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.
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"
)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") # posterhvtiPlotR 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| 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")| 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 |
| 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 |
| 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 |
| 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 |
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 functionsThe online reference is at https://ehrlinger.github.io/hvtiPlotR/.
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.
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.
This project adheres to the Contributor Covenant. By participating, you are expected to uphold this code.