Title: Diagnostic Tools for Data Frame Joins
Version: 0.7.3
Language: en-US
Description: Provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in Wickham (2014) <doi:10.18637/jss.v059.i10>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: cli, rlang
Suggests: dplyr, data.table, tibble, testthat (≥ 3.0.0), knitr, rmarkdown, shiny, miniUI
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://gillescolling.com/joinspy/, https://github.com/gcol33/joinspy
BugReports: https://github.com/gcol33/joinspy/issues
Depends: R (≥ 4.1)
NeedsCompilation: no
Packaged: 2026-01-19 20:31:30 UTC; Gilles Colling
Author: Gilles Colling ORCID iD [aut, cre, cph]
Maintainer: Gilles Colling <gilles.colling051@gmail.com>
Repository: CRAN
Date/Publication: 2026-01-23 14:30:06 UTC

joinspy: Diagnostic Tools for Data Frame Joins

Description

Provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in Wickham (2014) doi:10.18637/jss.v059.i10.

Author(s)

Maintainer: Gilles Colling gilles.colling051@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

Analyze Match Between Two Key Sets

Description

Compares keys from two tables to determine overlap.

Usage

.analyze_match(x_keys, y_keys, x_n_rows)

Arguments

x_keys

Vector of keys from left table (NA removed).

y_keys

Vector of keys from right table (NA removed).

x_n_rows

Total rows in left table.

Value

A list with match analysis.

Analyze Multi-Column Key Breakdown

Description

For composite keys, determines which column(s) cause mismatches.

Usage

.analyze_multicolumn_keys(x, y, x_by, y_by)

Arguments

x

Data frame (left table).

y

Data frame (right table).

x_by

Column names in x.

y_by

Column names in y.

Value

A list with per-column match analysis.

Detect Case Mismatches Between Keys

Description

Finds keys that would match if case-insensitive but don't match case-sensitive.

Usage

.detect_case_mismatch(x, y)

Arguments

x

A character vector (keys from left table).

y

A character vector (keys from right table).

Value

A list with:

has_issues

Logical indicating if case mismatches found

mismatches

Data frame of key pairs that differ only by case

Detect Empty Strings in Keys

Description

Checks for empty strings which behave differently from NA in joins.

Usage

.detect_empty_strings(x)

Arguments

x

A character vector to check.

Value

A list with empty string detection results.

Detect Encoding Issues in Keys

Description

Checks for encoding inconsistencies and invisible Unicode characters.

Usage

.detect_encoding_issues(x)

Arguments

x

A character vector to check.

Value

A list with:

has_issues

Logical indicating if encoding issues found

mixed_encoding

Logical if multiple encodings detected

invisible_chars

Indices of values with invisible Unicode

affected_values

Values with encoding issues

Detect Factor Level Mismatches

Description

Checks if factor columns have mismatched levels.

Usage

.detect_factor_mismatch(x_col, y_col, x_name, y_name)

Arguments

x_col

Factor column from left table.

y_col

Factor column from right table.

x_name

Name of left column.

y_name

Name of right column.

Value

A list with factor mismatch information.

Detect Near-Matches Between Keys

Description

Finds keys that almost match (small edit distance).

Usage

.detect_near_matches(x_keys, y_keys, max_distance = 2, max_candidates = 10)

Arguments

x_keys

Unique keys from left table.

y_keys

Unique keys from right table.

max_distance

Maximum 'Levenshtein' distance to consider a near-match.

max_candidates

Maximum number of near-match candidates to return.

Value

A list with near-match information.

Detect Numeric Precision Issues

Description

Checks for floating-point keys that may not match exactly.

Usage

.detect_numeric_precision(x)

Arguments

x

A numeric vector to check.

Value

A list with precision issue detection results.

Detect Type Mismatches Between Key Columns

Description

Checks if key columns have compatible types for joining.

Usage

.detect_type_mismatch(x_col, y_col, x_name, y_name)

Arguments

x_col

Column from left table.

y_col

Column from right table.

x_name

Name of left column.

y_name

Name of right column.

Value

A list with type mismatch information.

Detect Whitespace Issues in Keys

Description

Checks for leading or trailing whitespace in character vectors.

Usage

.detect_whitespace(x)

Arguments

x

A character vector to check.

Value

A list with:

has_issues

Logical indicating if whitespace issues found

leading

Indices of values with leading whitespace

trailing

Indices of values with trailing whitespace

affected_values

Unique values with whitespace issues

Format Bytes as Human-Readable String

Description

Format Bytes as Human-Readable String

Usage

.format_bytes(bytes)

Arguments

bytes

Numeric bytes.

Value

Character string.

Internal join wrapper helper

Description

Internal join wrapper helper

Usage

.join_spy_impl(x, y, by, type, verbose, .quiet = FALSE, ...)

Arguments

x

Left data frame.

y

Right data frame.

by

Column names.

type

Join type.

verbose

Print output.

.quiet

Suppress all output (overrides verbose).

...

Additional args to merge.

Value

Joined data frame with report attribute.

Calculate 'Levenshtein' Distance

Description

Simple implementation for detecting near-matches.

Usage

.levenshtein(s1, s2)

Arguments

s1

First string.

s2

Second string.

Value

Integer distance.

Predict Row Counts for Different Join Types

Description

Estimates the number of rows that will result from different join types.

Usage

.predict_row_counts(x, y, by)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

Column names to join by.

Value

A list with predicted row counts.

Convert JoinReport to list for serialization

Description

Convert JoinReport to list for serialization

Usage

.report_to_list(report, timestamp = TRUE)

Arguments

report

A JoinReport object.

timestamp

Include timestamp.

Value

List representation.

Convert JoinReport to text

Description

Convert JoinReport to text

Usage

.report_to_text(report, timestamp = TRUE)

Arguments

report

A JoinReport object.

timestamp

Include timestamp.

Value

Character string.

Summarize Key Column

Description

Creates a summary of a key column or composite key.

Usage

.summarize_keys(data, by)

Arguments

data

A data frame.

by

Column name(s) to summarize.

Value

A list with summary statistics.

Simple JSON serialization (no dependencies)

Description

Simple JSON serialization (no dependencies)

Usage

.to_json(x)

Arguments

x

List to convert.

Value

JSON string.

'RStudio' Addin: Join Inspector

Description

Opens an interactive dialog to explore join diagnostics between two data frames in the current R environment.

Usage

addin_join_inspector()

Value

Called for side effects (opens Shiny gadget).

Analyze Multi-Table Join Chain

Description

Analyzes a sequence of joins to identify potential issues in the chain. Useful for debugging complex multi-table joins.

Usage

analyze_join_chain(tables, joins)

Arguments

tables

A named list of data frames to join.

joins

A list of join specifications, each with elements:

left

Name of left table

right

Name of right table

by

Join column(s)

Value

A summary of the join chain analysis.

See Also

join_spy(), check_cartesian()

Examples

orders <- data.frame(order_id = 1:3, customer_id = c(1, 2, 2))
customers <- data.frame(customer_id = 1:3, region_id = c(1, 1, 2))
regions <- data.frame(region_id = 1:2, name = c("North", "South"))

analyze_join_chain(
  tables = list(orders = orders, customers = customers, regions = regions),
  joins = list(
    list(left = "orders", right = "customers", by = "customer_id"),
    list(left = "result", right = "regions", by = "region_id")
  )
)

Detect Potential Cartesian Product

Description

Warns if a join will produce a very large result due to many-to-many relationships (Cartesian product explosion).

Usage

check_cartesian(x, y, by, threshold = 10)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

Column names to join by.

threshold

Warn if result will exceed this many times the larger input. Default 10.

Value

A list with explosion analysis.

See Also

join_spy(), join_strict()

Examples

# Dangerous: both tables have duplicates
x <- data.frame(id = c(1, 1, 2, 2), val_x = 1:4)
y <- data.frame(id = c(1, 1, 2, 2), val_y = 1:4)

check_cartesian(x, y, by = "id")

Detect Join Relationship Type

Description

Determines the actual cardinality relationship between two tables.

Usage

detect_cardinality(x, y, by)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

Column names to join by.

Value

Character string: "1:1", "1:m", "m:1", or "m:m".

See Also

join_strict(), join_spy()

Examples

# 1:1 relationship
x <- data.frame(id = 1:3, val = 1:3)
y <- data.frame(id = 1:3, name = c("A", "B", "C"))
detect_cardinality(x, y, "id")

# 1:m relationship
x <- data.frame(id = 1:3, val = 1:3)
y <- data.frame(id = c(1, 1, 2, 3), name = c("A1", "A2", "B", "C"))
detect_cardinality(x, y, "id")

Full Join with Diagnostics

Description

Performs a full join and automatically prints diagnostic information.

Usage

full_join_spy(x, y, by, verbose = TRUE, .quiet = FALSE, ...)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

A character vector of column names to join by.

verbose

Logical. If TRUE (default), prints diagnostic summary.

.quiet

Logical. If TRUE, suppresses all output (overrides verbose). Useful for silent pipeline operations. Use last_report() to access the diagnostics afterward.

...

Additional arguments passed to the underlying join function.

Value

The joined data frame with a "join_report" attribute.

See Also

left_join_spy(), join_spy(), last_report()

Get Current Log File

Description

Returns the current automatic log file path, if set.

Usage

get_log_file()

Value

The log file path, or NULL if not set.

See Also

set_log_file()

Inner Join with Diagnostics

Description

Performs an inner join and automatically prints diagnostic information.

Usage

inner_join_spy(x, y, by, verbose = TRUE, .quiet = FALSE, ...)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

A character vector of column names to join by.

verbose

Logical. If TRUE (default), prints diagnostic summary.

.quiet

Logical. If TRUE, suppresses all output (overrides verbose). Useful for silent pipeline operations. Use last_report() to access the diagnostics afterward.

...

Additional arguments passed to the underlying join function.

Value

The joined data frame with a "join_report" attribute.

See Also

left_join_spy(), join_spy(), last_report()

Check if Object is a JoinReport

Description

Check if Object is a JoinReport

Usage

is_join_report(x)

Arguments

x

An object to test.

Value

TRUE if x is a JoinReport, FALSE otherwise.

Compare Data Frame Before and After Join

Description

Shows a side-by-side comparison of key statistics before and after a join operation.

Usage

join_diff(before, after, by = NULL)

Arguments

before

The original data frame (before joining).

after

The result data frame (after joining).

by

Optional. Column names to analyze for key statistics.

Value

Invisibly returns a comparison summary. Prints a formatted comparison.

See Also

join_explain(), join_spy()

Examples

before <- data.frame(id = 1:3, x = letters[1:3])
after <- data.frame(id = c(1, 2, 2, 3), x = c("a", "b", "b", "c"), y = 1:4
)

join_diff(before, after)

Explain Row Count Changes After a Join

Description

After performing a join, use this function to understand why the row count changed. It analyzes the original tables and the result to explain the difference.

Usage

join_explain(result, x, y, by, type = NULL)

Arguments

result

The result of a join operation.

x

The original left data frame.

y

The original right data frame.

by

A character vector of column names used in the join.

type

Character. The type of join that was performed. One of "left", "right", "inner", "full". If NULL (default), attempts to infer the join type from row counts.

Value

Invisibly returns a list with explanation details. Prints a human-readable explanation.

See Also

join_spy(), join_diff()

Examples

orders <- data.frame(id = c(1, 2, 2, 3), value = 1:4)
customers <- data.frame(id = c(1, 2, 2, 4), name = c("A", "B1", "B2", "D"))

result <- merge(orders, customers, by = "id", all.x = TRUE)

# Explain why we got more rows than expected
join_explain(result, orders, customers, by = "id", type = "left")

Repair Common Key Issues

Description

Automatically fixes trivial join key issues like whitespace and case mismatches. Returns the repaired data frame(s) with a summary of changes.

Usage

join_repair(
  x,
  y = NULL,
  by,
  trim_whitespace = TRUE,
  standardize_case = NULL,
  remove_invisible = TRUE,
  empty_to_na = FALSE,
  dry_run = FALSE
)

Arguments

x

A data frame (left table).

y

A data frame (right table). If NULL, only repairs x.

by

A character vector of column names to repair.

trim_whitespace

Logical. Trim leading/trailing whitespace. Default TRUE.

standardize_case

Character. Standardize case to "lower", "upper", or NULL (no change). Default NULL.

remove_invisible

Logical. Remove invisible Unicode characters. Default TRUE.

empty_to_na

Logical. Convert empty strings to NA. Default FALSE.

dry_run

Logical. If TRUE, only report what would be changed without modifying data. Default FALSE.

Value

If y is NULL, returns the repaired x. If both are provided, returns a list with x and y. In dry_run mode, returns a summary of proposed changes.

See Also

join_spy(), key_check()

Examples

# Data with whitespace issues
orders <- data.frame(
  id = c(" A", "B ", "C"),
  value = 1:3,
  stringsAsFactors = FALSE
)

# Dry run to see what would change
join_repair(orders, by = "id", dry_run = TRUE)

# Actually repair
orders_fixed <- join_repair(orders, by = "id")

Comprehensive Pre-Join Diagnostic Report

Description

Analyzes two data frames before joining to detect potential issues and predict the outcome. Returns a detailed report of key quality, match rates, and detected problems.

Usage

join_spy(x, y, by, sample = NULL, ...)

Arguments

x

A data frame (left table in the join).

y

A data frame (right table in the join).

by

A character vector of column names to join by, or a named character vector for joins where column names differ (e.g., c("id" = "customer_id")).

sample

Integer or NULL. If provided, randomly sample this many rows from each table for faster diagnostics on large datasets. Default NULL (analyze all rows).

...

Reserved for future use.

Details

This function detects the following common join issues:

Value

A JoinReport object with the following components:

x_summary

Summary statistics for keys in the left table

y_summary

Summary statistics for keys in the right table

match_analysis

Details of which keys will/won't match

issues

List of detected problems (duplicates, whitespace, etc.)

expected_rows

Predicted row counts for each join type

See Also

key_check(), join_explain(), join_strict()

Examples

# Create sample data with issues
orders <- data.frame(
  order_id = 1:5,
  customer_id = c("A", "B", "B", "C", "D ")
)
customers <- data.frame(
  customer_id = c("A", "B", "C", "E"),
  name = c("Alice", "Bob", "Carol", "Eve")
)

# Get diagnostic report
join_spy(orders, customers, by = "customer_id")

Strict Join with Cardinality Enforcement

Description

Performs a join operation that fails if the specified cardinality constraint is violated. Use this to catch unexpected many-to-many relationships early.

Usage

join_strict(
  x,
  y,
  by,
  type = c("left", "right", "inner", "full"),
  expect = c("1:1", "1:m", "1:many", "m:1", "many:1", "m:m", "many:many"),
  ...
)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

A character vector of column names to join by.

type

Character. The type of join to perform. One of "left" (default), "right", "inner", "full".

expect

Character. The expected cardinality relationship. One of:

"1:1"

Each key in x matches at most one key in y, and vice versa

"1:m" or "1:many"

Each key in x can match multiple keys in y, but each key in y matches at most one key in x

"m:1" or "many:1"

Each key in y can match multiple keys in x, but each key in x matches at most one key in y

"m:m" or "many:many"

No cardinality constraints (allows all relationships)

...

Additional arguments passed to the underlying join function.

Value

The joined data frame if the cardinality constraint is satisfied. Throws an error if the constraint is violated.

See Also

join_spy(), left_join_spy()

Examples

orders <- data.frame(id = 1:3, product = c("A", "B", "C"))
customers <- data.frame(id = 1:3, name = c("Alice", "Bob", "Carol"))

# This succeeds (1:1 relationship)
join_strict(orders, customers, by = "id", expect = "1:1")

# This fails if customers had duplicate ids (wrapped in try to show error)
customers_dup <- data.frame(id = c(1, 1, 2), name = c("A1", "A2", "B"))
try(join_strict(orders, customers_dup, by = "id", expect = "1:1"))

Quick Key Quality Check

Description

A fast check of join key quality that returns a simple pass/fail status with a brief summary. Use this for quick validation; use join_spy() for detailed diagnostics.

Usage

key_check(x, y, by, warn = TRUE)

Arguments

x

A data frame (left table in the join).

y

A data frame (right table in the join).

by

A character vector of column names to join by.

warn

Logical. If TRUE (default), prints warnings for detected issues. Set to FALSE for silent operation.

Value

Invisibly returns a logical: TRUE if no issues detected, FALSE otherwise. Also prints a brief status message unless warn = FALSE.

See Also

join_spy(), key_duplicates()

Examples

orders <- data.frame(id = c(1, 2, 2, 3), value = 1:4)
customers <- data.frame(id = c(1, 2, 4), name = c("A", "B", "D"))

# Quick check
key_check(orders, customers, by = "id")

# Silent check
is_ok <- key_check(orders, customers, by = "id", warn = FALSE)

Find Duplicate Keys

Description

Identifies rows with duplicate values in the specified key columns. Returns a data frame containing only the rows with duplicated keys, along with a count of occurrences.

Usage

key_duplicates(data, by, keep = c("all", "first", "last"))

Arguments

data

A data frame.

by

A character vector of column names to check for duplicates.

keep

Character. Which duplicates to return:

"all"

Return all rows with duplicated keys (default)

"first"

Return only the first occurrence of each duplicate

"last"

Return only the last occurrence of each duplicate

Value

A data frame containing the duplicated rows, with an additional column .n_duplicates showing how many times each key appears. Returns an empty data frame (0 rows) if no duplicates found.

See Also

key_check(), join_spy()

Examples

df <- data.frame(
  id = c(1, 2, 2, 3, 3, 3, 4),
  value = letters[1:7]
)

# Find all duplicates
key_duplicates(df, by = "id")

# Find first occurrence only
key_duplicates(df, by = "id", keep = "first")

Get the Last Join Report

Description

Retrieves the most recent JoinReport object from any ⁠*_join_spy()⁠ call. Useful when using .quiet = TRUE in pipelines and wanting to inspect the diagnostics afterward.

Usage

last_report()

Value

The last JoinReport object, or NULL if no join has been performed.

See Also

left_join_spy(), join_spy()

Examples

orders <- data.frame(id = 1:3, value = c(10, 20, 30))
customers <- data.frame(id = c(1, 2, 4), name = c("A", "B", "D"))

# Silent join in a pipeline
result <- left_join_spy(orders, customers, by = "id", .quiet = TRUE)

# Inspect the report afterward
last_report()

Left Join with Diagnostics

Description

Performs a left join and automatically prints diagnostic information about the operation. The diagnostic report is also attached as an attribute.

Usage

left_join_spy(x, y, by, verbose = TRUE, .quiet = FALSE, ...)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

A character vector of column names to join by.

verbose

Logical. If TRUE (default), prints diagnostic summary.

.quiet

Logical. If TRUE, suppresses all output (overrides verbose). Useful for silent pipeline operations. Use last_report() to access the diagnostics afterward.

...

Additional arguments passed to the underlying join function.

Value

The joined data frame with a "join_report" attribute containing the diagnostic information.

See Also

join_spy(), join_strict(), last_report()

Examples

orders <- data.frame(id = 1:3, value = c(10, 20, 30))
customers <- data.frame(id = c(1, 2, 4), name = c("A", "B", "D"))

result <- left_join_spy(orders, customers, by = "id")

# Access the diagnostic report
attr(result, "join_report")

# Silent mode for pipelines
result2 <- left_join_spy(orders, customers, by = "id", .quiet = TRUE)
last_report()  # Access diagnostics afterward

Log Join Report to File

Description

Writes a JoinReport object to a file for audit trails and reproducibility. Supports plain text, JSON, and RDS formats.

Usage

log_report(report, file, append = FALSE, timestamp = TRUE)

Arguments

report

A JoinReport object from join_spy() or retrieved via last_report().

file

File path to write to. Extension determines format:

  • .txt or .log: Plain text (human-readable)

  • .json: JSON format (machine-readable)

  • .rds: R binary format (preserves all data)

append

Logical. If TRUE, appends to existing file (text/log only). Default FALSE.

timestamp

Logical. If TRUE (default), includes timestamp in output.

Value

Invisibly returns the file path.

See Also

join_spy(), last_report()

Examples

orders <- data.frame(id = 1:3, value = c(10, 20, 30))
customers <- data.frame(id = c(1, 2, 4), name = c("A", "B", "D"))

report <- join_spy(orders, customers, by = "id")

# Log to temporary file
tmp <- tempfile(fileext = ".log")
log_report(report, tmp, append = TRUE)
unlink(tmp)

Create a JoinReport Object

Description

Create a JoinReport Object

Usage

new_join_report(
  x_summary,
  y_summary,
  match_analysis,
  issues,
  expected_rows,
  by
)

Arguments

x_summary

Summary statistics for keys in the left table.

y_summary

Summary statistics for keys in the right table.

match_analysis

Details of which keys will/won't match.

issues

List of detected problems.

expected_rows

Predicted row counts for each join type.

by

The columns used for joining.

Value

A JoinReport S3 object.

Plot Method for JoinReport

Description

Creates a Venn diagram showing key overlap between tables.

Usage

## S3 method for class 'JoinReport'
plot(
  x,
  file = NULL,
  width = 6,
  height = 5,
  colors = c("#4A90D9", "#D94A4A"),
  ...
)

Arguments

x

A JoinReport object.

file

Optional file path to save the plot (PNG, SVG, or PDF based on extension). If NULL (default), displays in the current graphics device.

width

Width in inches (default 6).

height

Height in inches (default 5).

colors

Character vector of length 2 for left and right circle colors.

...

Additional arguments (ignored).

Value

Invisibly returns the plot data (left_only, both, right_only counts).

Examples

orders <- data.frame(id = 1:5, val = 1:5)
customers <- data.frame(id = 3:7, name = letters[3:7])

report <- join_spy(orders, customers, by = "id")
plot(report)

Print Method for JoinReport

Description

Print Method for JoinReport

Usage

## S3 method for class 'JoinReport'
print(x, ...)

Arguments

x

A JoinReport object.

...

Additional arguments (ignored).

Value

Invisibly returns x.

Right Join with Diagnostics

Description

Performs a right join and automatically prints diagnostic information.

Usage

right_join_spy(x, y, by, verbose = TRUE, .quiet = FALSE, ...)

Arguments

x

A data frame (left table).

y

A data frame (right table).

by

A character vector of column names to join by.

verbose

Logical. If TRUE (default), prints diagnostic summary.

.quiet

Logical. If TRUE, suppresses all output (overrides verbose). Useful for silent pipeline operations. Use last_report() to access the diagnostics afterward.

...

Additional arguments passed to the underlying join function.

Value

The joined data frame with a "join_report" attribute.

See Also

left_join_spy(), join_spy(), last_report()

Configure Automatic Logging

Description

Sets up automatic logging of all join reports to a specified file. When enabled, every ⁠*_join_spy()⁠ call will append its report to the log.

Usage

set_log_file(file, format = c("text", "json"))

Arguments

file

File path for automatic logging. Set to NULL to disable.

format

Log format: "text" (default) or "json".

Value

Invisibly returns the previous log file setting.

See Also

log_report(), get_log_file()

Examples

# Enable automatic logging to temp file
tmp <- tempfile(fileext = ".log")
old <- set_log_file(tmp)

# Disable logging and clean up
set_log_file(NULL)
unlink(tmp)

Suggest Repair Code

Description

Analyzes join issues and returns R code snippets to fix them.

Usage

suggest_repairs(report)

Arguments

report

A JoinReport object from join_spy().

Value

Character vector of R code snippets to fix detected issues.

See Also

join_repair(), join_spy()

Examples

orders <- data.frame(id = c("A ", "B"), val = 1:2, stringsAsFactors = FALSE)
customers <- data.frame(id = c("a", "b"), name = c("Alice", "Bob"), stringsAsFactors = FALSE)

report <- join_spy(orders, customers, by = "id")
suggest_repairs(report)

Summary Method for JoinReport

Description

Returns a compact summary data frame of the join diagnostic report.

Usage

## S3 method for class 'JoinReport'
summary(object, format = c("data.frame", "text", "markdown"), ...)

Arguments

object

A JoinReport object.

format

Output format: "data.frame" (default), "text", or "markdown".

...

Additional arguments (ignored).

Value

A data frame with key metrics (or printed output for text/markdown).

Examples

orders <- data.frame(id = 1:5, val = 1:5)
customers <- data.frame(id = 3:7, name = letters[3:7])

report <- join_spy(orders, customers, by = "id")
summary(report)
summary(report, format = "markdown")