Plot merge validation diagnostics

Create diagnostic plots from a ValidateMerge() result object. This function visualizes key merge-audit outputs, including validation check status, key coverage, join-variable auditing, duplicate-variable agreement, and duplicate-variable conflict counts. Use this after running ValidateMerge() to quickly inspect whether a merged dataset appears trustworthy.

Usage

PlotMergeValidation(
  MergeObj,
  Plot = c("All", "Checks", "Coverage", "JoinAudit", "Agreement", "Conflicts"),
  interactive = TRUE,
  Interactive = lifecycle::deprecated()
)

Arguments

MergeObj

A list returned by ValidateMerge().

Plot

Character value specifying which plot to return. Options are "All", "Checks", "Coverage", "JoinAudit", "Agreement", and "Conflicts". Default is "All".

interactive

Logical; if TRUE, plots are converted to interactive plotly objects using plotly::ggplotly(). Default is TRUE.

Interactive

Deprecated (since 19.15.0). Use interactive instead.

Value

If Plot = "All", a named list of plots. Otherwise, a single plot object. Plot objects are either ggplot objects or plotly htmlwidgets, depending on Interactive.

Details

The function expects the object returned by ValidateMerge(). It does not re-run any merge validation checks.

Examples

set.seed(1)

# `site` comes from both sources and disagrees, leaving a site.x/site.y pair
left <- data.frame(
  id = 1:50,
  site = sample(c("A", "B"), 50, replace = TRUE),
  x = rnorm(50)
)
right <- data.frame(
  id = c(1:45, 101:105),
  site = sample(c("A", "B"), 50, replace = TRUE),
  y = rnorm(50)
)
merged <- merge(left, right, by = "id")

validation <- ValidateMerge(left, right, merged, keys = "id")

diagnostics <- PlotMergeValidation(
  validation,
  Plot = "All",
  interactive = FALSE
)

# Merge-check status, key coverage, join audit, agreement, and conflicts
diagnostics$Checks

diagnostics$Coverage

diagnostics$JoinAudit

diagnostics$Agreement

diagnostics$Conflicts