├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── data.R ├── get_features.R └── stringmatch.R ├── README.md ├── data ├── m.RData └── train.RData ├── man ├── get_features.Rd └── stringmatch.Rd └── vignettes ├── afsm_amicus_crosson.R ├── interests_data.csv ├── orgs.RData └── static_scores_withints321.csv /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: stringmatch 2 | Type: Package 3 | Title: Implements the Adaptive Fuzzy String Matching model from Kaufman & Klevs 4 | Version: 1.0 5 | Date: 2021-05-19 6 | Authors@R: c( 7 | person("Aaron R.", "Kaufman", email = "aaronkaufman@nyu.edu", role = c("aut", "cre")), 8 | person("Aja", "Klevs", email = "ak7288@nyu.edu", role = c("aut"))) 9 | Maintainer: Aaron Kaufman 10 | Description: Uses a pretrained model to predict whether an arbitrary pair of strings refers to the same entity. 11 | Depends: 12 | R (>= 3.5.0) 13 | Imports: 14 | stringdist, 15 | ranger, 16 | magrittr, 17 | dplyr 18 | License: GPL (>= 2) 19 | RoxygenNote: 7.1.1 20 | LazyData: true 21 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | export(get_features) 2 | export(stringmatch) 3 | importFrom(stringdist, stringdist) 4 | importFrom(ranger, ranger) 5 | importFrom(magrittr, "%>%") 6 | importFrom(dplyr, select) 7 | importFrom(dplyr, filter) 8 | -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | #' Training data 2 | #' 3 | #' This is a data frame used to train our ranger model 4 | #' 5 | #' @name train 6 | #' @docType data 7 | #' @format A training data set of Amicus-cosigning orgs and Bonica's DIME orgs. 8 | #' @keywords dataset 9 | "train" 10 | 11 | #' Trained model 12 | #' 13 | #' A ranger trained model object 14 | #' 15 | #' @name m 16 | #' @docType data 17 | #' @format A ranger object 18 | #' @keywords dataset 19 | "m" -------------------------------------------------------------------------------- /R/get_features.R: -------------------------------------------------------------------------------- 1 | #' Generate string distance metrics for all combinations of two vectors 2 | #' 3 | #' @param string1 A character vector 4 | #' @param string2 A character vector 5 | #' @return A data frame of rows length(string1) * length(string2) with columns for each string distance metric 6 | #' @examples 7 | #' get_features(string1 = c("hello", "goodbye"), 8 | #' string2 = c("alpha", "beta")) 9 | 10 | 11 | get_features <- 12 | function(string1, string2 = NULL) { 13 | if (is.null(string2)) { 14 | tmp = combn(length(string1), 2) # positions for combinations of unordered pairs 15 | tmp = split(tmp, 1:2) # split rows of matrix 16 | tmp = as.data.frame(lapply(tmp, \(x) string1[x])) # get elements based on positions 17 | names(tmp) <- c("Var1", "Var2") # Rename for compatibility with rest of code 18 | } else { 19 | tmp = expand.grid(string1, string2) 20 | } 21 | tmp$osa = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "osa") 22 | tmp$cosine = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "cosine") 23 | tmp$jaccard = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "jaccard") 24 | tmp$jw = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "jw") 25 | tmp$lcs = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "lcs") 26 | tmp$qgram = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "qgram") 27 | tmp$soundex = stringdist::stringdist(tmp[, 1], tmp[, 2], method = "soundex") 28 | return(tmp) 29 | } 30 | -------------------------------------------------------------------------------- /R/stringmatch.R: -------------------------------------------------------------------------------- 1 | #' Generate a string distance score for all combinations of two vectors 2 | #' 3 | #' @param string1 A character vector 4 | #' @param string2 A character vector. If no second vector is provided, the first is compared to itself. 5 | #' @param trainingset If NULL, use the base model. Otherwise, a data frame with columns A, B, and y corresponding to string1, string2, and a training label 6 | #' @param feature.importances If TRUE, returns feature importances. 7 | #' @return A data frame of rows length(string1) * length(string2) with columns for each string distance metric as well as a model-produced score 8 | #' @examples 9 | #' stringmatch(string1 = c("hello", "goodbye"), 10 | #' string2 = c("alpha", "beta")) 11 | 12 | 13 | 14 | stringmatch <- 15 | function(string1, string2 = NULL, 16 | trainingset = NULL, 17 | feature.importances = FALSE){ 18 | data(train) 19 | data(m) 20 | if(!is.null(trainingset)){ 21 | ## set some warnings for type: make sure the columns are A,B,y 22 | if(!all(c("A", "B", "y") %in% colnames(trainingset))){ 23 | stop("The columns of trainingset must include 'A', 'B', and 'y'") 24 | } 25 | 26 | ## Calculate stringdist for new training set 27 | newtrain = get_features(trainingset$A, trainingset$B) 28 | colnames(newtrain)[1:2] = c("amicus", "bonica") 29 | 30 | ## Add in the truth vector 31 | hash = paste0(trainingset$A, trainingset$B) 32 | hash2 = paste0(newtrain$amicus, newtrain$bonica) 33 | newtrain$match = 0 34 | newtrain$match[hash2 %in% hash] = 1 35 | newtrain = newtrain[,colnames(train)] 36 | 37 | ## Append the new trainingset to the old one and generate the model 38 | train = rbind(train, newtrain) 39 | m = ranger::ranger(x = train %>% select(osa:soundex), 40 | y = factor(train$match), 41 | probability = TRUE) 42 | 43 | } 44 | 45 | ## Calculate stringdist for string1, string2 46 | feats = get_features(string1, string2) 47 | feats = feats %>% filter(!is.na(Var1),!is.na(Var2)) 48 | 49 | ## Run the prediction 50 | preds = predict(m, data = feats) 51 | 52 | feats$pred = preds$predictions[,2] 53 | 54 | ## Return feature importances too 55 | if(feature.importances){ 56 | out = list(feats, ranger::importance(m)) 57 | } else { 58 | out = list(feats) 59 | } 60 | 61 | ## Return a data set of predictions 62 | return(out) 63 | 64 | } 65 | 66 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # stringmatch 2 | Implements the Adaptive Fuzzy String Matching model from Kaufman & Klevs (2022), https://www.cambridge.org/core/journals/political-analysis/article/adaptive-fuzzy-string-matching-how-to-merge-datasets-with-only-one-messy-identifying-field/275D7890548359215AC728C1E35B53CE#article 3 | 4 | Please cite as: 5 | Kaufman, Aaron R., and Aja Klevs. "Adaptive Fuzzy String Matching: How to Merge Datasets with Only One (Messy) Identifying Field." Political Analysis 30.4 (2022): 590-596. 6 | 7 | If you use this library and develop your own training set, please let us know! We would love to add your training set to our library so others can benefit. You can reach out at aaronkaufman@nyu.edu. 8 | -------------------------------------------------------------------------------- /data/m.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronrkaufman/stringmatch/681d1071d1fe72953c12f62620d35e6d5c9c3c05/data/m.RData -------------------------------------------------------------------------------- /data/train.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronrkaufman/stringmatch/681d1071d1fe72953c12f62620d35e6d5c9c3c05/data/train.RData -------------------------------------------------------------------------------- /man/get_features.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/get_features.R 3 | \name{get_features} 4 | \alias{get_features} 5 | \title{Generate string distance metrics for all combinations of two vectors} 6 | \usage{ 7 | get_features(string1, string2) 8 | } 9 | \arguments{ 10 | \item{string1}{A character vector} 11 | 12 | \item{string2}{A character vector} 13 | } 14 | \value{ 15 | A data frame of rows length(string1) * length(string2) with columns for each string distance metric 16 | } 17 | \description{ 18 | Generate string distance metrics for all combinations of two vectors 19 | } 20 | \examples{ 21 | get_features(string1 = c("hello", "goodbye"), 22 | string2 = c("alpha", "beta")) 23 | } 24 | -------------------------------------------------------------------------------- /man/stringmatch.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stringmatch.R 3 | \name{stringmatch} 4 | \alias{stringmatch} 5 | \title{Generate a string distance score for all combinations of two vectors} 6 | \usage{ 7 | stringmatch(string1, string2, trainingset = NULL, feature.importances = FALSE) 8 | } 9 | \arguments{ 10 | \item{string1}{A character vector} 11 | 12 | \item{string2}{A character vector} 13 | 14 | \item{trainingset}{If NULL, use the base model. Otherwise, a data frame with columns A, B, and y corresponding to string1, string2, and a training label} 15 | 16 | \item{feature.importances}{If TRUE, returns feature importances.} 17 | } 18 | \value{ 19 | A data frame of rows length(string1) * length(string2) with columns for each string distance metric as well as a model-produced score 20 | } 21 | \description{ 22 | Generate a string distance score for all combinations of two vectors 23 | } 24 | \examples{ 25 | stringmatch(string1 = c("hello", "goodbye"), 26 | string2 = c("alpha", "beta")) 27 | } 28 | -------------------------------------------------------------------------------- /vignettes/afsm_amicus_crosson.R: -------------------------------------------------------------------------------- 1 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) 2 | library(data.table) 3 | library(tidyverse) 4 | 5 | set.seed(12345) 6 | 7 | ## Load amicus data 8 | load("orgs.RData") 9 | 10 | ## Load data from Crosson et al (2021) 11 | crosson = read.csv("static_scores_withints321.csv") %>% 12 | mutate(name = tolower(orgname_new)) %>% select(-org_index) %>% 13 | mutate(realorg = is.na(as.numeric(orgname_new))) %>% 14 | filter(realorg == TRUE) %>% select(-orgname_new, -realorg) 15 | 16 | ## check exact matches 17 | sum(crosson$name %in% out$name) # 866/2646 18 | 19 | dat = merge(crosson, out, by ="name") 20 | 21 | crosson2 = crosson[!crosson$name %in% out$name,] 22 | 23 | ## Load AFSM library 24 | source("stringmatch.R") 25 | source("get_features.R") 26 | load("train.rda") 27 | #load("m.rda") 28 | 29 | train2 = train %>% mutate(A = amicus, B = bonica, y = match) %>% 30 | select(A,B,y) 31 | 32 | ## Do some fuzzy string matching 33 | ## Either run this line or load m directly 34 | m = ranger::ranger(x = train %>% select(osa:soundex), 35 | y = factor(train$match), 36 | probability = TRUE) 37 | 38 | ## Start with 1:500 of string1 for computational tractability; 39 | ## Lower as necessary for your machine! 40 | sm1 = stringmatch(string1 = crosson2$name[1:500], string2 = out$name, 41 | feature.importances = FALSE) 42 | 43 | ## Check quality 44 | matches = sm1[[1]] 45 | matches = matches %>% arrange(desc(pred)) 46 | 47 | matches2 = matches %>% rename(amicus = Var1, bonica = Var2) 48 | matches2 = matches2[1:1000,] 49 | matches2 = matches2 %>% rename(match = pred) 50 | matches2$match = 0 51 | 52 | ## Here is where you scan through the top matches and list which ones are correct 53 | matches2$match[c(1,3,13,15,17,23,38,39,41,43,56,57,62,64,66,73,80, 54 | 82:83,97,99,105,147,162,182,199,209,219,238,247:249, 55 | 274,295,296,306,308:311,320:322,331,345,351,396, 56 | 475,524,589,608,642,660,662,663,665,669,670:674, 57 | 677,679,680,709,735:737,749:750,755,758,765,800,816, 58 | 908)] = 1 59 | 60 | ## Add those new matches to the training set 61 | matches3 = matches2 %>% 62 | filter(match==1)%>% 63 | select(amicus, bonica) %>% 64 | inner_join(crosson2, by=c("amicus" = "name")) %>% 65 | select(-amicus) %>% 66 | inner_join(out, by=c("bonica" = "name")) %>% 67 | mutate(name = bonica) %>% select(-bonica) 68 | 69 | dat2 = rbind(dat, matches3) 70 | 71 | ## Retrain the model and go again 72 | train = rbind(train, matches2) 73 | 74 | m = ranger::ranger(x = train %>% select(osa:soundex), 75 | y = factor(train$match), 76 | probability = TRUE) 77 | 78 | sm2 = stringmatch(string1 = crosson2$name[501:1000], string2 = out$name, 79 | feature.importances = FALSE) 80 | 81 | matches = sm2[[1]] 82 | matches = matches %>% arrange(desc(pred)) 83 | 84 | matches2 = matches %>% rename(amicus = Var1, bonica = Var2) 85 | matches2 = matches2[1:1000,] 86 | matches2 = matches2 %>% rename(match = pred) 87 | matches2$match = 0 88 | matches2$match[c(1:13,16,19,23,25,35,36,146)] = 1 89 | 90 | matches3 = matches2 %>% 91 | filter(match==1)%>% 92 | select(amicus, bonica) %>% 93 | inner_join(crosson2, by=c("amicus" = "name")) %>% 94 | select(-amicus) %>% 95 | inner_join(out, by=c("bonica" = "name")) %>% 96 | mutate(name = bonica) %>% select(-bonica) 97 | 98 | dat2 = rbind(dat2, matches3) 99 | 100 | ## One more iteration if needed 101 | train = rbind(train, matches2) 102 | 103 | m = ranger::ranger(x = train %>% select(osa:soundex), 104 | y = factor(train$match), 105 | probability = TRUE) 106 | 107 | sm3 = stringmatch(string1 = crosson2$name[1001:nrow(crosson)], string2 = out$name, 108 | feature.importances = FALSE) 109 | 110 | matches = sm3[[1]] 111 | matches = matches %>% arrange(desc(pred)) 112 | 113 | matches2 = matches %>% rename(amicus = Var1, bonica = Var2) 114 | matches2 = matches2[1:1000,] 115 | matches2 = matches2 %>% rename(match = pred) 116 | matches2$match = 0 117 | 118 | matches2$match[c(1:61,63,65,74,90,96,99,103,108,115,125,141)] = 1 119 | 120 | matches3 = matches2 %>% 121 | filter(match==1)%>% 122 | select(amicus, bonica) %>% 123 | inner_join(crosson2, by=c("amicus" = "name")) %>% 124 | select(-amicus) %>% 125 | inner_join(out, by=c("bonica" = "name")) %>% 126 | mutate(name = bonica) %>% select(-bonica) 127 | 128 | dat2 = rbind(dat2, matches3) 129 | 130 | 131 | cor(dat2$mean_score_new, dat2$crossover, use = "complete.obs") # 0.61 132 | -------------------------------------------------------------------------------- /vignettes/interests_data.csv: -------------------------------------------------------------------------------- 1 | voterID,idealPoint,idealPointL95,idealPointU95,name,justice,interest 2 | 1,-.56771219,-.67015201,-.48256001,HLBlack,1,0 3 | 2,.53880751,.34412101,.736718,SFReed,1,0 4 | 3,.49073979,.378456,.59839201,FFrankfurter,1,0 5 | 4,-1.2664902,-1.47638,-1.098624,WODouglas,1,0 6 | 5,.45168981,.078497998,.86204398,RHJackson,1,0 7 | 6,.5619182,.41773301,.70410699,HHBurton,1,0 8 | 7,.1347498,.056603,.20463599,TCClark,1,0 9 | 8,.39399779,.20838501,.58087099,SMinton,1,0 10 | 9,-.55128628,-.65710598,-.464798,EWarren,1,0 11 | 10,.5897904,.49291199,.67763102,JHarlan2,1,0 12 | 11,-.51152468,-.58068901,-.45381299,WJBrennan,1,0 13 | 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4182,-1.6480429,-1.9927419,-1.343639,CALIFORNIA WOMEN LAWYERS,0,1 205 | 4184,-1.9255759,-2.443372,-1.450326,CALIFORNIA WOMEN'S LAW CENTER,0,1 206 | 4279,-.051328801,-.27976099,.18042199,"CAPITAL CITIES/ABC, INC.",0,1 207 | 4394,-.68258142,-.93309897,-.44812101,CATHOLIC CHARITIES USA,0,1 208 | 4410,.49205369,.342502,.64920801,CATHOLIC LEAGUE FOR RELIGIOUS AND CIVIL RIGHTS,0,1 209 | 4411,-.51996332,-.75560302,-.30080301,CATHOLIC LEGAL IMMIGRATION NETWORK,0,1 210 | 4429,-1.882877,-2.582716,-1.299228,CATHOLICS FOR CHOICE,0,1 211 | 4438,.077486098,-.0082740001,.162193,CATO INSTITUTE,0,1 212 | 4450,-.050352901,-.19451199,.092156,CBS BROADCASTING INC.,0,1 213 | 4506,.35415399,.067925997,.658041,CENTER FOR COMPETITIVE POLITICS,0,1 214 | 4507,.7071265,.51384199,.904208,CENTER FOR CONSTITUTIONAL JURISPRUDENCE,0,1 215 | 4508,-.95887399,-1.137022,-.79831302,CENTER FOR CONSTITUTIONAL RIGHTS,0,1 216 | 4516,-.26195201,-.479848,-.052754,CENTER FOR DEMOCRACY & TECHNOLOGY,0,1 217 | 4537,1.0108922,.70043498,1.360534,CENTER FOR EQUAL OPPORTUNITY,0,1 218 | 4559,.46301651,.191008,.76281101,CENTER FOR INDIVIDUAL FREEDOM,0,1 219 | 4560,.55596173,.324361,.808236,CENTER FOR INDIVIDUAL RIGHTS,0,1 220 | 4573,-.43110359,-.64640599,-.233813,CENTER FOR LAW AND EDUCATION,0,1 221 | 4624,.3614538,.16813201,.57393599,CENTER FOR THE COMMUNITY INTEREST,0,1 222 | 4643,-1.5445246,-1.8700401,-1.238284,CENTER FOR WOMEN POLICY STUDIES,0,1 223 | 4659,-.33355969,-.55602002,-.132448,CENTER ON SOCIAL WELFARE POLICY AND LAW,0,1 224 | 4660,-.37090209,-.57549399,-.180639,CENTER ON THE ADMINISTRATION OF CRIMINAL LAW,0,1 225 | 4681,-.84863502,-1.080052,-.630683,CENTRAL CONFERENCE OF AMERICAN RABBIS,0,1 226 | 4765,.38143671,.31538501,.44664201,CHAMBER OF COMMERCE OF THE U.S.,0,1 227 | 4840,.039540201,-.14771201,.233575,CHEVRON CORP.,0,1 228 | 4912,-.36541849,-.53825802,-.207334,CHILD WELFARE LEAGUE OF AMERICA,0,1 229 | 4934,-.69849598,-.87630397,-.53146702,CHILDREN'S DEFENSE FUND,0,1 230 | 4973,-.8407805,-1.141152,-.56768799,CHINESE FOR AFFIRMATIVE ACTION,0,1 231 | 5011,.4063167,.28088,.53625101,CHRISTIAN LEGAL SOCIETY,0,1 232 | 5035,.4086678,.19925299,.632936,CHURCH OF JESUS CHRIST OF LATTER-DAY SAINTS,0,1 233 | 5068,-.13858689,-.36669299,.086846001,"CISCO SYSTEMS, INC.",0,1 234 | 5115,.1851505,.001587,.380463,CITIZENS FOR DECENCY THROUGH LAW,0,1 235 | 5147,.54088932,.264943,.83244997,CITIZENS UNITED FOUNDATION,0,1 236 | 5785,.040296201,-.172644,.25902301,CLARENDON FOUNDATION,0,1 237 | 5942,-2.1991332,-2.778971,-1.754908,COALITION OF LABOR UNION WOMEN,0,1 238 | 6258,-.28408259,-.43952,-.138318,COMMON CAUSE,0,1 239 | 6363,.2911371,.091022998,.50858903,COMPETITIVE ENTERPRISE INSTITUTE,0,1 240 | 6369,-.096301697,-.278896,.083953001,COMPUTER & COMMUNICATIONS INDUSTRY ASSOCIATION,0,1 241 | 6393,.83462751,.64306802,1.04984,CONCERNED WOMEN FOR AMERICA,0,1 242 | 6448,-.66103679,-.93391401,-.428691,CONGRESSIONAL BLACK CAUCUS,0,1 243 | 6525,-1.99218,-2.3459849,-1.643988,CONNECTICUT WOMEN'S EDUCATION AND LEGAL FUND,0,1 244 | 6537,.3801091,.201644,.57089001,CONSERVATIVE LEGAL DEFENSE AND EDUCATION FUND,0,1 245 | 6557,-.39517909,-.57915503,-.221536,CONSTITUTION PROJECT,0,1 246 | 6559,-.75011063,-1.0221339,-.50076598,CONSTITUTIONAL ACCOUNTABILITY CENTER,0,1 247 | 6575,-.31662941,-.54229403,-.105646,CONSUMER ATTORNEYS OF CALIFORNIA,0,1 248 | 6576,.2299521,.058076002,.41151199,CONSUMER BANKERS ASSOCIATION,0,1 249 | 6585,-.25991601,-.39489001,-.13191301,CONSUMER FEDERATION OF AMERICA,0,1 250 | 6601,-.27395999,-.43560201,-.122739,CONSUMERS UNION,0,1 251 | 6644,-.0394466,-.229333,.15385801,"COPLEY PRESS, INC.",0,1 252 | 6706,-.32034329,-.52929902,-.127048,"COUNCIL FOR PERIODICAL DISTRIBUTORS ASSOCIATION, INC.",0,1 253 | 6748,.307607,.230436,.38668099,COUNCIL OF STATE GOVERNMENTS,0,1 254 | 6752,-.38336071,-.614797,-.16279,COUNCIL OF THE GREAT CITY SCHOOLS,0,1 255 | 6765,-.33063689,-.48940799,-.17840099,COUNCIL ON RELIGIOUS FREEDOM,0,1 256 | 6769,-.0228182,-.182374,.14012399,COUNCIL ON STATE TAXATION,0,1 257 | 6853,.5980038,.50875598,.68817401,CRIMINAL JUSTICE LEGAL FOUNDATION,0,1 258 | 6865,.3712652,.165282,.59065503,CROPLIFE AMERICA,0,1 259 | 6889,.1073509,-.089102,.31025901,CTIA -- THE WIRELESS ASSOCIATION,0,1 260 | 7091,.1201834,-.059916999,.30655,DEFENDERS OF PROPERTY RIGHTS,0,1 261 | 7092,-.39487559,-.59484398,-.204916,DEFENDERS OF WILDLIFE,0,1 262 | 7274,-.0676286,-.275498,.138897,DIRECT MARKETING ASSOCIATION,0,1 263 | 7278,-.1000976,-.340453,.13440099,DIRECTORS GUILD OF AMERICA,0,1 264 | 7298,-.9186002,-1.140205,-.71736503,DISABILITY RIGHTS EDUCATION AND DEFENSE FUND,0,1 265 | 7302,-.64586979,-.89573598,-.420654,DISABILITY RIGHTS LEGAL CENTER,0,1 266 | 7373,-.30776429,-.52284402,-.107572,DKT LIBERTY PROJECT,0,1 267 | 7444,-.048374798,-.190963,.089607999,"DOW JONES & COMPANY, INC.",0,1 268 | 7448,.2838791,.025857,.56881398,DOWNSIZE DC FOUNDATION,0,1 269 | 7449,.30885211,.032398,.59807903,DOWNSIZEDC.ORG,0,1 270 | 7461,.34027189,.21668699,.46615401,DRI -- THE VOICE OF THE DEFENSE BAR,0,1 271 | 7517,-.3248181,-.58258599,-.081683002,DV LEAP,0,1 272 | 7524,.071037598,-.106168,.250539,E.I. DU PONT DE NEMOURS AND COMPANY,0,1 273 | 7529,-.0820494,-.325082,.156497,E.W. SCRIPPS COMPANY,0,1 274 | 7532,.4488627,.30113301,.60233301,EAGLE FORUM EDUCATION & LEGAL DEFENSE FUND,0,1 275 | 7567,-.50921118,-.74464399,-.286329,"EASTER SEALS, INC.",0,1 276 | 7624,.1215535,-.013549,.260241,EDISON ELECTRIC INSTITUTE,0,1 277 | 7705,-.155525,-.303312,-.01054,ELECTRONIC FRONTIER FOUNDATION,0,1 278 | 7708,-.30418539,-.48101699,-.13442799,ELECTRONIC PRIVACY INFORMATION CENTER,0,1 279 | 7720,-.1243728,-.34504899,.095512003,ELI LILLY AND COMPANY,0,1 280 | 7783,.57771111,.32249099,.86352903,EMPLOYERS GROUP,0,1 281 | 7845,-.36251259,-.52474201,-.20443299,ENVIRONMENTAL DEFENSE FUND,0,1 282 | 7863,-.6990841,-.90693098,-.51529598,EPILEPSY FOUNDATION,0,1 283 | 7876,.62855828,.53992802,.71634698,EQUAL EMPLOYMENT ADVISORY COUNCIL,0,1 284 | 7877,-.451868,-.56761903,-.34323099,EQUAL EMPLOYMENT OPPORTUNITY COMMISSION,0,1 285 | 7880,-1.057175,-1.511593,-.67447501,EQUAL JUSTICE SOCIETY,0,1 286 | 7885,-1.7364501,-2.077801,-1.445304,EQUAL RIGHTS ADVOCATES,0,1 287 | 7916,.2892966,.12765101,.46052101,ERISA INDUSTRY COMMITTEE,0,1 288 | 7941,.71318358,.510113,.92977899,ETHICS & RELIGIOUS LIBERTY COMMISSION OF THE SOUTHERN BAPTIST CONVENTION,0,1 289 | 8063,-.42610481,-.58413899,-.279825,FAMILIES AGAINST MANDATORY MINIMUMS,0,1 290 | 8096,.82696837,.62470198,1.02751,FAMILY RESEARCH COUNCIL,0,1 291 | 8162,.0023218,-.22252899,.23360001,FEDERAL CIRCUIT BAR ASSOCIATION,0,1 292 | 8176,.0098954998,-.190549,.219743,FEDERAL ENERGY REGULATORY COMMISSION,0,1 293 | 8196,.047536202,-.162974,.264431,FEDERAL TRADE COMMISSION,0,1 294 | 8199,-1.3856821,-1.884295,-.97322297,FEDERALLY EMPLOYED WOMEN,0,1 295 | 8256,-1.8230227,-2.18908,-1.472607,FEMINIST MAJORITY FOUNDATION,0,1 296 | 8304,.1663309,-.017185001,.36110699,FINANCIAL SERVICES ROUNDTABLE,0,1 297 | 8319,-.054136898,-.29231599,.19123,FIRST AMENDMENT COALITION,0,1 298 | 8320,-.1785216,-.394375,.035519999,FIRST AMENDMENT LAWYERS ASSOCIATION,0,1 299 | 8524,.8700527,.64281899,1.1220289,FOCUS ON THE FAMILY,0,1 300 | 8543,.1233563,-.094246998,.352797,FORD MOTOR COMPANY,0,1 301 | 8611,1.0034083,.59023398,1.478141,FOUNDATION FOR MORAL LAW,0,1 302 | 8672,.56718409,.31170899,.846578,FREE CONGRESS RESEARCH AND EDUCATION FOUNDATION,0,1 303 | 8695,-.36368391,-.52032799,-.217124,FREEDOM TO READ FOUNDATION,0,1 304 | 8743,-.3711963,-.57365501,-.177082,FRIENDS OF THE EARTH,0,1 305 | 8796,-.077629998,-.239852,.081324004,"GANNETT CO., INC.",0,1 306 | 8815,-.94387698,-1.2606519,-.64359802,GAY & LESBIAN ADVOCATES & DEFENDERS,0,1 307 | 8853,-.7206369,-.94993502,-.51137102,GENERAL BOARD OF CHURCH AND SOCIETY OF THE UNITED METHODIST CHURCH,0,1 308 | 8858,-.1216077,-.291913,.047122002,GENERAL CONFERENCE OF SEVENTH-DAY ADVENTISTS,0,1 309 | 8866,.17073689,.01342,.33529899,GENERAL ELECTRIC COMPANY,0,1 310 | 8872,-.046465699,-.21057101,.120929,GENERAL MOTORS CORP.,0,1 311 | 9022,-.70425409,-.91977501,-.51276201,GLOBAL RIGHTS,0,1 312 | 9055,-.1061268,-.34313601,.127373,"GOOGLE, INC.",0,1 313 | 9065,.3214041,.094022997,.56650001,GOVERNMENT FINANCE OFFICERS ASSOCIATION,0,1 314 | 9082,-.0698587,-.211492,.068759002,GRAHAM HOLDINGS COMPANY,0,1 315 | 9112,-.5144245,-.757478,-.29755399,GRAY PANTHERS,0,1 316 | 9221,.050002601,-.15945201,.26497799,GROCERY MANUFACTURERS ASSOCIATION,0,1 317 | 9284,.26711041,.089548998,.45212299,GUN OWNERS FOUNDATION,0,1 318 | 9285,.2654531,.079869002,.459342,"GUN OWNERS OF AMERICA, INC.",0,1 319 | 9310,-1.3311017,-1.663904,-1.008298,HADASSAH,0,1 320 | 9435,-.58956158,-.85548699,-.34448701,HAWAII STATE COALITION AGAINST DOMESTIC VIOLENCE,0,1 321 | 9467,.1947788,.022767,.37245199,HEALTH INSURANCE ASSOCIATION OF AMERICA,0,1 322 | 9480,-.0031774,-.16288701,.155031,HEARST CORPORATION,0,1 323 | 9538,-.078779303,-.267297,.107338,HEWLETT-PACKARD COMPANY,0,1 324 | 9589,-.8646487,-1.141404,-.60044402,HISPANIC NATIONAL BAR ASSOCIATION,0,1 325 | 9683,-.76519167,-1.09144,-.474879,HORACE MANN LEAGUE,0,1 326 | 9790,-.74249887,-1.032915,-.490971,HUMAN RIGHTS ADVOCATES,0,1 327 | 9791,-1.1895732,-1.566342,-.85313201,HUMAN RIGHTS CAMPAIGN,0,1 328 | 9795,-.57276428,-.75523901,-.408371,HUMAN RIGHTS FIRST,0,1 329 | 9800,-.55392331,-.73603898,-.39131001,HUMAN RIGHTS WATCH,0,1 330 | 9909,.79567748,.52254301,1.092948,ILLINOIS ASSOCIATION OF CHIEFS OF POLICE,0,1 331 | 9972,-.3804698,-.617562,-.155085,ILLINOIS STATE BAR ASSOCIATION,0,1 332 | 10037,-.39830169,-.65195,-.15013701,INDEPENDENT BOOK PUBLISHERS ASSOCIATION,0,1 333 | 10039,.085381798,-.115165,.29640999,INDEPENDENT COMMUNITY BANKERS OF AMERICA,0,1 334 | 10064,-.28333431,-.51792997,-.061841,INDEPENDENT SECTOR,0,1 335 | 10146,.81831831,.46279499,1.224625,INDIVIDUAL RIGHTS FOUNDATION,0,1 336 | 10189,-.49837479,-.76624203,-.25395,INNOCENCE NETWORK,0,1 337 | 10215,.071778297,-.049084,.192214,INSTITUTE FOR JUSTICE,0,1 338 | 10255,.4828642,.183203,.81138998,INSTITUTE ON THE CONSTITUTION,0,1 339 | 10275,-.088357098,-.25727999,.075548001,INTEL CORPORATION,0,1 340 | 10280,-.0113442,-.171378,.15139,INTELLECTUAL PROPERTY OWNERS ASSOCIATION,0,1 341 | 10301,-.78909808,-1.21255,-.43723601,INTERFAITH ALLIANCE FOUNDATION,0,1 342 | 10338,.71387768,.59047103,.84988302,INTERNATIONAL ASSOCIATION OF CHIEFS OF POLICE,0,1 343 | 10373,-.2265071,-.42730099,-.029990001,INTERNATIONAL BROTHERHOOD OF TEAMSTERS,0,1 344 | 10375,-.1161332,-.32203501,.091109,INTERNATIONAL BUSINESS MACHINES CORPORATION,0,1 345 | 10391,.28148979,.20726299,.35374799,INTERNATIONAL CITY-COUNTY MANAGEMENT ASSOCIATION,0,1 346 | 10407,.27909511,.068145998,.50353199,INTERNATIONAL COUNCIL OF SHOPPING CENTERS,0,1 347 | 10473,.26866639,.194866,.34626201,INTERNATIONAL MUNICIPAL LAWYERS ASSOCIATION,0,1 348 | 10480,-.32174969,-.51005602,-.139392,INTERNATIONAL PERIODICAL DISTRIBUTORS ASSOCIATION,0,1 349 | 10506,-.043733802,-.246465,.159922,INTERNATIONAL TRADEMARK ASSOCIATION,0,1 350 | 10516,-.41837019,-.56263602,-.28367001,"INTERNATIONAL UNION, UNITED AUTOMOBILE, AEROSPACE, AND AGRICULTURAL IMPLEMENT WORKERS OF AMERICA",0,1 351 | 10733,-1.0058742,-1.297765,-.74224102,JAPANESE AMERICAN CITIZENS LEAGUE,0,1 352 | 10787,-.66036707,-.84918201,-.49370101,JEWISH COUNCIL FOR PUBLIC AFFAIRS,0,1 353 | 10796,-.82306778,-1.154429,-.53722298,JEWISH LABOR COMMITTEE,0,1 354 | 10806,-.56939793,-.84365898,-.31390101,JEWISH WAR VETERANS OF THE U.S.A.,0,1 355 | 10807,-1.7014658,-2.1688969,-1.268169,JEWISH WOMEN INTERNATIONAL,0,1 356 | 10837,-.0815899,-.290234,.12703601,JOHNSON & JOHNSON,0,1 357 | 10900,.37152979,.12237,.654737,JUSTICE AND FREEDOM FUND,0,1 358 | 10917,-.65022099,-.85323697,-.46132699,JUSTICE IN AGING,0,1 359 | 10926,-.63286233,-.825293,-.44558799,JUVENILE LAW CENTER,0,1 360 | 11056,-.46434319,-.727377,-.22398099,KENTUCKY COALITION AGAINST DOMESTIC VIOLENCE,0,1 361 | 11178,1.1767482,.83873999,1.58553,KNIGHTS OF COLUMBUS,0,1 362 | 11320,-1.0046318,-1.2301461,-.80675799,LAMBDA LEGAL DEFENSE AND EDUCATION FUND,0,1 363 | 11339,.4883858,.30756101,.68842399,LANDMARK LEGAL FOUNDATION,0,1 364 | 11377,-1.1211215,-1.322571,-.91122198,LATINOJUSTICE PRLDEF,0,1 365 | 11424,-.69695169,-.956442,-.44725901,LAWYERS COMMITTEE FOR CIVIL RIGHTS OF THE SAN FRANCISCO BAY AREA,0,1 366 | 11425,-.88417119,-1.020005,-.76156902,LAWYERS COMMITTEE FOR CIVIL RIGHTS UNDER LAW,0,1 367 | 11448,-.95406598,-1.23852,-.69318902,LEADERSHIP CONFERENCE ON CIVIL AND HUMAN RIGHTS,0,1 368 | 11472,.32011679,.078612998,.580742,LEAGUE OF CALIFORNIA CITIES,0,1 369 | 11483,-.65239918,-.89047599,-.45308,LEAGUE OF UNITED LATIN AMERICAN CITIZENS,0,1 370 | 11502,-.97833461,-1.168779,-.80525798,LEAGUE OF WOMEN VOTERS OF THE UNITED STATES,0,1 371 | 11537,-.50037891,-.66696101,-.356529,LEGAL AID SOCIETY,0,1 372 | 11564,-1.1104064,-1.3175451,-.92300498,"LEGAL AID SOCIETY, EMPLOYMENT LAW CENTER",0,1 373 | 11579,.5373677,.399124,.68857902,LEGAL FOUNDATION OF AMERICA,0,1 374 | 11582,-1.5245327,-1.741709,-1.329591,LEGAL MOMENTUM,0,1 375 | 11598,-.36387271,-.58584899,-.150916,LEGAL SERVICES NYC,0,1 376 | 11608,-2.1534085,-2.5627561,-1.7970769,LEGAL VOICE,0,1 377 | 11653,.50386029,.3272,.70039999,LIBERTY COUNSEL,0,1 378 | 11655,.1004936,-.078279004,.28798899,LIBERTY INSTITUTE,0,1 379 | 11699,.50886142,.30039001,.738195,LINCOLN INSTITUTE FOR RESEARCH AND EDUCATION,0,1 380 | 11825,-.041476499,-.264478,.18163601,LOS ANGELES TIMES,0,1 381 | 11938,.9877547,.69506001,1.3040349,LUTHERAN CHURCH-MISSOURI SYNOD,0,1 382 | 11942,-.56488049,-.82135701,-.32772401,LUTHERAN IMMIGRATION AND REFUGEE SERVICE,0,1 383 | 12059,-.0051663001,-.223289,.209566,MAJOR CITIES CHIEFS ASSOCIATION,0,1 384 | 12077,-1.4660591,-1.947873,-1.059996,MANA: A NATIONAL LATINA ORGANIZATION,0,1 385 | 12147,-.1050568,-.279286,.062711999,MARITIME LAW ASSOCIATION OF THE UNITED STATES,0,1 386 | 12345,-.053793602,-.29116201,.18005,MCCLATCHY COMPANY,0,1 387 | 12386,-.075492702,-.26277301,.110486,MEDIA INSTITUTE,0,1 388 | 12463,-.64508671,-.829485,-.47619301,MENTAL HEALTH AMERICA,0,1 389 | 12569,-.96323359,-1.129244,-.82320899,MEXICAN AMERICAN LEGAL DEFENSE AND EDUCATIONAL FUND,0,1 390 | 12585,-.0530788,-.238444,.129555,MIAMI HERALD PUBLISHING CO.,0,1 391 | 12690,-.049437702,-.23384,.134138,MICROSOFT CORPORATION,0,1 392 | 12693,.2215309,.038869001,.41500399,MID-AMERICA LEGAL FOUNDATION,0,1 393 | 13129,.219243,.002967,.44315699,MORTGAGE BANKERS ASSOCIATION,0,1 394 | 13142,-.1968735,-.34706199,-.048140001,"MOTION PICTURE ASSOCIATION OF AMERICA, INC.",0,1 395 | 13161,.4531211,.32664901,.58346701,MOUNTAIN STATES LEGAL FOUNDATION,0,1 396 | 13172,-.1006967,-.246839,.046569999,MPA THE ASSOCIATION OF MAGAZINE MEDIA,0,1 397 | 13202,.0440307,-.072352998,.16031601,MULTISTATE TAX COMMISSION,0,1 398 | 13250,-.76176542,-.88255298,-.653974,"NAACP LEGAL DEFENSE AND EDUCATIONAL FUND, INC.",0,1 399 | 13299,-1.5048019,-1.9756809,-1.118874,NARAL PRO-CHOICE AMERICA,0,1 400 | 13344,-1.812393,-2.3348861,-1.348262,NATIONAL ABORTION FEDERATION,0,1 401 | 13390,-.66913068,-.88169402,-.47263399,NATIONAL ALLIANCE ON MENTAL ILLNESS,0,1 402 | 13401,-.94092411,-1.2513469,-.65466499,NATIONAL ASIAN PACIFIC AMERICAN BAR ASSOCIATION,0,1 403 | 13403,-1.5025933,-1.947418,-1.05981,NATIONAL ASIAN PACIFIC AMERICAN WOMEN'S FORUM,0,1 404 | 13427,-.88900691,-1.0313441,-.75501001,NATIONAL ASSOCIATION FOR THE ADVANCEMENT OF COLORED PEOPLE,0,1 405 | 13461,-.093500398,-.210749,.020006999,NATIONAL ASSOCIATION OF BROADCASTERS,0,1 406 | 13475,-.3019788,-.53671598,-.079999998,"NATIONAL ASSOCIATION OF COLLEGE STORES, INC.",0,1 407 | 13480,-.42711011,-.63371497,-.23641901,NATIONAL ASSOCIATION OF CONSUMER ADVOCATES,0,1 408 | 13482,-.18166409,-.35954201,-.0080070002,NATIONAL ASSOCIATION OF CONSUMER BANKRUPTCY ATTORNEYS,0,1 409 | 13485,-.42550299,-.65167397,-.224099,NATIONAL ASSOCIATION OF COUNSEL FOR CHILDREN,0,1 410 | 13486,.28024179,.206283,.350941,NATIONAL ASSOCIATION OF COUNTIES,0,1 411 | 13493,-.53922719,-.61558402,-.4691,NATIONAL ASSOCIATION OF CRIMINAL DEFENSE LAWYERS,0,1 412 | 13501,.4570066,.31996301,.59980702,NATIONAL ASSOCIATION OF 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594 | 19881,.2476663,.175346,.319074,U.S. CONFERENCE OF MAYORS,0,1 595 | 19899,.4878999,.295432,.69811398,U.S. JUSTICE FOUNDATION,0,1 596 | 19917,-.37898609,-.54901803,-.213963,U.S. PUBLIC INTEREST RESEARCH GROUP,0,1 597 | 19927,-1.3521812,-1.76325,-.99196798,U.S. STUDENT ASSOCIATION,0,1 598 | 19929,-.092922203,-.28901801,.095265001,U.S. TELECOM ASSOCIATION,0,1 599 | 19995,-.90569299,-1.059033,-.75596499,UNION FOR REFORM JUDAISM,0,1 600 | 20013,.043180998,-.118285,.20810699,UNION OF ORTHODOX JEWISH CONGREGATIONS OF AMERICA,0,1 601 | 20032,-1.0643502,-1.284814,-.867019,UNITARIAN UNIVERSALIST ASSOCIATION,0,1 602 | 20040,-1.3958265,-1.8412009,-1.010344,UNITARIAN UNIVERSALIST WOMEN'S FEDERATION,0,1 603 | 20061,-.62222642,-.83571798,-.424014,UNITED CEREBRAL PALSY,0,1 604 | 20068,-1.0027541,-1.349996,-.70800197,UNITED CHURCH BOARD FOR HOMELAND MINISTRIES,0,1 605 | 20070,-.89441228,-1.222857,-.59915602,UNITED CHURCH OF CHRIST,0,1 606 | 20076,-1.4751531,-2.091116,-1.006997,UNITED CHURCH OF 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AND URBAN AFFAIRS,0,1 618 | 20823,.42778051,.35476199,.49863401,WASHINGTON LEGAL FOUNDATION,0,1 619 | 21146,-1.8248221,-2.3857911,-1.3801351,WIDER OPPORTUNITIES FOR WOMEN,0,1 620 | 21151,-.3122198,-.53564203,-.097924002,WILDERNESS SOCIETY,0,1 621 | 21331,-2.1603899,-2.595727,-1.787604,WOMEN EMPLOYED,0,1 622 | 21356,-1.517741,-1.8919671,-1.187268,WOMEN LAWYERS ASSOCIATION OF LOS ANGELES,0,1 623 | 21368,-1.7656487,-2.2812741,-1.327396,WOMEN OF REFORM JUDAISM,0,1 624 | 21390,-1.5853972,-2.0698869,-1.136344,WOMEN'S BAR ASSOCIATION OF MASSACHUSETTS,0,1 625 | 21391,-1.4792494,-1.948835,-1.06926,WOMEN'S BAR ASSOCIATION OF THE DISTRICT OF COLUMBIA,0,1 626 | 21392,-1.2562422,-1.723685,-.85183698,WOMEN'S BAR ASSOCIATION OF THE STATE OF NEW YORK,0,1 627 | 21419,-1.4767802,-1.8764369,-1.157584,WOMEN'S EQUITY ACTION LEAGUE,0,1 628 | 21445,-2.0155764,-2.5175591,-1.5696681,WOMEN'S LAW CENTER OF MARYLAND,0,1 629 | 21447,-2.4747577,-2.9875579,-2.024507,WOMEN'S LAW PROJECT,0,1 630 | 21468,-1.30039,-1.705592,-.918136,WOMEN'S SPORTS FOUNDATION,0,1 631 | 21533,-.105493,-.34873801,.137146,"WRITERS GUILD OF AMERICA, WEST",0,1 632 | 21590,.00045190001,-.21500599,.21491,YAHOO! INC.,0,1 633 | 21650,-.67800963,-.92839998,-.45674199,YOUTH LAW CENTER,0,1 634 | 21665,-1.5693512,-2.0493469,-1.194705,YWCA OF THE U.S.A.,0,1 635 | -------------------------------------------------------------------------------- /vignettes/orgs.RData: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aaronrkaufman/stringmatch/681d1071d1fe72953c12f62620d35e6d5c9c3c05/vignettes/orgs.RData --------------------------------------------------------------------------------