HVTI Themes and Clinical Plot Functions for the Cleveland Clinic Heart & Vascular Institute — nomorevibe
HVTI Themes and Clinical Plot Functions for the Cleveland Clinic Heart & Vascular Institute
Themes, clinical plot constructors and methods for creating publication quality ggplot2 graphics in R that conform to the standards of Cardiovascular Outcomes Registries and Research (CORR) within the
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hvtiPlotR: ggplot2 themes and plot functions for CORR clinical figures
hvtiPlotR is an R port of the plot.sas macro suite that CORR biostatisticians have used for years. It keeps the same visual layout and options, built through ggplot2's constructor + plot() pattern instead of PROC GPLOT statements. Where a macro's job is also a statistical estimate (%kaplan / %nelsont survival curves, for instance), the R constructor wraps the same underlying estimator (survival::survfit()), so the curve, confidence limits, and numbers at risk match what the SAS macro reports. The SAS migration guide vignette walks every macro to its hv_*() replacement.
hvtiPlotR packages the ggplot2 themes and plot functions we use in Cardiovascular Outcomes Registries and Research (CORR) at the Cleveland Clinic Heart & Vascular Institute. If you are migrating from the plot.sas macro, this is its R successor.
hvtiR::install() installs every member in one call, so the versions the
members require of one another resolve together. Update later with
hvtiR::update(), from a fresh R session: it will not overwrite a package
that is already loaded.
To install hvtiPlotR on its own:
pak::pak("ehrlinger/hvtiPlotR")
Quick Start
Apply an HVTI theme to a ggplot2 figure in one line:
Export the result directly to an editable PowerPoint slide. A small dark CORR
template ships with the package, so you can save without supplying your own:
Four themes cover the publication contexts we use. Each follows the
theme_bw() contract. Pass base_size / base_family, or chain + theme(...),
to override anything:
Theme
Best for
theme_hv_manuscript()
Journal figures, white background
theme_hv_poster()
Conference posters
theme_hv_ppt_dark()
Dark-background PowerPoint slides (black panel, white text)
theme_hv_ppt_light()
Light/transparent PowerPoint slides
p + theme_hv_manuscript() # journal figure
p + theme_hv_ppt_dark() # dark PPT slide
p + theme_hv_ppt_light() # light PPT slide
p + theme_hv_poster() # poster
The two PPT themes default to Arial 32 Bold axis tick labels and Arial 40
Bold axis titles, matching the standard CORR slide deck.
The earlier alias functions (theme_man(), theme_ppt(), theme_dark_ppt(),
theme_light_ppt(), theme_poster(), and the hv_theme_*() family) still
work but are deprecated (one-time warning); prefer the theme_hv_*() names
above. The old hv_theme("...") string dispatcher has been removed.
Plot Function Gallery
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) + theme_hv_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()
Standardized mean difference dot-plot for assessing propensity matching or weighting quality
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
hv_atrisk() / hv_atrisk_compose()
Numbers-at-risk table panel, and a composer that stacks a survival curve over it with aligned time axes
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_correlation_matrix()
Lower-triangle scatter-plot matrix for numeric variables, with pairwise deletion per panel and the coefficient matrix in $tables$coefficients
hv_upset()
UpSet diagram for visualizing procedure co-occurrences or set memberships
hv_venn()
Venn diagram of 2-3 overlapping set memberships, with a region-count table; the small-set-count companion to hv_upset()
hv_alluvial()
Sankey/alluvial diagram for patient flow across categorical stages
hv_sankey()
Cluster stability Sankey showing patient transitions across cluster solutions
Export a ggplot to an editable PowerPoint slide using an HVTI template; panel_box = list(width = ..., height = ..., left = ..., top = ...) (on by default) anchors the panel content area to the same slide coordinates on every slide
hv_ggsave_dims()
Compute ggsave()width/height that preserve a target panel content area regardless of axis-label, legend, or title size
hv_ph_location()
Compute officer::ph_location() args so a ggplot's panel lands at a fixed slide rectangle; the per-slide worker that save_ppt(panel_box=) calls
make_footnote()
Add a footnote annotation to the current figure
Vignettes
Five vignettes ship with the package. After installation, call any of them from the R console:
vignette("hvtiPlotR", package = "hvtiPlotR") # package tutorial: generating plot.sas-style figures in R
vignette("sas-migration-guide", package = "hvtiPlotR") # SAS template -> hvtiPlotR migration mapping
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
Slides
A short PowerPoint presentation covering the v2.x redesign (the two-step S3 API,
renamed theme functions, and save_ppt(panel_box =)) ships with the package
as a Quarto source file:
Render it to .pptx with quarto render or quarto::quarto_render(). The slides condense what you will find in vignette("plot-decorators") and vignette("hvtiPlotR").
Migrating from plot.sas
If you are moving existing SAS analyses to R, the SAS migration vignette (vignette("sas-migration-guide")) maps each plot.sas macro call to its R equivalent.
The inst/plot.README and inst/plot.sas files are preserved for historical reference.
Related Packages
hvtiPlotR is one part of the HVTI CORR R ecosystem. The packages a reader of this one is most likely to reach for next:
hvtiRpropensity builds the matched/weighted datasets and SMD balance tables that hv_mirror_hist() and hv_balance() are built to plot.
hvtiRdatabuild builds and verifies the analysis-ready CORR datasets you would feed into these constructors in place of the sample_*() generators.
hvtiRtables produces the Table 1 / manuscript tables that typically accompany the figures built here.
HVTI ggplot graphics recipes is the companion recipes book, with worked examples that tie the constructors above into a house style for clinical figures.
Contributing
Pull requests are welcome. 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)
