This vignette focuses on the visualization functionalities available within the inTextSummaryTable package.

If you need to see figures bigger, just click on a graphic, and it will pop up more readable.

Summary statistics can be visualized by:

  1. extracting the summary statistics table with the computeSummaryStatisticsTable function (used for getSummaryStatistics function)
  2. visualizing the summary statistics via the subjectProfileSummaryPlot function

By default the subjectProfileSummaryPlot plots the mean values with error bars based on standard errors.

Moreover, the subjectProfileSummaryPlot allows the possibility to add a table of counts of the number of subject for a specific combination of variables.

This vignette will guide the user to

  • default visualizations for A4 and presentation documents
  • customize palettes of colors, shape, linetypes
  • visualization with and without table of counts and by facets
  • general customizable input

However, in order to get a full overview of the functionalities the documentation is available in the console with ? subjectProfileSummaryPlot.

We will first create example data sets to show how the exporting functionalities work. The data sets used here are available in the clinUtils package.

1 Load packages and data

library(inTextSummaryTable)
library(clinUtils)
library(pander)
# load example data
data(dataADaMCDISCP01)

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

2 Compute summary statistics

Below we compute summary statistics for the laboratory data set.

dataLB <- subset(dataAll$ADLBC, grepl("Baseline|Week", AVISIT))
dataLB$AVISIT <- with(dataLB, reorder(trimws(AVISIT), AVISITN))
dataLB$TRTA <- with(dataLB, reorder(TRTA, TRTAN))

summaryTableDf <- computeSummaryStatisticsTable(
    data = dataLB,
    var = "AVAL",
    rowVar = c("PARCAT1", "PARAM"),
    colVar = c("TRTA", "AVISIT")
)

3 Visualization for A4 document

The code below shows the default visualization, which plots the mean with standard errors suitable for an A4 document, e.g.ย Word/pdf.

Note that the table below the plot is based on the counts for treatment and visit variables, as specificed in the colVar argument of the code chuck above for computeSummaryStatisticsTable.

# create the plot
dataPlot <- subset(
    summaryTableDf, 
    !isTotal &
     PARAM == "Alanine Aminotransferase (U/L)"
)

subjectProfileSummaryPlot(
    data = dataPlot,
    xVar = "AVISIT",
    colorVar = "TRTA",
    labelVars = labelVars,
    useLinetype = TRUE,
    tableText = "statN"
)

4 Visualization for presentation

The code below shows the default visualization suitable for a presentation, either in Powerpoint or ioslides.

subjectProfileSummaryPlot(
    data = dataPlot,
    xVar = "AVISIT",
    colorVar = "TRTA",
    labelVars = labelVars,
    useLinetype = TRUE,
    tableText = "statN",
    style = "presentation"
)