R Tools for FEC Campaign Finance Disclosure Data

[This article was first published on datum » R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

For my first contribution to the blog, I wanted to make some kind of enlightening visualization of campaign finance disclosure data from the Federal Election Commission’s website. It looks like they’re working on some new, easy-to-use data dumps here, but I decided to try to use the more detailed data files here because I couldn’t really tell the difference between the two data pages, and as a rule I always of for the most granular unaggregated data when I have a choice.

Anyway, the FEC dumps the data in some weird fixed-width COBOL format that kept me from using any of the read.delim functions to get the data into R, so I had to write a bunch of little parsing functions for each data file. I spent all day yesterday on these little helpers and I haven’t yet had the opportunity to do anything interesting with the data, so I decided that I would just post the code and work on some visualizations later this week.

So in summary, this code makes each of the FEC data dump file into R data frames:

  • Committee Master File: cmteeMaster
  • Candidate Master File: candMaster
  • Individual Contributions: individuals
  • Contributions to Candidates from Committees: candFromCommittees
  • Transactions between Committees: commToComm
This data is DIRTY, and it still needs a lot of work… this code just gets it into data frames. More to come.

# makeData_campaignFinance_v1_0.R -- copyright 10.17.2011, christopher compeau (email: my last name aht gmail dot com) # use as you please but please attribute credit to christopher compeau if you publish anything # the use of the FEC campaign finance data is subject to the rules on the FEC website # have fun my babies. bonus points if you get yourself on some conrgessional campaign's shit list. # this uses the 2011-2012 detailed disclosure data files at http://www.fec.gov/finance/disclosure/ftpdet.shtml # still to be done: write tools for amended individual contributions files and other stuff as yet undiscovered. # RAW DATA FILE PARSING TOOLS trim.trailing <- function (x) {sub("\\s+$", "", x)} # committee master file cmMaster = function(line) { cmID = substr(line,1,9) cmNAME = substr(line,10,99) treasurer = substr(line,100,137) streetOne = substr(line,138,171) streetTwo = substr(line,172,205) cityTown = substr(line,206,223) state = substr(line,224,225) zip = substr(line,226,230) cmDESIG = substr(line,231,231) cmTYPE = substr(line,232,232) cmPARTY = substr(line,233,235) fileFreq = substr(line,236,236) groupCategory = substr(line,237,237) orgName = substr(line,238,275) candidateID = substr(line,276,284) record = c(cmID,cmNAME,treasurer,streetOne,streetTwo,cityTown,state,zip,cmDESIG,cmTYPE,cmPARTY,fileFreq,groupCategory,orgName,candidateID) for (i in 1:length(record)) { record[i] = trim.trailing(record[i]) } return(record) } # candidate master file candMaster = function(line) { cndID = substr(line,1,9) cndName = substr(line,10,47) partyDesig1 = substr(line,48,50) filler1 = substr(line,51,53) partyDesig3 = substr(line,54,56) seatStatus = substr(line,57,57) filler2 = substr(line,58,58) candidateStatus = substr(line,59,59) streetOne = substr(line,60,93) streetTwo = substr(line,94,127) cityTown = substr(line,128,145) state = substr(line,146,147) zip = substr(line,148,152) principalCommID = substr(line,153,161) electionYear = substr(line,162,163) currentDistrict = substr(line,164,165) record = c(cndID,cndName,partyDesig1,filler1,seatStatus,filler2,candidateStatus,streetOne,streetTwo,cityTown,state,zip,principalCommID,electionYear,currentDistrict) for (i in 1:length(record)) { record[i] = trim.trailing(record[i]) } return(record) } # indivudual candidate contributions, committee to committe transactions indAndComContribution = function(line) { filerID = substr(line,1,9) amendIndicator = substr(line,10,10) reportType = substr(line,11,13) primaryGeneral = substr(line,14,14) microfilmLocation = substr(line,15,25) transactionType = substr(line,26,28) contributorName = substr(line,29,62) cityTown = substr(line,63,80) state = substr(line,81,82) zip = substr(line,83,87) occupation = substr(line,88,122) month = substr(line,123,124) transactionDay = substr(line,125,126) transactionCentury = substr(line,127,128) transactionYear = substr(line,129,130) amount = substr(line,131,137) otherID = substr(line,138,146) fecRecord = substr(line,147,153) record = c(filerID,amendIndicator,reportType,primaryGeneral,microfilmLocation,transactionType,contributorName,cityTown,state,zip,occupation,month,transactionDay,transactionCentury,transactionYear,amount,otherID,fecRecord) for (i in 1:length(record)) { record[i] = trim.trailing(record[i]) } return(record) } # contributions to candidate from committees candComContibution = function(line) { filerID = substr(line,1,9) amendIndicator = substr(line,10,10) reportType = substr(line,11,13) primaryGeneral = substr(line,14,14) microfilmLocation = substr(line,15,25) transactionType = substr(line,26,28) transactionMonth = substr(line,29,30) transactionDay = substr(line,31,32) transactionCentury = substr(line,33,34) transactionYear = substr(line,35,36) amount = substr(line,37,43) otherID = substr(line,44,52) candidateID = substr(line,53,61) fecRecord = substr(line,62,68) record = c(filerID,amendIndicator,reportType,primaryGeneral,microfilmLocation,transactionType,transactionMonth,transactionDay,transactionCentury,transactionYear,amount,otherID,candidateID,fecRecord) for (i in 1:length(record)) { record[i] = trim.trailing(record[i]) } return(record) } # overpunch tool overpunch = function(x) { # remove leading zeroes amount = sub("^0+","",x) sign = rep(1,length(x)) changeChar = c( expression(sub("\\[$","0",amount)), expression(sub("\\]$","0",amount)), expression(sub("[{}]$","0",amount)), expression(sub("[AJ]$","1",amount)), expression(sub("[BK]$","2",amount)), expression(sub("[CL]$","3",amount)), expression(sub("[DM]$","4",amount)), expression(sub("[EN]$","5",amount)), expression(sub("[FO]$","6",amount)), expression(sub("[GP]$","7",amount)), expression(sub("[HQ]$","8",amount)), expression(sub("[IR]$","9",amount)) ) changes1 = grep("\\]$",amount) changes2 = grep("[JKLMNOPQR}]$",amount) sign[c(changes1,changes2)] = -1 for (i in 1:length(changeChar)) { amount = eval(changeChar[i]) } holder = as.numeric(sign) * as.numeric(amount) return(holder) } # function using parsing tools to make data frames # 'expsn' is an unevaluated expression for each parsing tool # some raw data records are not the length stated in data docs mkDataFrame = function(data,lineLength,columnNames,expsn) { properData = data[nchar(data, allowNA=TRUE)==lineLength] nRecords = length(properData) finalMatrix = matrix(nrow=length(properData),ncol=length(columnNames)) for (i in 1:nRecords) { result = eval(expsn) finalMatrix[i,] = result } finalDF = as.data.frame(finalMatrix) names(finalDF) = columnNames return(finalDF) } # Now use parsing tools to read data into dataframes # Committee Master File cmteeMasterRaw = read.delim(file="~/Projects/campaign_finance/data/committeeMaster_2011_2012.dta", sep="\n") cmteeMasterRaw = as.character(cmteeMasterRaw[,1]) cmteeMasterNames = c("cmID","cmNAME","treasurer","streetOne","streetTwo","cityTown","state","zip","cmDESIG","cmTYPE","cmPARTY","fileFreq","groupCategory","orgName","candidateID") cmteeMaster = mkDataFrame(cmteeMasterRaw,284,cmteeMasterNames,expression(cmMaster(properData[i]))) # Candidate Master File candMasterRaw = read.delim(file="~/Projects/campaign_finance/data/candidateMaster_2011_2012.dta", sep="\n") candMasterRaw = as.character(candMasterRaw[,1]) candMasterNames = c('cndID','cndName','partyDesig1','filler1','seatStatus','filler2','candidateStatus','streetOne','streetTwo','cityTown','state','zip','principalCommID','electionYear','currentDistrict') candMaster = mkDataFrame(candMasterRaw,165,candMasterNames,expression(candMaster(properData[i]))) # Individual Contributions individualRaw = read.delim(file="~/Projects/campaign_finance/data/individualContributions_2011_2012.dta", sep="\n") individualRaw = as.character(individualRaw[,1]) individualNames = c('filerID','amendIndicator','reportType','primaryGeneral','microfilmLocation','transactionType','contributorName','cityTown','state','zip','occupation','month','transactionDay','transactionCentury','transactionYear','amount','otherID','fecRecord') individuals = mkDataFrame(individualRaw,153,individualNames,expression(indAndComContribution(properData[i]))) individuals$amount = overpunch(individuals$amount) # Contributions from Committees candFromCommitteesRaw = read.delim(file="~/Projects/campaign_finance/data/candidatesFromCommittees_2011_2012.dta", sep="\n") candFromCommitteesRaw = as.character(candFromCommitteesRaw[,1]) candFromCommitteesNames = c('filerID','amendIndicator','reportType','primaryGeneral','microfilmLocation','transactionType','transactionMonth','transactionDay','transactionCentury','transactionYear','amount','otherID','candidateID','fecRecord') candFromCommittees = mkDataFrame(candFromCommitteesRaw,68,candFromCommitteesNames,expression(candComContibution(properData[i]))) candFromCommittees$amount = overpunch(candFromCommittees$amount) # Transaction from committee to another commToCommRaw = read.delim(file="~/Projects/campaign_finance/data/comitteeToCommittee_2011_2012.dta", sep="\n") commToCommRaw = as.character(commToCommRaw[,1]) commToCommNames = c('filerID','amendIndicator','reportType','primaryGeneral','microfilmLocation','transactionType','contributorName','cityTown','state','zip','occupation','month','transactionDay','transactionCentury','transactionYear','amount','otherID','fecRecord') commToComm = mkDataFrame(commToCommRaw,153,commToCommNames,expression(indAndComContribution(properData[i]))) commToComm$amount = overpunch(commToComm$amount)

To leave a comment for the author, please follow the link and comment on their blog: datum » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)