我正在清理和规范化导入的数据。我需要在许多列中grep许多模式，然后使用＆＃34; clean＆＃34;更新列。初始列旁边的grepped模式。
目标文本是df（这只是实际列和行的子集）：
name street_1 street_2 city
217916 Australia 49 Carbine Street Donvale, Melbourne
197423 C/Guatemala 3, Local 11 Balearic Island
205195 In Haiti: #30 Carrefour Digicel Port Salut
Grep模式是247个国家/地区。最终结果应如下所示（为了演示而省略上面的列）：
country
217916 Australia
197423 Guatemala
205195 Haiti
如果一行返回多个匹配，则应列出它们以供稍后处理，例如"Haiti, Australia"或应该是c("Haiti", "Australia")？
country
217916 c("Haiti", "Australia")
另外：当grep（使用管道分隔符）连接在一起时，国家/地区名称太长，所以我不得不求助于按国家/地区划分处理，但必须有更好的方法。
我提出的用于打算多列的代码是：
hits <- sapply(
colnames(df),
function(x) grep(
paste0(
"(?<![a-z])(?:",
paste(head(countries$name, 120), collapse = "|"),
")(?![a-z])"),
df[, x],
ignore.case = T,
perl = T))
results <- df[unique(unlist(hits)), sapply(hits, function(x) any(length(unlist(x)) > 0))]
这个子集df分为行和列，其中找到命中，但仍然远远不够。我没有使用边界模式\\b这个词，因为我想在边界中包含数字和下划线。
国家/地区名称：
structure(list(name = c("Afghanistan", "Aland Islands", "Albania",
"Algeria", "American Samoa", "Andorra", "Angola", "Anguilla",
"Antarctica", "Antigua and Barbuda", "Argentina", "Armenia",
"Aruba", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain",
"Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin",
"Bermuda", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana",
"Bouvet Island", "Brazil", "British Virgin Islands", "British Indian Ocean Territory",
"Brunei Darussalam", "Bulgaria", "Burkina Faso", "Burundi", "Cambodia",
"Cameroon", "Canada", "Cape Verde", "Cayman Islands", "Central African Republic",
"Chad", "Chile", "China", "Hong Kong, Special Administrative Region of China",
"Macao, Special Administrative Region of China", "Christmas Island",
"Cocos (Keeling) Islands", "Colombia", "Comoros", "Congo?(Brazzaville)",
"Congo, Democratic Republic of the", "Cook Islands", "Costa Rica",
"C?te d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czech Republic",
"Denmark", "Djibouti", "Dominica", "Dominican Republic", "Ecuador",
"Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia",
"Ethiopia", "Falkland Islands (Malvinas)", "Faroe Islands", "Fiji",
"Finland", "France", "French Guiana", "French Polynesia", "French Southern Territories",
"Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Gibraltar",
"Greece", "Greenland", "Grenada", "Guadeloupe", "Guam", "Guatemala",
"Guernsey", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Heard Island and Mcdonald Islands",
"Holy See?(Vatican City State)", "Honduras", "Hungary", "Iceland",
"India", "Indonesia", "Iran, Islamic Republic of", "Iraq", "Ireland",
"Isle of Man", "Israel", "Italy", "Jamaica", "Japan", "Jersey",
"Jordan", "Kazakhstan", "Kenya", "Kiribati", "Korea, Democratic People's Republic of",
"Korea, Republic of", "Kuwait", "Kyrgyzstan", "Lao PDR", "Latvia",
"Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", "Lithuania",
"Luxembourg", "Macedonia, Republic of", "Madagascar", "Malawi",
"Malaysia", "Maldives", "Mali", "Malta", "Marshall Islands",
"Martinique", "Mauritania", "Mauritius", "Mayotte", "Mexico",
"Micronesia, Federated States of", "Moldova", "Monaco", "Mongolia",
"Montenegro", "Montserrat", "Morocco", "Mozambique", "Myanmar",
"Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles",
"New Caledonia", "New Zealand", "Nicaragua", "Niger", "Nigeria",
"Niue", "Norfolk Island", "Northern Mariana Islands", "Norway",
"Oman", "Pakistan", "Palau", "Palestinian Territory, Occupied",
"Panama", "Papua New Guinea", "Paraguay", "Peru", "Philippines",
"Pitcairn", "Poland", "Portugal", "Puerto Rico", "Qatar", "R?union",
"Romania", "Russian Federation", "Rwanda", "Saint-Barth?lemy",
"Saint Helena", "Saint Kitts and Nevis", "Saint Lucia", "Saint-Martin (French part)",
"Saint Pierre and Miquelon", "Saint Vincent and Grenadines",
"Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia",
"Senegal", "Serbia", "Seychelles", "Sierra Leone", "Singapore",
"Slovakia", "Slovenia", "Solomon Islands", "Somalia", "South Africa",
"South Georgia and the South Sandwich Islands", "South Sudan",
"Spain", "Sri Lanka", "Sudan", "Suriname?*", "Svalbard and Jan Mayen Islands",
"Swaziland", "Sweden", "Switzerland", "Syrian Arab Republic?(Syria)",
"Taiwan, Republic of China", "Tajikistan", "Tanzania?*, United Republic of",
"Thailand", "Timor-Leste", "Togo", "Tokelau", "Tonga", "Trinidad and Tobago",
"Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands",
"Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom",
"United States of America", "United States Minor Outlying Islands",
"Uruguay", "Uzbekistan", "Vanuatu", "Venezuela?(Bolivarian Republic of)",
"Viet Nam", "Virgin Islands, US", "Wallis and Futuna Islands",
"Western Sahara", "Yemen", "Zambia", "Zimbabwe")), .Names = "name", class = "data.frame", row.names = c(NA,
-247L))
目标文本样本数据（50行/ 358626）：
structure(list(org_order = c(12444L, 33914L, 44556L, 44592L,
101963L, 109988L, 116027L, 116369L, 119774L, 129643L, 138317L,
144409L, 144409L, 145106L, 162134L, 163578L, 163782L, 165580L,
168297L, 169073L, 174144L, 182441L, 186903L, 187474L, 200151L,
202672L, 203554L, 203696L, 204983L, 205534L, 205669L, 206412L,
206998L, 209374L, 212087L, 214424L, 214549L, 214784L, 215966L,
217497L, 219141L, 219235L, 219611L, 220974L, 221249L, 221704L,
222197L, 226097L, 226987L, 228977L), organizations.api_path = c("organizations/tricked-dk",
"organizations/xdirect", "organizations/lincoln-dental-supply",
"organizations/scil-animal-care", "organizations/sportymob",
"organizations/fanflipped", "organizations/afribaba-holdings",
"organizations/rentokil", "organizations/counter-logic-gaming",
"organizations/predictive-analytics", "organizations/curatern",
"organizations/gotv-entertaining-africa", "organizations/gotv-entertaining-africa",
"organizations/fst-biometrics", "organizations/picons", "organizations/eye",
"organizations/inspiring-fifty", "organizations/fraudcracker",
"organizations/saev", "organizations/take-eat-easy", "organizations/cgiar-consortium",
"organizations/live-tile", "organizations/soko-insight", "organizations/leeds-beckett-university",
"organizations/guialocal", "organizations/wisboo", "organizations/edukwest-europe",
"organizations/demo-africa", "organizations/allafrica-global-media",
"organizations/code-week", "organizations/spotcast-inc", "organizations/synergy-social-ventures",
"organizations/lenoir-rhyne-college", "organizations/ceed-tech",
"organizations/netocratic", "organizations/balkan-venture-forum",
"organizations/adn-contents", "organizations/sarv-crm", "organizations/gooutmap",
"organizations/fi-ware", "organizations/dla-venture-pipeline",
"organizations/angels-on-time", "organizations/tangram-solutions",
"organizations/start-with-seven", "organizations/games-in-asia",
"organizations/openaxel", "organizations/z-safety-systems", "organizations/telkom-sa",
"organizations/cadigo", "organizations/filepreviews"), type = c("Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address", "Address", "Address", "Address", "Address", "Address",
"Address"), uuid = c("f5d931f41c976de95e8e19d98c9d1e71", "e4cdc0eab5370addf2cfcc2968b352ed",
"84bd97b962366ba892e9c9e21134c636", "f03a05df323aa842406e8a4652951876",
"52bb2fcc51b56d2a6f124d1084b46c31", "fb3e017f18fdd8e25d350c0acb787d7a",
"775a805b428c129b7e4a4cd724461868", "bee1141933033ac79da8eace50acafdc",
"44e55ce5044b6c1c8bc3d3aef7df87bf", "00e163d78e1809063778e4766a18bdfb",
"2a65aca53c8e39792b113464c681ea52", "41b8b2fd215158d9dcb49ba366b7a036",
"923e0fbdea2e3c1da9f67b721b65d8e4", "d999ff11eae271f9876ec4e3075e4125",
"e51603d3020a327edbfeec14f21cccc3", "9b9e58951d59ed2aec3ca0ab766116ed",
"15e5998b1e8c3d4345c85e76f3dac240", "a22b40a3305de557315758a94080aa76",
"b6332226dec612644868f7491a901f4a", "4a3f4401cc908be0a77185e7d3c9a206",
"73dbe3fda355a357dee29caf5aa4dc99", "e85678b7bf1085432c63600319012a10",
"7f5e79d588492491c012fbdb90122073", "f4e47d7927be2eacddfb960cee55dc6b",
"5c59be51096e8c0a94f87b1de3689d42", "38548540aa739607dec960129cf12ba9",
"952ab75de5ea8d85a44a8dcd432607bd", "e68ee31280619ce2f77b932f8daec571",
"4bb54989ab4497e319d036c89427d8cf", "ae09b50b190db4a183bda9eccbe4cfce",
"862eec31ebb44e73d0ece854ab314ce5", "6380fdab108b1fee616d847d7f392f6a",
"85d6bd776b725d934d0bff98b2c83cad", "f7e3911becbbeb4dfec0418ed5f2b39a",
"b5ef4f3d9660e23808ca0a0ed0e338bb", "da9861eded1a9a0e9bf6ba6a48aedf1e",
"76e1d4c1a1c56c5fe2b1c21c656a3a69", "f3f5becd3a1693efca41bcc1af1703f6",
"18ac74abae87d07ce3b11eb0dbd820a9", "e428d523943b2cc3aec47b72129b5fe6",
"36e696fed157afe1602ca8fda46fa8cc", "329fb82b86a66a9e656f3c552430ceca",
"b7d59665f2a8154e7359b9e290112460", "aa4ef592efedfd60816af1b8f46705dc",
"56cc4caadab46e6badfc8d6dee7d42e1", "17c659034260a6b393f566f0499f4178",
"e05c45df62761524de921aca8c489b44", "26e0ec01fc14f58419a5553bc2e0503a",
"3d3b5834d32f858b65ee07fae02252cf", "49e224e43d352755a51e543c9d036a96"
), name = c("Headquarter", "xDirect", "Headquarters", "Headquarters",
"Headquarters", "Main office", "Headquarters", "Headquarter",
"Main office", "Main office", "Headquaters", "Headquarters",
"Headquarters", "Headquarters", "HQ", "Headquarters", "HQ", "Headquarters",
"Aberdeen (UK)", "Branch Office", "Headquarters", "HQ", "Africa",
"Main Campus", "", "HQ", "HQ", "HQ", "Main office", "HQ", "Headquarters",
"HQ", "Main Campus", "Headquarters", "HQ", "Main office", "Headquarters",
"Headquarters", "Headquarters", "HQ", "", "HQ", "Main office",
"HQ", "HQ", "HQ", "HQ", "Headquarters", "", "HQ"), street_1 = c("",
"", "", "", "", "", "", "", "", "", "", "", "", "33 Lazarov St.",
"", "", "", "Building 3, Sandgate Office Park", "Abercrombie Court",
"", "1000, Avenue Agropolis Montpellier,", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "2 passage of Mulhouse City Hall",
"GOLGASHT STREET, NO. 8", "", "", "", "Russia, Europe", "", "Three Seasons Office Park, Block A, 7 Spring Street",
"", "", "Industrial Area Rotem Heulentakstraat 8", "178 Madiba Street Pretoria",
"", ""), street_2 = c("", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "16 Desmond Street, Kramerville Sandton",
"Arnhall Business Park", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "KDPSTY17489", "", "", "",
"", "", "Rivonia, Sandton, South Africa", "", "", "Dilsen-Stokkem",
"", "", ""), postal_code = c("", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "AB32 6FE", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "68100",
"19157", "", "", "", "", "", "", "", "", "", "1", "", ""), city = c("",
"", "", "", "", "", "", "", "", "", "", "", "", "Rishon Lezion",
"", "", "", "", "Aberdeen, UK and Oslo, Norway", "", "France",
"France", "", "", "Mex", "", "", "", "", "", "", "", "", "",
"", "", "France", "", "", "", "Usa", "", "", "", "", "", "",
"", "Usa", ""), city_web_path = c("location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/asia/b41404d28ecccaf512688fdd3afe73ee", "location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1",
"location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1", "location/africa/5b194c3b3018b47df611ab142cdfd74c",
"location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1", "location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1",
"location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/africa/5b194c3b3018b47df611ab142cdfd74c", "location/africa/5b194c3b3018b47df611ab142cdfd74c",
"", "location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/africa/5b194c3b3018b47df611ab142cdfd74c",
"", "location/europe/6106f5dc823e5da840d751612c0b2c4e", "", "",
"location/africa/5b194c3b3018b47df611ab142cdfd74c", "", "", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/africa/5b194c3b3018b47df611ab142cdfd74c",
"location/africa/5b194c3b3018b47df611ab142cdfd74c", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1", "location/asia/b41404d28ecccaf512688fdd3afe73ee",
"", "location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/europe/6106f5dc823e5da840d751612c0b2c4e", "", "location/africa/5b194c3b3018b47df611ab142cdfd74c",
"location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"", "location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1",
"location/africa/5b194c3b3018b47df611ab142cdfd74c", "location/asia/b41404d28ecccaf512688fdd3afe73ee",
"location/europe/6106f5dc823e5da840d751612c0b2c4e", "location/europe/6106f5dc823e5da840d751612c0b2c4e",
"location/africa/5b194c3b3018b47df611ab142cdfd74c", "", "location/north-america/b25caef9a1b83a5d623293b2dfb6a1d1"
), region = c("", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", ""), region_code2 = c("", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", ""), region_web_path = c("",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
""), country = c("", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", ""), country_code2 = c("", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", ""), country_code3 = c("",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
""), country_web_path = c("", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", ""), latitude = c("", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", ""),
longitude = c("", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", ""), created_at = c(1472017733L,
1465290491L, 1462452917L, 1462449522L, 1453173099L, 1442981619L,
1441282477L, 1441188785L, 1440063839L, 1436866575L, 1449038192L,
1431511632L, 1431511671L, 1385115787L, 1426216594L, 1451463126L,
1425892972L, 1425395355L, 1366007686L, 1429092875L, 1422609471L,
1419929819L, 1425486709L, 1397988672L, 1397646249L, 1412939086L,
1412685691L, 1412669552L, 1412165155L, 1412059260L, 1427182640L,
1411738828L, 1397988677L, 1426923750L, 1410082123L, 1412660190L,
1422885253L, 1409737973L, 1453226669L, 1408033946L, 1390890580L,
1407478355L, 1407390601L, 1407497667L, 1406899810L, 1408455996L,
1406606186L, 1405405821L, 1396892107L, 1406895938L), updated_at = c(1472017733L,
1465290491L, 1462452917L, 1462449523L, 1453173099L, 1442981619L,
1441282599L, 1441188785L, 1440063840L, 1436866576L, 1449038328L,
1431511633L, 1431511671L, 1431452774L, 1426216595L, 1451463126L,
1425892973L, 1425395356L, 1424818704L, 1429092914L, 1422646398L,
1419967132L, 1425486709L, 1423708863L, 1423516571L, 1412939087L,
1412685692L, 1412669553L, 1412165258L, 1412059261L, 1427182641L,
1411738828L, 1415300534L, 1426923751L, 1410082123L, 1412660191L,
1422902286L, 1409737975L, 1453226669L, 1408033946L, 1416861739L,
1407478355L, 1407390602L, 1407497668L, 1406899810L, 1408455996L,
1406606187L, 1409264446L, 1416858194L, 1406895938L)), .Names = c("org_order",
"organizations.api_path", "type", "uuid", "name", "street_1",
"street_2", "postal_code", "city", "city_web_path", "region",
"region_code2", "region_web_path", "country", "country_code2",
"country_code3", "country_web_path", "latitude", "longitude",
"created_at", "updated_at"), row.names = c(6712L, 23888L, 30537L,
30568L, 66291L, 70998L, 74766L, 74967L, 77009L, 83681L, 89607L,
93685L, 93686L, 94208L, 106079L, 107094L, 107242L, 108555L, 110439L,
110980L, 114553L, 120627L, 123869L, 124297L, 133799L, 135863L,
136578L, 136699L, 137802L, 138269L, 138377L, 138976L, 139449L,
141300L, 143473L, 145303L, 145391L, 145574L, 146477L, 147807L,
149247L, 149327L, 149650L, 150870L, 151106L, 151466L, 151892L,
155304L, 156151L, 157890L), class = "data.frame")
答案 0 :(得分：3)
我建议采用基于两个观察的方法：
setOnShowListener，"Hong Kong, Special Administrative Region of China"，"Korea, Republic of"等条目，当用作"Venezuela?(Bolivarian Republic of)"的模式时很少会产生匹配。需要事先清理列表或者需要部分匹配。 基本思路是将国家/地区名称和目标文本拆分为单词，找到匹配的单词（即国家/地区名称与目标文本的部分匹配），最后附加所有国家/地区与目标文本的适当行部分匹配的名称。
grep版本1.9.8用于出于性能原因（请运行data.table以验证您是否已安装CRAN的最新版本。）
packageVersion("data.table")library(data.table)
setDT(countries)
# split country names into words
countries <- countries[, stringr::str_split(name, boundary("word")), by = name]
print(countries)
# name V1
# 1: Afghanistan Afghanistan
# 2: Aland Islands Aland
# 3: Aland Islands Islands
# 4: Albania Albania
# 5: Algeria Algeria
# ---
#406: Western Sahara Western
#407: Western Sahara Sahara
#408: Yemen Yemen
#409: Zambia Zambia
#410: Zimbabwe Zimbabwe
# show top ten entries after split
countries[, .N, by = V1][order(-N)][1:10]
# V1 N
# 1: Islands 17
# 2: of 13
# 3: and 12
# 4: Republic 12
# 5: Saint 7
# 6: United 5
# 7: Island 4
# 8: China 4
# 9: Guinea 4
#10: French 4
# remove some unspecific entries
countries <- countries[!V1 %chin% c("and", "of")]
# keep original row names as unique identifier for each record, verify uniqueness
setDT(df, keep.rownames = TRUE)
stopifnot(!df[, anyDuplicated(rn)])
# reshape from wide to long form to have all target text in one column
mdf <- melt(df, id.vars = "rn")
# split target text into words
mdf <- mdf[, stringr::str_split(value, boundary("word")),
by = .(rn, variable, value)]
对于每个唯一的行ID，具有部分匹配的国家/地区名称将折叠在一个字符串中，该字符串可以附加到原始# right join of mdf with countries on words, all rows of mdf are kept
mw <- countries[mdf, on = .(V1)]
的相应行中。 df确保国家/地区名称仅在多个部分匹配的情况下才会显示一次（例如，unique()将在单词"South Africa"和"South"上有多个匹配项。
我决定使用斜杠"Africa"作为字符来分隔折叠的条目，因为当逗号"/"已经存在时，斜杠似乎不会在国家/地区名称中使用。
","# collapse country names which have a partial match
ccn <- mw[, .(pmcn = paste(sort(unique(name)), collapse = "/")), by = rn]
# append collapsed list of partially matching country names to original df by join on row id
result <- ccn[df, on = "rn"]
# re-order columns to have new column as last column
setcolorder(result, c(names(df), "pmcn"))
# show statistics on matches
result[, .N, by = pmcn]
# pmcn N
#1: 44
#2: Norway 1
#3: France 2
#4: South Africa 1
#5: France/Holy See?(Vatican City State) 1
#6: South Africa/South Georgia and the South Sandwich Islands/South Sudan 1
对于此行，我在# show one row where partially matching country names where found
result[pmcn != ""][1]
# rn org_order organizations.api_path type uuid
#1: 110439 168297 organizations/saev Address b6332226dec612644868f7491a901f4a
# name street_1 street_2 postal_code
#1: Aberdeen (UK) Abercrombie Court Arnhall Business Park AB32 6FE
# city city_web_path region region_code2 region_web_path
#1: Aberdeen, UK and Oslo, Norway
# country country_code2 country_code3 country_web_path latitude longitude created_at
#1: 1366007686
# updated_at pmcn
#1: 1424818704 Norway
列的Norway上找到了匹配项，但未在city列找到匹配项，因为国家/地区名称列表包含UK。
United Kingdom对于此行，result[pmcn != ""][5]
# rn org_order organizations.api_path type
#1: 145391 214549 organizations/adn-contents Address
# uuid name street_1
#1: 76e1d4c1a1c56c5fe2b1c21c656a3a69 Headquarters 2 passage of Mulhouse City Hall
# street_2 postal_code city city_web_path region region_code2 region_web_path
#1: 68100 France
# country country_code2 country_code3 country_web_path latitude longitude created_at
#1: 1422885253
# updated_at pmcn
#1: 1422902286 France/Holy See?(Vatican City State)
列中的France上找到了匹配项，city列中出现了Vatican City State的误报，因为City列中的street_1