Package index • hvtiPlotR

Package Overview

Package-level documentation and the base S3 class shared by all hvtiPlotR data objects.

hvtiPlotR-package hvtiPlotR: hvtiPlotR: Publication-Quality Graphics for Clinical Manuscripts
is_hv_data(): Test whether an object is an hvtiPlotR data object

Themes

Apply publication-quality themes to any ggplot2 object. Use hv_theme() as the single entry point; the lower-level hv_theme_*() functions are also exported for direct use.

hv_theme(): hvtiPlotR Theme Generic
hv_theme_manuscript() theme_manuscript() theme_man(): Theme for Manuscript Figures
hv_theme_dark_ppt() theme_dark_ppt() theme_ppt() hv_theme_ppt(): Dark PowerPoint Theme (default PPT theme)
hv_theme_light_ppt() theme_light_ppt(): Light PowerPoint Theme
hv_theme_poster() theme_poster(): Theme for Poster Figures

Survival & Hazard

Kaplan-Meier survival curves, parametric hazard plots, survival differences, and number-needed-to-treat. Use hv_survival() for non-parametric KM estimates; hv_hazard(), hv_survival_difference(), and hv_nnt() for pre-fitted parametric model output.

hv_survival(): Prepare survival data for plotting
plot(<hv_survival>): Plot an hv_survival object
print(<hv_survival>): Print an hv_survival object
hv_hazard(): Prepare parametric hazard / survival data for plotting
plot(<hv_hazard>): Plot an hv_hazard object
print(<hv_hazard>): Print an hv_hazard object
hv_survival_difference(): Prepare survival difference (life-gained) data for plotting
plot(<hv_survival_difference>): Plot an hv_survival_difference object
print(<hv_survival_difference>): Print an hv_survival_difference object
hv_nnt(): Prepare number-needed-to-treat data for plotting
plot(<hv_nnt>): Plot an hv_nnt object
print(<hv_nnt>): Print an hv_nnt object
sample_survival_data(): Generate Sample Survival Data
sample_hazard_data(): Sample Parametric Hazard Model Predictions
sample_hazard_empirical(): Sample Kaplan-Meier Empirical Points for Hazard Plot Overlay
sample_life_table(): Sample Population Life Table Data
sample_survival_difference_data(): Sample Survival Difference (Life-Gained) Data
sample_nnt_data(): Sample Number Needed to Treat Data
nonparametric: Nonparametric survival estimates
parametric: Parametric survival estimates

Nonparametric Temporal Curves

Average temporal curves and ordinal outcome trajectories from decomposition models (tp.np.* template family).

hv_nonparametric(): Prepare nonparametric temporal trend curve data for plotting
plot(<hv_nonparametric>): Plot an hv_nonparametric object
print(<hv_nonparametric>): Print an hv_nonparametric object
hv_ordinal(): Prepare nonparametric ordinal outcome curve data for plotting
plot(<hv_ordinal>): Plot an hv_ordinal object
print(<hv_ordinal>): Print an hv_ordinal object
sample_nonparametric_curve_data(): Sample Nonparametric Curve Data
sample_nonparametric_curve_points(): Sample Nonparametric Curve Data Points
sample_nonparametric_ordinal_data(): Sample Nonparametric Ordinal Curve Data
sample_nonparametric_ordinal_points(): Sample Nonparametric Ordinal Data Points

Propensity Score & Matching

Visualise propensity score distributions and covariate balance before and after propensity matching or IPTW weighting.

hv_mirror_hist(): Prepare mirror-histogram data for plotting
plot(<hv_mirror_hist>): Plot an hv_mirror_hist object
print(<hv_mirror_hist>): Print an hv_mirror_hist object
hv_balance(): Prepare covariate balance data for plotting
plot(<hv_balance>): Plot an hv_balance object
print(<hv_balance>): Print an hv_balance object
sample_mirror_histogram_data(): Generate Sample Data for Mirrored Histogram
sample_covariate_balance_data(): Generate Sample Covariate Balance Data

Temporal Trends & Longitudinal

Annual trend lines, individual patient trajectories, and longitudinal count summaries. Ports tp.lp.trends.*, tp.rp.trends.*, tp.dp.trends.R, and tp.dp.spaghetti.echo.R.

hv_trends(): Prepare temporal trend data for plotting
plot(<hv_trends>): Plot an hv_trends object
print(<hv_trends>): Print an hv_trends object
hv_spaghetti(): Prepare spaghetti / profile data for plotting
plot(<hv_spaghetti>): Plot an hv_spaghetti object
print(<hv_spaghetti>): Print an hv_spaghetti object
hv_longitudinal(): Prepare longitudinal participation counts data for plotting
plot(<hv_longitudinal>): Plot an hv_longitudinal object
print(<hv_longitudinal>): Print an hv_longitudinal object
sample_trends_data(): Sample Temporal Trend Data
sample_spaghetti_data(): Sample Spaghetti / Profile Plot Data
sample_longitudinal_counts_data(): Sample Longitudinal Counts Data

Study Design & Goodness of Follow-Up

Visualise follow-up completeness. Ports tp.dp.gfup.R.

hv_followup(): Prepare goodness-of-follow-up data for plotting
plot(<hv_followup>): Plot an hv_followup object
print(<hv_followup>): Print an hv_followup object
sample_goodness_followup_data(): Generate Sample Goodness-of-Follow-Up Data

Stacked Histogram

Stacked or filled histogram of a numeric variable by group.

hv_stacked(): Prepare stacked histogram data for plotting
plot(<hv_stacked>): Plot an hv_stacked object
print(<hv_stacked>): Print an hv_stacked object
sample_stacked_histogram_data(): Generate Sample Data for Stacked Histogram

Flow Diagrams

Alluvial (Sankey) plots showing patient flow between states or cluster assignments across K values.

hv_alluvial(): Prepare alluvial / Sankey diagram data for plotting
plot(<hv_alluvial>): Plot an hv_alluvial object
print(<hv_alluvial>): Print an hv_alluvial object
hv_sankey(): Prepare cluster stability Sankey data for plotting
plot(<hv_sankey>): Plot an hv_sankey object
print(<hv_sankey>): Print an hv_sankey object
sample_alluvial_data(): Sample Sankey / Alluvial Data
sample_cluster_sankey_data(): Sample Cluster Stability Sankey Data

Exploratory Data Analysis

Rapid bar charts and scatter plots for variable screening, univariate summaries, and set-membership visualisation.

hv_eda(): Prepare EDA data for a single variable
plot(<hv_eda>): Plot an hv_eda object
print(<hv_eda>): Print an hv_eda object
hv_upset(): Prepare UpSet co-occurrence data for plotting
plot(<hv_upset>): Plot an hv_upset object
print(<hv_upset>): Print an hv_upset object
eda_classify_var(): Classify a Variable as Continuous or Categorical
eda_select_vars(): Select and Reorder Variables from a Data Frame
sample_eda_data(): Sample EDA Data
sample_upset_data(): Sample Procedure Co-occurrence Data

Saving & Utilities

Save figures to PowerPoint or PDF. Add draft footnotes during analysis; omit them for publication-ready output.

save_ppt(): Save ggplot Objects to an Editable PowerPoint Presentation
make_footnote() makeFootnote(): Add a Draft Footnote to a Figure