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The hvtiPlotR package

ggplot2 themes and plot functions for creating publication-quality graphics in R, conforming to the standards of Cardiovascular Outcomes Registries and Research (CORR) within The Heart & Vascular Institute at the Cleveland Clinic. This package is the modern R replacement for the historical plot.sas macro.

Quick Start

Install from GitHub using remotes:

remotes::install_github("ehrlinger/hvtiPlotR")

Apply an HVI theme to any ggplot2 figure in one line:

library(ggplot2)
library(hvtiPlotR)

ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +
  geom_point() +
  hv_theme("manuscript")

Export the result directly to an editable PowerPoint slide:

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point() + hv_theme("ppt")

save_ppt(
  object     = p,
  template   = system.file("ClevelandClinic.pptx", package = "hvtiPlotR"),
  powerpoint = "outputs/figure1.pptx"
)

Themes

Use hv_theme() to apply any supported style, or call the named aliases directly:

Style key Alias(es) Best for
"manuscript" theme_manuscript(), theme_man() Journal figures, white background
"ppt" / "dark_ppt" theme_ppt(), theme_dark_ppt() Dark-background PowerPoint slides
"light_ppt" theme_light_ppt() Light/transparent PowerPoint slides
"poster" theme_poster() Conference posters 
p + hv_theme("manuscript")   # via generic
p + theme_man()                # direct alias
p + hv_theme("ppt")          # dark PPT
p + hv_theme("light_ppt")    # light PPT
p + hv_theme("poster")       # poster

hvtiPlotR 2.0 uses a two-step API. A constructor (hv_*()) shapes the data and returns an S3 object; plot() renders a bare ggplot. Add scale_*, labs(), annotate(), and a theme with the usual + operator.

km <- hv_survival(sample_survival_data())
plot(km) + hv_theme("manuscript")
km$tables$risk                         # risk-table data frame

Propensity Score & Balance

Constructor Description
hv_mirror_hist() Mirrored histograms showing propensity score distributions for two groups, before and after matching or IPTW weighting
hv_stacked() Stacked or proportional-fill histogram of a numeric variable by group
hv_balance() Standardised mean difference dot-plot for assessing propensity matching or weighting quality 
mh <- hv_mirror_hist(sample_mirror_histogram_data(n = 2000),
                  group_labels = c("SAVR", "TF-TAVR"))
plot(mh) + hv_theme("manuscript")

Survival & Time-to-Event

Constructor Description
hv_survival() Kaplan-Meier and Nelson-Aalen analysis. plot(km, type = ...) renders survival, cumulative hazard, hazard, log-log, or life/RMST; tables via km$tables
hv_nonparametric() Nonparametric survival or event-rate curve with optional confidence intervals
hv_ordinal() Nonparametric curves for ordinal outcomes (e.g. severity grades)
hv_hazard() Parametric hazard/survival curves from Weibull or other models, with optional KM overlay
hv_survival_difference() Absolute treatment benefit vs. a reference group over time
hv_nnt() Number needed to treat derived from survival difference estimates
hv_followup() Goodness-of-follow-up scatter: actual vs. potential follow-up by operation year; plot(gf, type = "event") for non-fatal competing events

Longitudinal & Repeated Measures

Constructor Description
hv_trends() Temporal trend: annual means/medians with LOESS smooth, by group
hv_spaghetti() Individual subject trajectories over time with optional per-group LOESS overlay
hv_longitudinal() Pre-aggregated patient and measurement counts; plot(lc, type = "table") for text table

Exploratory & Multivariate

Constructor Description
hv_eda() Exploratory plot for a single variable. Auto-detects type: scatter + LOESS for continuous, stacked bar for categorical. Missing values shown as "(Missing)"
hv_upset() UpSet diagram for visualising procedure co-occurrences or set memberships
hv_alluvial() Sankey/alluvial diagram for patient flow across categorical stages
hv_sankey() Cluster stability Sankey showing patient transitions across cluster solutions

Utilities

Function Description
hv_theme(style) Generic dispatcher returning the named ggplot2 theme
save_ppt() Export a ggplot to an editable PowerPoint slide using an HVI template
make_footnote() Add a footnote annotation to the current figure

Vignettes

Four vignettes ship with the package and are available after installation:

vignette("hvtiPlotR",        package = "hvtiPlotR")  # SAS migration guide
vignette("plot-functions",   package = "hvtiPlotR")  # per-function reference with worked examples
vignette("plot-decorators",  package = "hvtiPlotR")  # composition: scale_*, labs(), themes, saving
vignette("contributing",     package = "hvtiPlotR")  # guide for adding new plot functions

The online reference is at https://ehrlinger.github.io/hvtiPlotR/.

Migrating from plot.sas

If you are moving existing SAS analyses to R, see the SAS migration vignette (vignette("hvtiPlotR")). It maps each plot.sas macro call to the equivalent R recipe and shows side-by-side code comparisons.

The inst/plot.README and inst/plot.sas files are preserved for historical reference.

Contributing

Pull requests are welcome! Please read CONTRIBUTING.md for the full guide, which covers:

  • Track A — porting a SAS template (for biostatisticians and analysts adding a new plot function or sample-data generator)
  • Track B — package infrastructure (for R developers working on dependencies, CI, testing, and CRAN compliance)

Quick-start:

devtools::install_deps(dependencies = TRUE)
devtools::load_all()
devtools::check()

Open an issue to discuss a new port before writing code, and tag @ehrlinger for review on all pull requests.

Code of Conduct

This project adheres to the Contributor Covenant. By participating, you are expected to uphold this code.