Package {rankinPlot}

Type:	Package
Title:	Convenient Plotting for the Modified Rankin Scale and Other Ordinal Outcome Data
Version:	1.2.0
Maintainer:	Hannah Johns <dr.hannah.johns@gmail.com>
Description:	Provides convenient tools for visualising ordinal outcome data following conventions within stroke research literature. It currently supports the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401> and Probability-Probability plots for visualising Desirability of Outcome Ranking (DOOR) scales with large numbers of categories proposed by Johns et al. (2026) <doi:10.1177/17474930261475853>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5)
Imports:	ggplot2 (≥ 4.0), scales (≥ 1.2), rlang, grDevices, RColorBrewer, lifecycle
Suggests:	viridis
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-10-09 07:55:33 UTC; hannah
Author:	Hannah Johns [aut, cre], Andreas Gammelgaard Damsbo [aut], Florian van Leeuwen [aut]
Repository:	CRAN
Date/Publication:	2026-10-09 10:40:02 UTC

Effect of time to treatment on efficacy of rt-PA in treating acute stroke

Description

A dataset reconstructing reported outcomes after stroke as reported in Lees et. al. (2010) which pooled the results of several studies.

Usage

alteplase

Format

A data frame with 3669 rows and 3 variables:

time

Time interval (minutes) between stroke onset and treatment

treat

Type of treatment received

mRS

Outcome at 3 months measured using the modified Rankin Scale

References

Lees, K. R., Bluhmki, E., Von Kummer, R., Brott, T. G., Toni, D., Grotta, J. C., Albers, G. W., Kaste, M., Marler, J. R., Hamilton, S. A., Tilley, B. C., Davis, S. M., Donnan, G. A., Hacke, W. (2010). Time to treatment with intravenous alteplase and outcome in stroke: an updated pooled analysis of ECASS, ATLANTIS, NINDS, and EPITHET trials. The Lancet, 375(9727), 1695-1703.

grottaBar

Description

Automates the production of a Grotta Bar using ggplot()

Usage

grottaBar(
  x,
  groupName,
  scoreName,
  strataName = NULL,
  colorScheme = "Blues",
  colorScheme.reverse = FALSE,
  printNumbers = "count",
  nCol = 1,
  dir = "v",
  width = 0.9,
  textSize = 15,
  numberSize = 5,
  textFace = "plain",
  textColor = NULL,
  lineSize = 0.5,
  lineColor = "black",
  drawLines = TRUE,
  returnData = FALSE,
  ...
)

Arguments

Details

This tool produces a "Grotta" bar chart based on a table of count data. A Grotta bar chart is a common data visualisation tool in stroke research, and is in essence a horizontally stacked proportional bar chart showing the distribution of ordinal outcome data (typically the modified Rankin Scale) across groups, with lines drawn connecting categories across groups.

colorScheme supports all color schemes from ColorBrewer. See https://colorbrewer2.org for more information, or run RColorBrewer::display.brewer.all() to display available options. If the number of categories exceeds what is provided by ColorBrewer, additional values are interpolated as needed.

In addition, setting colorScheme to a ggplot2 discrete scale (e.g. ggplot2::scale_fill_brewer()) allows for a user-specified color scheme using the ggplot2 family of scale_fill_ functions.

The options for printNumbers are:

count

The raw counts in the table.

proportion

The within-group proportion, rounded to 2 decimal places.

percentage

The within-group percentage, rounded to 2 decimal places.

count.percentage

The raw count with percentage in parentheses.

none

Do not print any numbers.

These options may be abbreviated. "p" is not a valid abbreviation as it matches to multiple options. The minimal abbreviation for "count.percentage" is "c.p"

Value

If returnData is FALSE

A ggplot object

If returnData is TRUE

A list containing a ggplot object and the data used to generate it

References

National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. "Tissue plasminogen activator for acute ischemic stroke." New England Journal of Medicine 333.24 (1995): 1581-1588.

Rohmann, Jessica L., et al. "Adjusted horizontal stacked bar graphs ("Grotta bars") for consistent presentation of observational stroke study results." European Stroke Journal 8.1 (2023): 370-379.

Forrest, Meghan R., et al. "Use of stacked proportional bar graphs ("Grotta bars") in observational neurology research: a meta-research study." Neurology 104.4 (2025): e210169.

Examples

df <- alteplase

x <- table(mRS=df$mRS,
          Group=df$treat,
          Time=df$time)

grottaBar(x,groupName="Group",
         scoreName = "mRS",
         strataName="Time"
)


grottaBar(x,groupName="Time",
         scoreName = "mRS",
         strataName="Group",
         textColor = c(rep("black",4),rep("white",3))
)


x <- table(mRS=df$mRS,
          Group=df$treat)


grottaBar(x,groupName="Group",
         scoreName = "mRS",
         colorScheme = FALSE
)

grottaBar(x,groupName="Group",
         scoreName = "mRS",
         colorScheme = ggplot2::scale_fill_brewer(palette = "Spectral", direction=-1)
)


grottaBar(x,groupName="Group",
         scoreName = "mRS",
         colorScheme = FALSE
)+ ggplot2::scale_fill_brewer(palette = "Spectral", direction=-1)


grottaBar(x,groupName="Group",
         scoreName = "mRS",
         printNumbers = "count.percentage",
         colorScheme = "Greys"
)

# Example with IPTW weights
 
 alteplase_confounded <- alteplase
 ps = c("0-90" = 0.7,
        "91-180" = 0.6,
        "181-270" = 0.3,
        "271-360" = 0.7
 )
 set.seed(123)
 alteplase_confounded <- by(alteplase_confounded,alteplase_confounded$time,function(x){
 
         n_alt <- round(nrow(x)*ps[unique(x$time)])
         n_pbo <- round(nrow(x)*(1-ps[unique(x$time)]))
 
         this_probs <- prop.table(table(x$mRS,x$treat),margin=2)
        
        rbind(
                data.frame(treat="Alteplase",
                            mRS = sample(as.numeric(rownames(this_probs)),
                                        size = n_alt, replace=TRUE,
                                        prob=this_probs[,"Alteplase"]),
                            time=unique(x$time)
                            ),
                data.frame(treat="Placebo",
                            mRS = sample(as.numeric(rownames(this_probs)),
                                        size = n_pbo, replace=TRUE,
                                        prob=this_probs[,"Placebo"]),
                            time=unique(x$time)
                            )
        )
})
alteplase_confounded <- do.call("rbind",alteplase_confounded)
alteplase_confounded$treat <- factor(alteplase_confounded$treat,levels=levels(alteplase$treat))

# Raw table, with confounding
x_confounded <- xtabs(~ mRS + treat, data=alteplase_confounded) 

# Using IPTW to remove confounding

propensity <- ps[alteplase_confounded$time]
alteplase_confounded$iptw_weight <- ifelse(alteplase_confounded$treat == "Alteplase",
                                           1/propensity,
                                           1/(1-propensity)
                                          )
x_weighted <- xtabs(iptw_weight ~ mRS + treat, data=alteplase_confounded) 

grottaBar(x_confounded, groupName = "treat", scoreName = "mRS", printNumbers = "percentage")
grottaBar(x_weighted, groupName = "treat", scoreName = "mRS",printNumbers = "percentage")

# The original dataset for comparison
x <- xtabs(~ mRS + treat, data=alteplase) 
grottaBar(x, groupName = "treat", scoreName = "mRS", printNumbers = "percentage")

pp_plot

Description

Creates probability-probability plots for visualizing all-to-all comparisons of ranked data across two groups

Usage

pp_plot(x,
        groupName,
        scoreName,
        strataName = NULL,
        reverse.scores = FALSE,
        panel = TRUE,
        panel.nCol = NULL,
        panel.dir = "h",
        polygon = panel,
        polygon.alpha = 0.4,
        polygon.color = "#999999",
        polygon.win.fill = "#71f594",
        polygon.tie.fill = "#e8e156",
        polygon.loss.fill= "#f97194",
        contour = FALSE,
        contour.color = "#555555",
        contour.line.color = contour.color,
        contour.label.color = contour.color,
        confint = TRUE,
        confint.angle = "fixed",
        confint.level = 0.95,
        bar = TRUE,
        bar.colorScheme = "Blues",
        bar.colorScheme.reverse = FALSE,
        bar.width = 0.1,
        bar.lineColor = "black",
        bar.linewidth =  0.5,
        bar.text = "count",
        bar.text.size = 5,
        bar.text.color = NULL,
        bar.text.face = "plain",
        neutral.color = "#222222",
        neutral.linetype = "dashed",
        neutral.linewidth = 0.5,
        line.color = NULL,
        line.linetype = "solid",
        strata.text.size = bar.text.size,
        ...
        )

Arguments

Details

Confidence intervals are estimated using Fisher's exact test and correspond to an odds ratio. They are visually represented as segments on the figure, where the start and end points correspond to cumulative probabilities that match the upper/lower bound of the odds ratio. Two options are given for the direction of these lines. "fixed" indicates that all confidence intervals should be drawn at 45 degrees, while "proportional.odds" indicates that they should be drawn perpendicular to the proportional odds contour line.

bar.colorScheme supports all color schemes from ColorBrewer. See [https://colorbrewer2.org](https://colorbrewer2.org) for more information, or run RColorBrewer::display.brewer.all() to display available options. If the number of categories exceeds what is provided by ColorBrewer, additional values are interpolated as needed.

In addition, setting colorScheme to a ggplot2 discrete scale (e.g. ggplot2::scale_fill_brewer() allows for a user-specified color scheme using the ggplot2 family of scale_fill_ functions.

The options for bar.text are:

"count"

The raw counts in the table.

"proportion"

The within-group proportion, rounded to 2 decimal places.

"percentage"

The within-group percentage, rounded to 2 decimal places.

"count.percentage"

The raw count with percentage in parentheses.

"none", FALSE, NULL or NA

Do not print any numbers.

These options may be abbreviated. "p" is not a valid abbreviation as it matches to multiple options. The minimal abbreviation for "count.percentage" is "c.p"

Value

A ggplot object containing the plot.

References

Johns, Hannah, et al. "Practical guidance for Win Statistics and Tournament Methods for multifaceted outcomes in stroke research: Review and recommendations." International Journal of Stroke (2026) DOI: https://doi.org/10.1177/17474930261475853

Examples

df <- alteplase

x <- table(mRS=df$mRS,
          Group=df$treat,
          Time=df$time)

pp_plot(x,
       groupName =  "Group",
       scoreName = "mRS",
       strataName = "Time",
       panel = TRUE,
       confint = TRUE
)


pp_plot(x,
       groupName =  "Group",
       scoreName = "mRS",
       strataName = "Time",
       panel = FALSE,
       confint = FALSE
)


df <- remapcap
df <- df[which(df$group %in% c("uc","shock")),]
df$group <-droplevels(df$group)

x <- table(Score=df$score,
          Group=df$group
)



pp_plot(x,groupName="Group",
       scoreName = "Score",
       reverse.scores = TRUE,
       bar.text = FALSE,
       confint.angle = "fixed",
       bar.colorScheme = "RdBu",
       bar.colorScheme.reverse = TRUE
)

# Visually inspect proportional odds assumption
pp_plot(x,groupName="Group",
       scoreName = "Score",
       reverse.scores = TRUE,
       bar.text = FALSE,
       confint.angle = "proportional.odds",
       bar.colorScheme = "RdBu",
       bar.colorScheme.reverse = TRUE,
       contour = TRUE,
       polygon = FALSE,
       neutral.color = "darkred", neutral.linewidth =1
)



# Transform the data for comparison of two arms against a common control arm

df_shock <- remapcap[which(remapcap$group %in% c("uc","shock")),]
df_fixed <- remapcap[which(remapcap$group %in% c("uc","fixed")),]

df_shock$group2 <- ifelse(df_shock$group=="uc","Usual Care","Intervention")
df_fixed$group2 <- ifelse(df_fixed$group=="uc","Usual Care","Intervention")
df_shock$intervention <- "Shock"
df_fixed$intervention <- "Fixed"

df <- rbind(df_shock,df_fixed)
df$group2 <- factor(df$group2,levels=c("Usual Care","Intervention"))


x <- table(Score=df$score,
          Group=df$group2,
          Intervention=df$intervention
)



pp_plot(x,groupName="Group",
       scoreName = "Score",
       strataName = "Intervention",
       reverse.scores = TRUE,
       bar.text = FALSE,
       confint= FALSE,
       panel=FALSE,
       bar.colorScheme = "RdBu",
       bar.colorScheme.reverse = TRUE
)

Effect of hydrocortisone on mortality and organ support in patients with severe COVID-19

Description

A dataset reconstructing reported outcomes in severe COVID-19 as reported in Angus et al. (2020)

Usage

remapcap

Format

A data frame with 379 rows and 6 variables:

group

The treatment group in the trial. uc = Usual Care, shock = Shock-dependent hydrocortisone, fixed = Fixed-dose hydrocortisone

deathTime

Time of death, censored at 21 days

dischargeTime

Time of ICU discharge, censored at 21 days

deathStatus

If death was observed (0=no, 1=yes)

dischargeStatus

If ICU discharde was observed (0=no, 1=yes)

score

An ordinal score ranging from -1 to 21 covering death and organ support-free days

References

Angus, Derek C., et al. "Effect of hydrocortisone on mortality and organ support in patients with severe COVID-19: the REMAP-CAP COVID-19 corticosteroid domain randomized clinical trial." Jama 324.13 (2020): 1317-1329.