One or multiple types of dependencies | Building and visualising a dependency network | Boundary and giant component | Topological ordering of nodes | Going forward

Introduction | 1. Inheritance transparency | 1.1 Global, local, and mixed aes() | 1.2 Global vs per-layer data | 1.3 Layer order | 2. Bar chart equivalence | 2.1 Direct equivalents | 2.2 geom_col() — structural mode required | 2.3 coord_flip() — visual mode required | 2.4 Scale name vs labs() — visual mode required | 3. Count plot equivalence | 3.1 geom_count() variants — direct equivalents | 3.2 geom_point(aes(size = n)) + pre-counted data — separate equivalence group | 3.5 Pre-counted bar charts — exhausting the equivalence cases | 4. Histogram binning variants | 5. Pending work | 5.1 Cross-geom stat equivalence | 5.2 Multi-dataset layer verification

Introduction | A motivating example | Layer specification | Controlling inheritance | Aesthetic specification (long format) | Scale specification | Facet specification | Label specification | Coordinate system specification | The master summary: spec_plot()

Introduction | Comparing two plots with equiv_plot() | Running individual checks | The exact argument | Framework-agnostic checking with check_plot() | Swapping in a learnr/gradethis fail function | Using expect_equiv_plot() in testthat | Inspecting the diff | Comparing layer parameters | Canonicalisation-aware comparison with compare_plots() | Modes | Using a mode in check_plot() | What each mode covers | Summary of available checks

All types of dependencies, in a data frame | Network of one type of dependencies, as an igraph object | External reverse dependencies & defunct packages | Summary statistics | Visualisation | Going forward

Obtaining the reverse dependencies | Exploratory analysis and selecting the threshold | Fitting the discrete power law | Fitting extreme value mixture distribution