalette, limits = names(colorPalette))

3.2 Get default palettes

Default palettes for visualizations are included in the package.

Palettes can be extracted based on a variable, or number of elements.

These packages are included for consistency across the entire suite of R packages.

dataPlot <- subset(dataADaM$ADLB, PARAMCD == "CHOL")

# extract palettes
colorPalette <- getColorPalette(x = dataPlot$USUBJID)
shapePalette <- getShapePalette(x = dataPlot$USUBJID)
linetypePalette <- getLinetypePalette(x = dataPlot$USUBJID)

# create the plot
ggplot(data = dataPlot, aes(x = ADY, y = CHG, color = USUBJID)) +
    geom_point(aes(shape = USUBJID)) +
    geom_line(aes(linetype = USUBJID, group = USUBJID)) +
    scale_color_manual(values = colorPalette) +
    scale_shape_manual(values = shapePalette) +
    scale_linetype_manual(values = linetypePalette) +
    labs(x = "Relative day", y = "Change from baseline",
        title = "Profile plot of cholesterol change from baseline")

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 row(s) containing missing values (geom_path).

Custom palettes can be specified via the palette parameter.

4 Tables

4.1 Rounding

In R, numbers are by default rounded to the even digit for rounding off a 5.

Numbers can be rounded based on the ‘rounding up’ strategy for rounding off a 5 with roundHalfUp & roundHalfUpTextFormat. This is useful when statistics created with the SAS software should be reproduced in R.

# round up
roundHalfUp(c(0.45, 0.55), 1)

## [1] 0.5 0.6

# versus R default:
round(c(0.45, 0.55), 1)

## [1] 0.4 0.6

4.2 Display data in an interactive table

The getClinDT function is an utility function, based on the the DT package, with sensitive default settings and extra common functionalities of interest for data in clinical trials, as listing or summary tables of descriptive statistics.

There are built-in functionalities to expand row variable(s) to display patient-specific information or include bar visualization.

dataTEAE <- subset(dataADaM$ADAE, SAFFL == "Y" & TRTEMFL == "Y")

# set column names to labels
labelVarsTEAE <- getLabelVar(
    var = colnames(dataTEAE), 
    labelVars = labelVarsADaM
)
colnamesTEAE <- setNames(names(labelVarsTEAE), labelVarsTEAE)

dataTEAE <- dataTEAE[order(dataTEAE$AESOC), ]

getClinDT(
    dataTEAE, 
    colnames = colnamesTEAE, 
    rowGroupVar = c("AESOC"),
    barVar = "AGE",
    barRange = c(0, 100),
    caption = "Listing of treatment-emergent adverse events on the safety analysis set"
)

4.3 Comparison of tables

In clinical trials, datasets are typically delivered in successive batches, depending on the patient recruitment and the different milestones of the study.

The changes between successive data deliveries can be compared with the compareTables function.

# Build example dataset with treatment-emergent adverse events
# of multiple batches

varsListing <- c("USUBJID", "AESOC", "AEDECOD", "ASTDT", "AESEV", "AEOUT")
dataTEAEListing <- dataTEAE[, varsListing]

# simulate removal of observations in new batch
dataTEAENew <- dataTEAE[-sample.int(n = nrow(dataTEAEListing), size = 3), ]

# simulate addition of observations in new batch
dataTEAEOld <- dataTEAE[-sample.int(n = nrow(dataTEAEListing), size = 3), ]

# simulate change of observations       
dataTEAEOld[seq_len(2), "AESEV"] <- "SEVERE"

refVars <- c("USUBJID", "AESOC", "AEDECOD", "ASTDT")
tableComparison <- compareTables(
    newData = dataTEAENew, 
    oldData = dataTEAEOld, 
    referenceVars = refVars,
    changeableVars = setdiff(colnames(dataTEAEListing), refVars),
    # parameters passed to datatable
    colnames = setNames(names(labelVarsADaM), labelVarsADaM)
)

The new, old datasets with change information, or the difference between datasets are extracted. See documentation of outputType parameter for further details.

The table below highlight the differences between the datasets in an interactive table.

tableComparison$`table-comparison-interactive`

5 Reporting

5.1 Include list of objects in a Rmarkdown document

Dedicated functions: knitPrintListPlots and knitPrintListObjects are available to include a list of plots/objects in a Rmarkdown document, enabling to specify any (knitr) chunk options and title header for each object.

This function inserts each object in a separated code chunk, such as independent option (as figure dimensions) can be specified for each object.

Please note that this function should be used within a chunk having the option: results = 'asis'.

5.1.1 Static visualizations (`ggplot2`)

For example, by default in knitr the options to specify figure dimensions should be the same for all plots generated from the same chunk (fig.height/fig.width).

Plots can be included with different specified dimension with: knitPrintListPlots.

dataLB <- subset(dataSDTM$LB, 
    LBTESTCD %in% c("ALB", "ALT", "CHOL", "HCT", "KETONES", "PH")
)
dataLB$ACTARM <- dataSDTM$DM$ACTARM[match(dataLB$USUBJID, dataSDTM$DM$USUBJID)]

# create plots:
listPlotsLB <- plyr::dlply(dataLB, "LBCAT", function(data)
      ggplot(data = data) +
          geom_histogram(aes(fill = LBNRIND, x = ACTARM), stat = "count", position = "dodge") +
          facet_wrap(~LBTEST) +
          theme(axis.text.x = element_text(angle = -45, hjust = 0))
)
# n2mfrow: extract default dimensions for a specified number of plots
figDim <- plyr::dlply(dataLB, "LBCAT", function(data) 
      n2mfrow(length(unique(data$LBTESTCD)))
)
knitPrintListPlots(
    plotsList = listPlotsLB, 
    generalLabel = "lab-hist-static",
    type = "ggplot2",
    # set caption for each figure
    fig.cap = paste("Barplot of", tolower(names(listPlotsLB)), "measurements"),
    # specify different dimensions
    fig.width = sapply(figDim, "[[", 1) * 2 + 1, # 3 in for each plot + 1 in for the legend
    fig.height = sapply(figDim, "[[", 2) * 2 + 2, # 3 in for each plot + 2 for x-axis labels
    # include title before each visualization
    titles = simpleCap(tolower(names(listPlotsLB))),
    titleLevel = 4
)

5.1.1.1 Chemistry

Barplot of chemistry measurements

5.1.1.2 Hematology

Barplot of hematology measurements

5.1.1.3 Urinalysis

Barplot of urinalysis measurements

5.1.2 Interactive visualizations (`plotly`)

A list of interactive figures is created with the plotly package:

library(plotly)
listPlotsInteractiveLB <- sapply(listPlotsLB, function(ggplot)
      ggplotly(ggplot) %>% partial_bundle()
    , simplify = FALSE)

5.1.2.1 `htmltools::tagList`

A list of interactive figures can be included within a Rmarkdown document with the tagList function of the htmltools package.

tagListArgs <- mapply(list,
    # section header
    lapply(names(listPlotsInteractiveLB), htmltools::h4),
    # interactive plots
    listPlotsInteractiveLB,
    SIMPLIFY = FALSE
)
tagListArgs <- unlist(tagListArgs, recursive = FALSE)
do.call(htmltools::tagList, tagListArgs)

CHEMISTRY

HEMATOLOGY

URINALYSIS

5.1.2.2 Inclusion in a separated chunk

The function knitPrintListPlots with the type set to ‘plotly’ enables to include additionally e.g. a caption or a title.

knitPrintListPlots(
    plotsList = listPlotsInteractiveLB, 
    generalLabel = "lab-hist-interactive",
    type = "plotly",
    # include title before each visualization
    titles = simpleCap(tolower(names(listPlotsInteractiveLB))),
    titleLevel = 5
)

5.1.2.2.1 Chemistry

5.1.2.2.2 Hematology

5.1.2.2.3 Urinalysis

5.1.3 Tables (`flextable`)

The flextable package enables to create highly customizable tables for Word/PowerPoint format (among others).

The function knitPrintListObjects enables to include a list of flextable objects within a single chunk.

Please note that the following chunk option should be used: results = 'asis'.

library(flextable)
listFtLB <- plyr::dlply(dataLB, "LBCAT", function(dataParcat){
      flextable::flextable(data = head(dataParcat))
    })

knitPrintListObjects(
    xList = listFtLB, 
    generalLabel = "lab-listing-ft",
    titles = simpleCap(tolower(names(listFtLB))),
    # different alignment for each table
    ft.align = c("center", "right", "left"),
    titleLevel = 4
)

5.1.3.1 Chemistry

5.1.3.2 Hematology

5.1.3.3 Urinalysis

Please note that the table alignment option is more visible in a Word output format.

6 Appendix

6.1 Session info

R version 4.1.1 (2021-08-10)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_GB.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_GB.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_GB.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_GB.UTF-8 and LC_IDENTIFICATION=C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: flextable(v.0.6.8), plotly(v.4.9.4.1), plyr(v.1.8.6), htmltools(v.0.5.2), pander(v.0.6.4), ggplot2(v.3.3.5), clinUtils(v.0.1.0) and knitr(v.1.34)

loaded via a namespace (and not attached): tidyselect(v.1.1.1), xfun(v.0.26), purrr(v.0.3.4), haven(v.2.4.3), colorspace(v.2.0-2), vctrs(v.0.3.8), generics(v.0.1.0), viridisLite(v.0.4.0), base64enc(v.0.1-3), yaml(v.2.2.1), utf8(v.1.2.2), rlang(v.0.4.11), jquerylib(v.0.1.4), pillar(v.1.6.2), glue(v.1.4.2), withr(v.2.4.2), gdtools(v.0.2.3), uuid(v.0.1-4), lifecycle(v.1.0.0), stringr(v.1.4.0), munsell(v.0.5.0), gtable(v.0.3.0), zip(v.2.2.0), htmlwidgets(v.1.5.4), evaluate(v.0.14), labeling(v.0.4.2), forcats(v.0.5.1), tzdb(v.0.1.2), fastmap(v.1.1.0), crosstalk(v.1.1.1), curl(v.4.3.2), fansi(v.0.5.0), highr(v.0.9), Rcpp(v.1.0.7), readr(v.2.0.1), scales(v.1.1.1), DT(v.0.19), jsonlite(v.1.7.2), systemfonts(v.1.0.2), farver(v.2.1.0), hms(v.1.1.0), digest(v.0.6.27), stringi(v.1.7.4), dplyr(v.1.0.7), grid(v.4.1.1), tools(v.4.1.1), magrittr(v.2.0.1), lazyeval(v.0.2.2), tibble(v.3.1.4), crayon(v.1.4.1), tidyr(v.1.1.3), pkgconfig(v.2.0.3), ellipsis(v.0.3.2), xml2(v.1.3.2), data.table(v.1.14.0), officer(v.0.4.0), rmarkdown(v.2.11), httr(v.1.4.2), R6(v.2.5.1) and compiler(v.4.1.1)

Introduction to the `clinUtils` package

Laure Cougnaud and Michela Pasetto

September 21, 2021

1 Introduction

2 Data pre-processing

2.1 Data import

2.2 Example datasets

2.2.1 ADaM

2.2.2 SDTM

2.3 Variable labels

2.4 Get parameter label from its parameter code

3 Visualizations

3.1 Palette for CDISC variables

3.1.1 Normal reference range indicators

3.2 Get default palettes

4 Tables

4.1 Rounding

4.2 Display data in an interactive table

4.3 Comparison of tables

5 Reporting

5.1 Include list of objects in a Rmarkdown document

5.1.1 Static visualizations (`ggplot2`)

5.1.1.1 Chemistry

5.1.1.2 Hematology

5.1.1.3 Urinalysis

5.1.2 Interactive visualizations (`plotly`)

5.1.2.1 `htmltools::tagList`

CHEMISTRY

HEMATOLOGY

URINALYSIS

5.1.2.2 Inclusion in a separated chunk

5.1.2.2.1 Chemistry

5.1.2.2.2 Hematology

5.1.2.2.3 Urinalysis

5.1.3 Tables (`flextable`)

5.1.3.1 Chemistry

5.1.3.2 Hematology

5.1.3.3 Urinalysis

6 Appendix

6.1 Session info

Introduction to the clinUtils package

Laure Cougnaud and Michela Pasetto

September 21, 2021

1 Introduction

2 Data pre-processing

2.1 Data import

2.2 Example datasets

2.2.1 ADaM

2.2.2 SDTM

2.3 Variable labels

2.4 Get parameter label from its parameter code

3 Visualizations

3.1 Palette for CDISC variables

3.1.1 Normal reference range indicators

3.2 Get default palettes

4 Tables

4.1 Rounding

4.2 Display data in an interactive table

4.3 Comparison of tables

5 Reporting

5.1 Include list of objects in a Rmarkdown document

5.1.1 Static visualizations (ggplot2)

5.1.1.1 Chemistry

5.1.1.2 Hematology

5.1.1.3 Urinalysis

5.1.2 Interactive visualizations (plotly)

5.1.2.1 htmltools::tagList

CHEMISTRY

HEMATOLOGY

URINALYSIS

5.1.2.2 Inclusion in a separated chunk

5.1.2.2.1 Chemistry

5.1.2.2.2 Hematology

5.1.2.2.3 Urinalysis

5.1.3 Tables (flextable)

5.1.3.1 Chemistry

5.1.3.2 Hematology

5.1.3.3 Urinalysis

6 Appendix

6.1 Session info

Introduction to the `clinUtils` package

5.1.1 Static visualizations (`ggplot2`)

5.1.2 Interactive visualizations (`plotly`)

5.1.2.1 `htmltools::tagList`

5.1.3 Tables (`flextable`)