depictr: A Unified Toolkit for Visualising Statistical Models and Data

depictr provides a consistent set of plots that span the whole analysis workflow, from a first look at the data, through model estimates and predictions, to diagnostics, uncertainty and reporting. Every plotting function returns a ggplot2::ggplot object (or a 'patchwork' for composite panels), so results can be customised with the usual + syntax, and every plot shares one theme, one colourblind-aware palette and one set of label conventions.

Exploring data

explore_distribution(): histograms / densities of one variable.
ecdf_plot(): empirical cumulative distribution, optionally by group.
explore_categorical(): bar charts of a categorical variable.
explore_bivariate(): a suitable plot for any pair of variables.
explore_pairs(): a scatter-plot matrix.
correlation_heatmap(): correlations as a heatmap.
missingness_map(): a map of missing values.
outlier_plot(): box / violin plots flagging outliers.
raincloud_plot(): half-violin, box and raw points together.
ridgeline_plot(): overlapping per-group densities.
group_comparison_plot(): group means with confidence intervals.
estimation_plot(): group differences with bootstrap intervals.
dumbbell_plot(): a two-group comparison across categories.
scatter_trend(): scatter plot with a fitted trend.
summary_table(): a "Table 1" style descriptive summary.

Multivariate and survival

pca_plot(): principal-component biplot.
scree_plot(): variance explained by each component.
cluster_plot(): k-means clusters on principal-component axes.
silhouette_plot(): silhouette widths of a clustering.
k_diagnostic(): suggest a number of clusters.
dendrogram_plot(): hierarchical-clustering dendrogram.
survival_plot(): Kaplan-Meier survival curves.

Time series

timeseries_plot(): one or more series over time.
acf_plot(): autocorrelation / partial autocorrelation.
decompose_plot(): trend / seasonal / remainder decomposition.
seasonal_plot(): seasonal subseries across cycles.
ts_forecast(): a simple forecast with prediction intervals.

Model estimates and inference

tidy_estimates(): the shared tidy estimate table.
coefficient_plot(): forest / coefficient plot.
compare_models(): estimates from several models, side by side.
frequentist_bayesian_plot(): frequentist vs. Bayesian estimates.
effects_plot(): predicted values for one predictor.
interaction_plot(): predicted values across two predictors.
random_effects_plot(): caterpillar plot of random effects.
optimizer_fixef_plot(): fixed effects across optimisers.
model_fit_table(): goodness-of-fit statistics across models.

Diagnostics and classification

residual_diagnostics_plot(): residual-diagnostic panel.
binned_residual_plot(): binned residuals for logistic and other GLMs.
influence_plot(): influence and leverage.
qq_plot(): normal quantile-quantile plot.
vif_plot(): multicollinearity (variance inflation factors).
roc_curve_plot(): ROC curve(s) with AUC.
pr_curve_plot(): precision-recall curve with average precision.
gain_plot(): cumulative gains chart.
lift_plot(): cumulative lift chart.
calibration_plot(): calibration of predicted probabilities.
threshold_plot(): classification metrics across decision thresholds.
confusion_matrix_plot(): confusion matrix as a heatmap.

Uncertainty and power

posterior_plot(): posterior intervals and densities.
power_curve_plot(): power against sample size.

Theming and reporting

theme_depictr(): the shared theme.
depictr_palette(), scale_colour_depictr(): the shared palette.
palette_preview(): preview the palettes.
format_terms(): tidy raw coefficient names for display.
model_report(): a one-figure overview of a fitted model.
arrange_plots(): compose plots with a shared legend and title.
save_plot(): save a plot with publication-ready defaults.
depictr_options(): set package-wide defaults once.

Bundled data

lexical_decision, wellbeing_survey, crop_yield, clinical_trial and monthly_sales are reproducibly simulated datasets used throughout the examples and vignettes.

References

Wickham H (2016). ggplot2: Elegant graphics for data analysis, 2nd edition. Springer, Cham, Switzerland. ISBN 978-3-319-24277-4. doi:10.1007/978-3-319-24277-4 .

Author

Pablo Bernabeu, author and maintainer (pcbernabeu@gmail.com, ORCID).