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If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data

R: countrycode

countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page

Problem

Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.

Solution

The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.

Supported country codes

Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.

Extra arguments

Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.

Installation

From the R console, type install.packages("countrycode")

Examples

Load library:

> library(countrycode)

Convert single country codes:

> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"

Convert a vector of country codes

>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"

Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):

>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))

Inspect the data:

>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026

Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:

>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES

About

R package: Convert country names and country codes. Assigns region descriptors.

cran.r-project.org/web/packages/countrycode/index.html

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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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, '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");
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Contributions
If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Contributions
If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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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); }
})();
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Contributions
If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Contributions
If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
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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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Repository files navigation
Contributions
If you would like to improve the country code dictionary, please modify the file held in this other repository: https://github.com/vincentarelbundock/pycountrycode/tree/master/countrycode/data
R: countrycode
countrycode standardizes country names, converts them into one of seven coding schemes, assigns region descriptors, and generates empty dyadic or country-year dataframes from the coding schemes. Scroll down for more details or visit the countrycode CRAN page
Problem
Different data sources use different coding schemes to represent countries (e.g. CoW or ISO). This poses two main problems: (1) some of these coding schemes are less than intuitive, and (2) merging these data requires converting from one coding scheme to another, or from long country names to a coding scheme.
Solution
The countrycode function can convert to and from 7 different country coding schemes. It uses regular expressions to convert long country names (e.g. Sri Lanka) into any of those coding schemes, or into standardized country names (official short English). It can create new variables with the name of the continent and/or region to which each country belongs.
Supported country codes
Correlates of War character, CoW-numeric, ISO3-character, ISO3-numeric, ISO2-character, IMF, Food and Agriculture Organization of the United Nations, International Olympic Committee, United Nations numeric, FIPS 10-4, official English short country names (ISO), continent, region.
Extra arguments
Use warn=TRUE to print out a list of source elements for which no match was found. When the source vector are long country names that need to be matched using regular expressions, there is always a risk that multiple regex will match a given string. When this is the case, countrycode assigns a value arbitrarily, but the warn argument allows the user to print a list of all strings that were matched many times.
Installation
From the R console, type install.packages("countrycode")
Examples
Load library:
> library(countrycode)
Convert single country codes:
> countrycode(232,"cown","country.name")
[1] "ANDORRA"> countrycode("United States","country.name","iso3c")
[1] "USA"> countrycode("DZA","iso3c","cowc")
[1] "ALG"
Convert a vector of country codes
>cowcodes<- c("ALG","ALB","UKG","CAN","USA")
> countrycode(cowcodes,"cowc","iso3c")
[1] "DZA""ALB""GBR""CAN""USA"
Generate vectors and 2 data frames without a common id (i.e. can't merge the 2 df):
>isocodes<- c(12,8,826,124,840)
>var1<- sample(1:500,5)
>var2<- sample(1:500,5)
>df1<- as.data.frame(cbind(cowcodes,var1))
>df2<- as.data.frame(cbind(isocodes,var2))
Inspect the data:
>df1cowcodesvar11ALG712ALB4273UKG1804CAN215USA383>df2isocodesvar21122382832938264634124437584026
Create a common variable with the iso3c code in each data frame, merge the data, and create a country identifier:
>df1$iso3c<- countrycode(df1$cowcodes, "cowc", "iso3c")
>df2$iso3c<- countrycode(df2$isocodes, "iso3n", "iso3c")
>df3<- merge(df1,df2,id="iso3c")
>df3$country<- countrycode(df3$iso3c, "iso3c", "country.name")
>df3iso3ccowcodesvar1isocodesvar2country1ALBALB1138245ALBANIA2CANCAN373124197CANADA3DZAALG25412295ALGERIA4GBRUKG35182657UNITEDKINGDOM5USAUSA24184085UNITEDSTATES
About
R package: Convert country names and country codes. Assigns region descriptors.
cran.r-project.org/web/packages/countrycode/index.html
Resources
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1 watching
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