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Introduction

linkrep provides an easy-to-use and extensible architecture for generating linkage quality reports in R. It simplifies the process of evaluating and reporting on the quality of data linkage.

This package aims to provide a comprehensive tool for data linkage analysts to assess the quality of their linkage processes, while also helping data providers and researchers understand linkage errors and evaluate potential biases.

Reports generated with linkrep can be further customized to fit your specific needs:

  • Add new elements: Include additional tables, figures, sections or data as required.

  • Modify content: Customize written portions, including the Methods section, to better reflect your specific linkage processes.

  • Personalize appearance: Adjust the report’s background, layout, styles, and references to tailor the report to your needs.

Installation

R Studio Installation

To install linkrep from GitHub, begin by installing and loading the devtools package:

# install.packages("devtools")
library(devtools)

Afterwards, you may install the automated data linkage package using install_github():

devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()

Local Installation

To install linkrep locally from GitHub, select the most recent release from the right-hand tab on the GitHub repository page. Download the Source code (zip) file, then move over to RStudio. You may then run the code:

path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()

Main Report Elements

Summary: Overview of the Methods section and results

How to Read This Report: Provides recommendations on how to interpret the tables and figures to assess for potential biases.

Linkage Rate Summary: Includes the linkage rate table which stratifies linkage rates by sociodemographic factors and other characteristics.

Linkage Algorithm Summary: If provided, includes tables and figures describing the linkage algorithm and its quality.

Performance Metrics: If provided, includes multiple performance metrics (0-100) listed in table, visualized using a radar chart.

Background: Describes record linkage, how it’s performed, and its limitations.

Methods: Details the linkage process, including pre-processing and techniques used.

Appendix: Algorithms that were considered for testing, along with their performance metrics, can be included at the end of the report.

Additional Information & Documentation

For detailed instructions on formatting data for the report and customizing features, refer to the User Documentation

For examples of reports that can be generated using the linkrep package, download and view the sample Final Report and Sensitivity Analysis Report which uses fake/synthetic data to better help showcase the elements that make up each report. The reports generated here use two synthetic datasets, both of which can be downloaded, with the Left Dataset containing 150 records that have a corresponding link to one of the 250 records in the Right Dataset.

Authors

About

A fully customizable tool for generating comprehensive data linkage quality reports.

Topics

data-linkagequartorr-package

Resources

Readme
MIT license
Activity
Custom properties

Stars

2 stars

Watchers

0 watching

Forks

1 fork
Report repository

Releases

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Introduction
linkrep provides an easy-to-use and extensible architecture for
generating linkage quality reports in R. It simplifies the process of
evaluating and reporting on the quality of data linkage.
This package aims to provide a comprehensive tool for data linkage
analysts to assess the quality of their linkage processes, while also
helping data providers and researchers understand linkage errors and
evaluate potential biases.
Reports generated with linkrep can be further customized to fit your
specific needs:
  • Add new elements: Include additional tables, figures, sections or
data as required.
  • Modify content: Customize written portions, including the Methods
section, to better reflect your specific linkage processes.
  • Personalize appearance: Adjust the report’s background, layout,
styles, and references to tailor the report to your needs.
Installation
R Studio Installation
To install linkrep from GitHub, begin by installing and loading the
devtools package:
# install.packages("devtools")
library(devtools)
Afterwards, you may install the automated data linkage package using
install_github():
devtools::install_github("CHIMB/linkrep")
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Local Installation
To install linkrep locally from GitHub, select the most recent release
from the right-hand tab on the GitHub repository page. Download the
Source code (zip) file, then move over to RStudio. You may then
run the code:
path_to_pkg<- file.choose() # Select the unmodified package you downloaded from GitHub.devtools::install_local(path_to_pkg)
# You may need to install tinytex:# install.packages("tinytex")# tinytex::install_tinytex()
Main Report Elements
Summary: Overview of the Methods section and results
How to Read This Report: Provides recommendations on how to
interpret the tables and figures to assess for potential biases.
Linkage Rate Summary: Includes the linkage rate table which
stratifies linkage rates by sociodemographic factors and other
characteristics.
Linkage Algorithm Summary: If provided, includes tables and figures
describing the linkage algorithm and its quality.
Performance Metrics: If provided, includes multiple performance
metrics (0-100) listed in table, visualized using a radar chart.
Background: Describes record linkage, how it’s performed, and its
limitations.
Methods: Details the linkage process, including pre-processing and
techniques used.
Appendix: Algorithms that were considered for testing, along with
their performance metrics, can be included at the end of the report.
Additional Information & Documentation
For detailed instructions on formatting data for the report and
customizing features, refer to the User
Documentation
For examples of reports that can be generated using the linkrep
package, download and view the sample Final
Report
and Sensitivity Analysis
Report
which uses fake/synthetic data to better help showcase the elements that
make up each report. The reports generated here use two synthetic
datasets, both of which can be downloaded, with the Left
Dataset
containing 150 records that have a corresponding link to one of the 250
records in the Right
Dataset.
Authors
About
A fully customizable tool for generating comprehensive data linkage quality reports.
Topics
data-linkagequartorr-package
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
0 watching
Forks
1 fork
Report repository
Releases
Contributors
Languages