R/hmda.R
In ajdamico/lodown: locally download and prepare publicly-available microdata
Defines functions
lodown_hmda
get_catalog_hmda
get_catalog_hmda <-
function( data_name = "hmda" , output_dir , ... ){
hmda_table <- rvest::html_table( xml2::read_html( "https://www.ffiec.gov/hmda/hmdaflat.htm" ) , fill = TRUE )
pmic_table <- rvest::html_table( xml2::read_html( "https://www.ffiec.gov/hmda/pmicflat.htm" ) , fill = TRUE )
latest_hmda_year <- max( unlist( sapply( hmda_table , function( z ) suppressWarnings( as.numeric( names( z ) ) ) ) ) , na.rm = TRUE )
latest_pmic_year <- max( unlist( sapply( pmic_table , function( z ) suppressWarnings( as.numeric( names( z ) ) ) ) ) , na.rm = TRUE )
cat_hmda_lar <-
data.frame(
year = 2006:latest_hmda_year ,
type = 'hmda_lar' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/" , "LAR/National" , "/" , 2006:latest_hmda_year , "HMDA" , "lar%20-%20National" , ".zip" ) ,
merge_table = paste0( 'hmda_' , 2006:latest_hmda_year ) ,
stringsAsFactors = FALSE
)
cat_pmic_lar <-
data.frame(
year = 2006:latest_pmic_year ,
type = 'pmic_lar' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/" , "LAR/National" , "/" , 2006:latest_pmic_year , "PMIC" , "lar%20-%20National" , ".zip" ) ,
merge_table = paste0( 'pmic_' , 2006:latest_pmic_year ) ,
stringsAsFactors = FALSE
)
cat_hmda_inst <-
data.frame(
year = 2006:latest_hmda_year ,
type = 'hmda_inst' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/" , "OTHER" , "/" , 2006:latest_hmda_year , "HMDA" , "institutionrecords" , ".zip" ) ,
merge_table = paste0( 'hmda_' , 2006:latest_hmda_year ) ,
stringsAsFactors = FALSE
)
cat_pmic_inst <-
data.frame(
year = 2006:latest_pmic_year ,
type = 'pmic_inst' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/" , "OTHER" , "/" , 2006:latest_pmic_year , "PMIC" , "institutionrecords" , ".zip" ) ,
merge_table = paste0( 'pmic_' , 2006:latest_pmic_year ) ,
stringsAsFactors = FALSE
)
cat_hmda_reporter <-
data.frame(
year = 2007:latest_hmda_year ,
type = 'hmda_reporter' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_hmda_year , "HMDA" , "ReporterPanel.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_pmic_reporter <-
data.frame(
year = 2007:latest_pmic_year ,
type = 'pmic_reporter' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/OTHER/" , 2007:latest_pmic_year , "PMIC" , "ReporterPanel.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_hmda_msa <-
data.frame(
year = 2007:latest_hmda_year ,
type = 'hmda_msa' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_hmda_year , "HMDA" , "MSAOffice.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_pmic_msa <-
data.frame(
year = 2007:latest_pmic_year ,
type = 'pmic_msa' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_pmic_year , "HMDA" , "MSAOffice.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
catalog <- rbind( cat_hmda_lar , cat_pmic_lar , cat_hmda_inst , cat_pmic_inst , cat_hmda_reporter , cat_pmic_reporter , cat_hmda_msa , cat_pmic_msa )
catalog[ , 'dbfile' ] <- paste0( output_dir , "/SQLite.db" )
catalog$db_tablename <- paste0( catalog$type , "_" , catalog$year )
catalog <- catalog[ order( catalog$year ) , ]
catalog
}
lodown_hmda <-
function( data_name = "hmda" , catalog , path_to_7za = '7za' , ... ){
if( ( .Platform$OS.type != 'windows' ) && ( system( paste0('"', path_to_7za , '" -h' ) ) != 0 ) ) stop( "you need to install 7-zip. if you already have it, include a path_to_7za='/directory/7za' parameter" )
on.exit( print( catalog ) )
tf <- tempfile()
ins_sas <- system.file("extdata", "hmda/ins_str.csv", package = "lodown")
lar_sas <- system.file("extdata", "hmda/lar_str.csv", package = "lodown")
rp_sas <- system.file("extdata", "hmda/Reporter_Panel_2010.sas", package = "lodown")
pr_str <- system.file("extdata", "hmda/Reporter_Panel_Pre-2010.sas", package = "lodown")
# read in the loan application record structure file
lar_str <- read.csv( lar_sas )
# paste all rows together into single strings
lar_col <- apply( lar_str , 1 , paste , collapse = " " )
# take a look at the `lar_str` and `lar_col` objects if you're curious
# just type 'em into the console to see what i mean ;)
# read in the loan application record structure file
ins_str <- read.csv( ins_sas )
# paste all rows together into single strings
ins_col <- apply( ins_str , 1 , paste , collapse = " " )
# create an msa office sas importation script..
office.lines <-
"INPUT
AS_OF_YEAR 4
Agency_Code $ 1
Respondent_ID $ 10
MSA_MD $ 5
MSA_MD_Description $ 50
;"
# ..save it to the local disk as a temporary file..
writeLines( office.lines , tf )
# ..and save the column names into a new object `office.names`
suppressWarnings( office.names <- tolower( SAScii::parse.SAScii( tf )$varname ) )
for ( i in seq_len( nrow( catalog ) ) ){
# open the connection to the monetdblite database
db <- DBI::dbConnect( RSQLite::SQLite() , catalog[ i , 'dbfile' ] )
# download the file
cachaca( catalog[ i , "full_url" ] , tf , mode = 'wb' )
if ( .Platform$OS.type == 'windows' ){
unzipped_files <- unzip_warn_fail( tf , exdir = paste0( tempdir() , "/unzips" ) , overwrite = TRUE )
} else {
files_before <- list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE )
# build the string to send to the terminal on non-windows systems
dos_command <- paste0( '"' , path_to_7za , '" x ' , tf , ' -aoa -o"' , paste0( tempdir() , "/unzips" ) , '"' )
system( dos_command )
unzipped_files <- list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE )
unzipped_files <- unzipped_files[ !( unzipped_files %in% files_before ) ]
}
if( length( unzipped_files ) != 1 ) stop( "always expecting a single import file" )
if( grepl( "reporter" , catalog[ i , 'type' ] ) ){
if( catalog[ i , 'year' ] < 2010 ) sas_ri <- pr_str else sas_ri <- rp_sas
# read that temporary file directly into MonetDB,
# using only the sas importation script
read_SAScii_monetdb (
unzipped_files , # the url of the file to download
sas_ri , # the
zipped = FALSE , # the ascii file is stored in a zipped file
tl = TRUE , # convert all column names to lowercase
tablename = catalog[ i , 'db_tablename' ] ,
connection = db
)
}
if( grepl( "msa" , catalog[ i , 'type' ] ) ){
# read the entire file into RAM
msa_ofc <-
read.table(
unzipped_files ,
header = FALSE ,
quote = "\"" ,
sep = '\t' ,
# ..using the `office.names` extracted from the code above
col.names = office.names
)
names( msa_ofc ) <- tolower( names( msa_ofc ) )
# write the `msa` table into the database directly
DBI::dbWriteTable( db , catalog[ i , 'db_tablename' ] , msa_ofc )
}
# cycle through both institutional records and loan applications received microdata files
if ( grepl( "inst|lar" , catalog[ i , 'type' ] ) ){
if( grepl( "inst" , catalog[ i , 'type' ] ) ) rectype <- "institutionrecords"
if( grepl( "lar" , catalog[ i , 'type' ] ) ) rectype <- "lar%20-%20National"
# strip just the first three characters from `rectype`
short.name <- substr( rectype , 1 , 3 )
# pull the structure construction
col_str <- get( paste( short.name , "col" , sep = "_" ) )
# design the monetdb table
sql.create <- sprintf( paste( "CREATE TABLE" , catalog[ i , 'db_tablename' ] , "(%s)" ) , paste( col_str , collapse = ", " ) )
# initiate the monetdb table
DBI::dbSendQuery( db , sql.create )
# find the url folder and the appropriate delimiter line for the monetdb COPY INTO command
if ( short.name == "lar" ){
delim.line <- "' using delimiters ',','\\n','\"' NULL AS ''"
} else {
delim.line <- "' using delimiters '\\t' NULL AS ''"
}
if( catalog[ i , 'db_tablename' ] == "hmda_inst_2016" ){
this_table <- read.table( unzipped_files , sep = '\t' , head = FALSE , comment.char = '' , quote = '' , fill = TRUE )
names( this_table ) <- DBI::dbListFields( db , catalog[ i , 'db_tablename' ] )
DBI::dbRemoveTable( db , catalog[ i , 'db_tablename' ] )
DBI::dbWriteTable( db , catalog[ i , 'db_tablename' ] , this_table )
} else {
# construct the monetdb COPY INTO command
sql.copy <-
paste0(
"copy into " ,
catalog[ i , 'db_tablename' ] ,
" from '" ,
normalizePath( unzipped_files ) ,
delim.line
)
# actually execute the COPY INTO command
DBI::dbSendQuery( db , sql.copy )
}
# conversion of numeric columns incorrectly stored as character strings #
# initiate a character vector containing all columns that should be numeric types
revision.variables <- c( "sequencenumber" , "population" , "minoritypopulationpct" , "hudmedianfamilyincome" , "tracttomsa_mdincomepct" , "numberofowneroccupiedunits" , "numberof1to4familyunits" )
# determine whether any of those variables are in the current table
field.revisions <- DBI::dbListFields( db , catalog[ i , 'db_tablename' ] )[ tolower( DBI::dbListFields( db , catalog[ i , 'db_tablename' ] ) ) %in% revision.variables ]
# loop through each of those variables
for ( col.rev in field.revisions ){
# add a new `temp_double` column in the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "ADD COLUMN temp_double DOUBLE" ) )
# copy over the contents of the character-typed column so long as the column isn't a textual missing
DBI::dbSendQuery( db , paste( "UPDATE" , catalog[ i , 'db_tablename' ] , "SET temp_double = CAST(" , col.rev , " AS DOUBLE ) WHERE TRIM(" , col.rev , ") <> 'NA'" ) )
# remove the character-typed column from the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "DROP COLUMN" , col.rev ) )
# re-initiate the same column name, but as a numeric type
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "ADD COLUMN" , col.rev , "DOUBLE" ) )
# copy the corrected contents back to the original column name
DBI::dbSendQuery( db , paste( "UPDATE" , catalog[ i , 'db_tablename' ] , "SET" , col.rev , "= temp_double" ) )
# remove the temporary column from the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "DROP COLUMN temp_double" ) )
}
# end of conversion of numeric columns incorrectly stored as character strings #
}
catalog[ i , 'case_count' ] <- DBI::dbGetQuery( db , paste( 'select count(*) from' , catalog[ i , 'db_tablename' ] ) )[ 1 , 1 ]
stopifnot( catalog[ i , 'case_count' ] > 0 )
# delete the temporary files
suppressWarnings( file.remove( tf , unzipped_files , list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE ) ) )
cat( paste0( data_name , " catalog entry " , i , " of " , nrow( catalog ) , " stored in '" , catalog[ i , 'db_tablename' ] , "'\r\n\n" ) )
}
unique_merge_tables <- unique( catalog$merge_table )
unique_merge_tables <- unique_merge_tables[ !is.na( unique_merge_tables ) ]
for( i in seq_along( unique_merge_tables ) ){
# open the connection to the monetdblite database
db <- DBI::dbConnect( RSQLite::SQLite() , unique( catalog[ which( catalog$merge_table == unique_merge_tables[ i ] ) , 'dbfile' ] ) )
ins.tablename <- catalog[ substr( catalog$type , 6 , 8 ) == 'ins' & catalog$merge_table == unique_merge_tables[ i ] , 'db_tablename' ]
lar.tablename <- catalog[ substr( catalog$type , 6 , 8 ) == 'lar' & catalog$merge_table == unique_merge_tables[ i ] , 'db_tablename' ]
new.tablename <- unique_merge_tables[ i ]
# three easy steps #
# step one: confirm the only intersecting fields are "respondentid" and "agencycode"
# these are the merge fields, so nothing else can overlap
stopifnot(
identical(
intersect(
DBI::dbListFields( db , lar.tablename ) ,
DBI::dbListFields( db , ins.tablename )
) ,
c( 'respondentid' , 'agencycode' )
)
)
# step two: merge the two tables
# extract the column names from the institution table
ins.fields <- DBI::dbListFields( db , ins.tablename )
# throw out the two merge fields
ins.nomatch <- ins.fields[ !( ins.fields %in% c( 'respondentid' , 'agencycode' ) ) ]
# add a "b." in front of every field name
ins.b <- paste0( "b." , ins.nomatch )
# separate all of them by commas into a single character string
ins.string <- paste( ins.b , collapse = ", " )
# construct the merge command
sql.merge.command <-
paste(
"CREATE TABLE" ,
new.tablename ,
"AS SELECT a.* ," ,
ins.string ,
"FROM" ,
lar.tablename ,
"AS a INNER JOIN" ,
ins.tablename ,
"AS b ON a.respondentid = b.respondentid AND a.agencycode = b.agencycode"
)
# with your sql string built, execute the command
DBI::dbSendQuery( db , sql.merge.command )
# step three: confirm that the merged table contains the same record count
stopifnot(
DBI::dbGetQuery(
db ,
paste(
'select count(*) from' ,
new.tablename
)
) ==
DBI::dbGetQuery(
db ,
paste(
'select count(*) from' ,
lar.tablename
)
)
)
# # # # # # # # # # # # # # # # # #
# # race and ethnicity recoding # #
# # # # # # # # # # # # # # # # # #
# number of minority races of applicant and co-applicant
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN app_min_cnt INTEGER' ) )
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN co_min_cnt INTEGER' ) )
# sum up all four possibilities
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
app_min_cnt =
(
( applicantrace1 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace2 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace3 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace4 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace5 IN ( 1 , 2 , 3 , 4 ) )*1
)'
)
)
# same for the co-applicant
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
co_min_cnt =
(
( coapplicantrace1 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace2 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace3 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace4 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace5 IN ( 1 , 2 , 3 , 4 ) )*1
)'
)
)
# zero-one test of whether the applicant or co-applicant indicated white
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN appwhite INTEGER' ) )
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN cowhite INTEGER' ) )
# check all five race categories for the answer
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
appwhite =
(
( ( applicantrace1 ) IN ( 5 ) )*1 +
( ( applicantrace2 ) IN ( 5 ) )*1 +
( ( applicantrace3 ) IN ( 5 ) )*1 +
( ( applicantrace4 ) IN ( 5 ) )*1 +
( ( applicantrace5 ) IN ( 5 ) )*1
)'
)
)
# same for the co-applicant
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
cowhite =
(
( ( coapplicantrace1 ) IN ( 5 ) )*1 +
( ( coapplicantrace2 ) IN ( 5 ) )*1 +
( ( coapplicantrace3 ) IN ( 5 ) )*1 +
( ( coapplicantrace4 ) IN ( 5 ) )*1 +
( ( coapplicantrace5 ) IN ( 5 ) )*1
)'
)
)
# if the applicant or co-applicant has a missing first race, set the above variables to missing as well
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET app_min_cnt = NULL WHERE applicantrace1 IN ( 6 , 7 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET appwhite = NULL WHERE applicantrace1 IN ( 6 , 7 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET co_min_cnt = NULL WHERE coapplicantrace1 IN ( 6 , 7 , 8 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET cowhite = NULL WHERE coapplicantrace1 IN ( 6 , 7 , 8 )' ) )
# main race variable
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN race INTEGER' ) )
# 7 indicates a loan by a white applicant and non-white co-applicant or vice-versa
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET race = 7 WHERE ( appwhite = 1 AND app_min_cnt = 0 AND co_min_cnt > 0 ) OR ( cowhite = 1 AND co_min_cnt = 0 AND app_min_cnt > 0 )' ) )
# 6 indicates the main applicant listed multiple non-white races
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET race = 6 WHERE ( app_min_cnt > 1 ) AND ( race IS NULL )' ) )
# for everybody else: if the first race listed by the applicant isn't white, use that.
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET race = applicantrace1 WHERE ( applicantrace1 IN ( '1' , '2' , '3' , '4' ) ) AND ( app_min_cnt = 1 ) AND ( race IS NULL )" ) )
# otherwise look to the second listed race
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET race = applicantrace2 WHERE ( applicantrace2 IN ( '1' , '2' , '3' , '4' ) ) AND ( app_min_cnt = 1 ) AND ( race IS NULL )" ) )
# otherwise confirm the applicant indicated he or she was white
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , "SET race = 5 WHERE ( appwhite = 1 ) AND ( race IS NULL )" ) )
# main ethnicity variable
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN ethnicity VARCHAR (255)' ) )
# simple. check the applicant's ethnicity
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Not Hispanic' WHERE applicantethnicity IN ( 2 )" ) )
# simple. check the applicant's ethnicity again
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Hispanic' WHERE applicantethnicity IN ( 1 )" ) )
# overwrite the ethnicity variable if the main applicant indicates hispanic but the co-applicant does not. or vice versa.
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Joint' WHERE ( applicantethnicity IN ( 1 ) AND coapplicantethnicity IN ( 2 ) ) OR ( applicantethnicity IN ( 2 ) AND coapplicantethnicity IN ( 1 ) )" ) )
# # # # # # # # # # # # # # # # # # # # # # # # #
# # finished with race and ethnicity recoding # #
# # # # # # # # # # # # # # # # # # # # # # # # #
}
on.exit()
catalog
}
ajdamico/lodown documentation built on Feb. 1, 2024, 3:44 p.m.
R Package Documentation
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Defines functions lodown_hmda get_catalog_hmda
get_catalog_hmda <-
function( data_name = "hmda" , output_dir , ... ){
hmda_table <- rvest::html_table( xml2::read_html( "https://www.ffiec.gov/hmda/hmdaflat.htm" ) , fill = TRUE )
pmic_table <- rvest::html_table( xml2::read_html( "https://www.ffiec.gov/hmda/pmicflat.htm" ) , fill = TRUE )
latest_hmda_year <- max( unlist( sapply( hmda_table , function( z ) suppressWarnings( as.numeric( names( z ) ) ) ) ) , na.rm = TRUE )
latest_pmic_year <- max( unlist( sapply( pmic_table , function( z ) suppressWarnings( as.numeric( names( z ) ) ) ) ) , na.rm = TRUE )
cat_hmda_lar <-
data.frame(
year = 2006:latest_hmda_year ,
type = 'hmda_lar' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/" , "LAR/National" , "/" , 2006:latest_hmda_year , "HMDA" , "lar%20-%20National" , ".zip" ) ,
merge_table = paste0( 'hmda_' , 2006:latest_hmda_year ) ,
stringsAsFactors = FALSE
)
cat_pmic_lar <-
data.frame(
year = 2006:latest_pmic_year ,
type = 'pmic_lar' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/" , "LAR/National" , "/" , 2006:latest_pmic_year , "PMIC" , "lar%20-%20National" , ".zip" ) ,
merge_table = paste0( 'pmic_' , 2006:latest_pmic_year ) ,
stringsAsFactors = FALSE
)
cat_hmda_inst <-
data.frame(
year = 2006:latest_hmda_year ,
type = 'hmda_inst' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/" , "OTHER" , "/" , 2006:latest_hmda_year , "HMDA" , "institutionrecords" , ".zip" ) ,
merge_table = paste0( 'hmda_' , 2006:latest_hmda_year ) ,
stringsAsFactors = FALSE
)
cat_pmic_inst <-
data.frame(
year = 2006:latest_pmic_year ,
type = 'pmic_inst' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/" , "OTHER" , "/" , 2006:latest_pmic_year , "PMIC" , "institutionrecords" , ".zip" ) ,
merge_table = paste0( 'pmic_' , 2006:latest_pmic_year ) ,
stringsAsFactors = FALSE
)
cat_hmda_reporter <-
data.frame(
year = 2007:latest_hmda_year ,
type = 'hmda_reporter' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_hmda_year , "HMDA" , "ReporterPanel.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_pmic_reporter <-
data.frame(
year = 2007:latest_pmic_year ,
type = 'pmic_reporter' ,
full_url = paste0( "https://www.ffiec.gov/" , "pmic" , "rawdata/OTHER/" , 2007:latest_pmic_year , "PMIC" , "ReporterPanel.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_hmda_msa <-
data.frame(
year = 2007:latest_hmda_year ,
type = 'hmda_msa' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_hmda_year , "HMDA" , "MSAOffice.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
cat_pmic_msa <-
data.frame(
year = 2007:latest_pmic_year ,
type = 'pmic_msa' ,
full_url = paste0( "https://www.ffiec.gov/" , "hmda" , "rawdata/OTHER/" , 2007:latest_pmic_year , "HMDA" , "MSAOffice.zip" ) ,
merge_table = NA ,
stringsAsFactors = FALSE
)
catalog <- rbind( cat_hmda_lar , cat_pmic_lar , cat_hmda_inst , cat_pmic_inst , cat_hmda_reporter , cat_pmic_reporter , cat_hmda_msa , cat_pmic_msa )
catalog[ , 'dbfile' ] <- paste0( output_dir , "/SQLite.db" )
catalog$db_tablename <- paste0( catalog$type , "_" , catalog$year )
catalog <- catalog[ order( catalog$year ) , ]
catalog
}
lodown_hmda <-
function( data_name = "hmda" , catalog , path_to_7za = '7za' , ... ){
if( ( .Platform$OS.type != 'windows' ) && ( system( paste0('"', path_to_7za , '" -h' ) ) != 0 ) ) stop( "you need to install 7-zip. if you already have it, include a path_to_7za='/directory/7za' parameter" )
on.exit( print( catalog ) )
tf <- tempfile()
ins_sas <- system.file("extdata", "hmda/ins_str.csv", package = "lodown")
lar_sas <- system.file("extdata", "hmda/lar_str.csv", package = "lodown")
rp_sas <- system.file("extdata", "hmda/Reporter_Panel_2010.sas", package = "lodown")
pr_str <- system.file("extdata", "hmda/Reporter_Panel_Pre-2010.sas", package = "lodown")
# read in the loan application record structure file
lar_str <- read.csv( lar_sas )
# paste all rows together into single strings
lar_col <- apply( lar_str , 1 , paste , collapse = " " )
# take a look at the `lar_str` and `lar_col` objects if you're curious
# just type 'em into the console to see what i mean ;)
# read in the loan application record structure file
ins_str <- read.csv( ins_sas )
# paste all rows together into single strings
ins_col <- apply( ins_str , 1 , paste , collapse = " " )
# create an msa office sas importation script..
office.lines <-
"INPUT
AS_OF_YEAR 4
Agency_Code $ 1
Respondent_ID $ 10
MSA_MD $ 5
MSA_MD_Description $ 50
;"
# ..save it to the local disk as a temporary file..
writeLines( office.lines , tf )
# ..and save the column names into a new object `office.names`
suppressWarnings( office.names <- tolower( SAScii::parse.SAScii( tf )$varname ) )
for ( i in seq_len( nrow( catalog ) ) ){
# open the connection to the monetdblite database
db <- DBI::dbConnect( RSQLite::SQLite() , catalog[ i , 'dbfile' ] )
# download the file
cachaca( catalog[ i , "full_url" ] , tf , mode = 'wb' )
if ( .Platform$OS.type == 'windows' ){
unzipped_files <- unzip_warn_fail( tf , exdir = paste0( tempdir() , "/unzips" ) , overwrite = TRUE )
} else {
files_before <- list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE )
# build the string to send to the terminal on non-windows systems
dos_command <- paste0( '"' , path_to_7za , '" x ' , tf , ' -aoa -o"' , paste0( tempdir() , "/unzips" ) , '"' )
system( dos_command )
unzipped_files <- list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE )
unzipped_files <- unzipped_files[ !( unzipped_files %in% files_before ) ]
}
if( length( unzipped_files ) != 1 ) stop( "always expecting a single import file" )
if( grepl( "reporter" , catalog[ i , 'type' ] ) ){
if( catalog[ i , 'year' ] < 2010 ) sas_ri <- pr_str else sas_ri <- rp_sas
# read that temporary file directly into MonetDB,
# using only the sas importation script
read_SAScii_monetdb (
unzipped_files , # the url of the file to download
sas_ri , # the
zipped = FALSE , # the ascii file is stored in a zipped file
tl = TRUE , # convert all column names to lowercase
tablename = catalog[ i , 'db_tablename' ] ,
connection = db
)
}
if( grepl( "msa" , catalog[ i , 'type' ] ) ){
# read the entire file into RAM
msa_ofc <-
read.table(
unzipped_files ,
header = FALSE ,
quote = "\"" ,
sep = '\t' ,
# ..using the `office.names` extracted from the code above
col.names = office.names
)
names( msa_ofc ) <- tolower( names( msa_ofc ) )
# write the `msa` table into the database directly
DBI::dbWriteTable( db , catalog[ i , 'db_tablename' ] , msa_ofc )
}
# cycle through both institutional records and loan applications received microdata files
if ( grepl( "inst|lar" , catalog[ i , 'type' ] ) ){
if( grepl( "inst" , catalog[ i , 'type' ] ) ) rectype <- "institutionrecords"
if( grepl( "lar" , catalog[ i , 'type' ] ) ) rectype <- "lar%20-%20National"
# strip just the first three characters from `rectype`
short.name <- substr( rectype , 1 , 3 )
# pull the structure construction
col_str <- get( paste( short.name , "col" , sep = "_" ) )
# design the monetdb table
sql.create <- sprintf( paste( "CREATE TABLE" , catalog[ i , 'db_tablename' ] , "(%s)" ) , paste( col_str , collapse = ", " ) )
# initiate the monetdb table
DBI::dbSendQuery( db , sql.create )
# find the url folder and the appropriate delimiter line for the monetdb COPY INTO command
if ( short.name == "lar" ){
delim.line <- "' using delimiters ',','\\n','\"' NULL AS ''"
} else {
delim.line <- "' using delimiters '\\t' NULL AS ''"
}
if( catalog[ i , 'db_tablename' ] == "hmda_inst_2016" ){
this_table <- read.table( unzipped_files , sep = '\t' , head = FALSE , comment.char = '' , quote = '' , fill = TRUE )
names( this_table ) <- DBI::dbListFields( db , catalog[ i , 'db_tablename' ] )
DBI::dbRemoveTable( db , catalog[ i , 'db_tablename' ] )
DBI::dbWriteTable( db , catalog[ i , 'db_tablename' ] , this_table )
} else {
# construct the monetdb COPY INTO command
sql.copy <-
paste0(
"copy into " ,
catalog[ i , 'db_tablename' ] ,
" from '" ,
normalizePath( unzipped_files ) ,
delim.line
)
# actually execute the COPY INTO command
DBI::dbSendQuery( db , sql.copy )
}
# conversion of numeric columns incorrectly stored as character strings #
# initiate a character vector containing all columns that should be numeric types
revision.variables <- c( "sequencenumber" , "population" , "minoritypopulationpct" , "hudmedianfamilyincome" , "tracttomsa_mdincomepct" , "numberofowneroccupiedunits" , "numberof1to4familyunits" )
# determine whether any of those variables are in the current table
field.revisions <- DBI::dbListFields( db , catalog[ i , 'db_tablename' ] )[ tolower( DBI::dbListFields( db , catalog[ i , 'db_tablename' ] ) ) %in% revision.variables ]
# loop through each of those variables
for ( col.rev in field.revisions ){
# add a new `temp_double` column in the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "ADD COLUMN temp_double DOUBLE" ) )
# copy over the contents of the character-typed column so long as the column isn't a textual missing
DBI::dbSendQuery( db , paste( "UPDATE" , catalog[ i , 'db_tablename' ] , "SET temp_double = CAST(" , col.rev , " AS DOUBLE ) WHERE TRIM(" , col.rev , ") <> 'NA'" ) )
# remove the character-typed column from the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "DROP COLUMN" , col.rev ) )
# re-initiate the same column name, but as a numeric type
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "ADD COLUMN" , col.rev , "DOUBLE" ) )
# copy the corrected contents back to the original column name
DBI::dbSendQuery( db , paste( "UPDATE" , catalog[ i , 'db_tablename' ] , "SET" , col.rev , "= temp_double" ) )
# remove the temporary column from the data table
DBI::dbSendQuery( db , paste( "ALTER TABLE" , catalog[ i , 'db_tablename' ] , "DROP COLUMN temp_double" ) )
}
# end of conversion of numeric columns incorrectly stored as character strings #
}
catalog[ i , 'case_count' ] <- DBI::dbGetQuery( db , paste( 'select count(*) from' , catalog[ i , 'db_tablename' ] ) )[ 1 , 1 ]
stopifnot( catalog[ i , 'case_count' ] > 0 )
# delete the temporary files
suppressWarnings( file.remove( tf , unzipped_files , list.files( paste0( tempdir() , "/unzips" ) , full.names = TRUE ) ) )
cat( paste0( data_name , " catalog entry " , i , " of " , nrow( catalog ) , " stored in '" , catalog[ i , 'db_tablename' ] , "'\r\n\n" ) )
}
unique_merge_tables <- unique( catalog$merge_table )
unique_merge_tables <- unique_merge_tables[ !is.na( unique_merge_tables ) ]
for( i in seq_along( unique_merge_tables ) ){
# open the connection to the monetdblite database
db <- DBI::dbConnect( RSQLite::SQLite() , unique( catalog[ which( catalog$merge_table == unique_merge_tables[ i ] ) , 'dbfile' ] ) )
ins.tablename <- catalog[ substr( catalog$type , 6 , 8 ) == 'ins' & catalog$merge_table == unique_merge_tables[ i ] , 'db_tablename' ]
lar.tablename <- catalog[ substr( catalog$type , 6 , 8 ) == 'lar' & catalog$merge_table == unique_merge_tables[ i ] , 'db_tablename' ]
new.tablename <- unique_merge_tables[ i ]
# three easy steps #
# step one: confirm the only intersecting fields are "respondentid" and "agencycode"
# these are the merge fields, so nothing else can overlap
stopifnot(
identical(
intersect(
DBI::dbListFields( db , lar.tablename ) ,
DBI::dbListFields( db , ins.tablename )
) ,
c( 'respondentid' , 'agencycode' )
)
)
# step two: merge the two tables
# extract the column names from the institution table
ins.fields <- DBI::dbListFields( db , ins.tablename )
# throw out the two merge fields
ins.nomatch <- ins.fields[ !( ins.fields %in% c( 'respondentid' , 'agencycode' ) ) ]
# add a "b." in front of every field name
ins.b <- paste0( "b." , ins.nomatch )
# separate all of them by commas into a single character string
ins.string <- paste( ins.b , collapse = ", " )
# construct the merge command
sql.merge.command <-
paste(
"CREATE TABLE" ,
new.tablename ,
"AS SELECT a.* ," ,
ins.string ,
"FROM" ,
lar.tablename ,
"AS a INNER JOIN" ,
ins.tablename ,
"AS b ON a.respondentid = b.respondentid AND a.agencycode = b.agencycode"
)
# with your sql string built, execute the command
DBI::dbSendQuery( db , sql.merge.command )
# step three: confirm that the merged table contains the same record count
stopifnot(
DBI::dbGetQuery(
db ,
paste(
'select count(*) from' ,
new.tablename
)
) ==
DBI::dbGetQuery(
db ,
paste(
'select count(*) from' ,
lar.tablename
)
)
)
# # # # # # # # # # # # # # # # # #
# # race and ethnicity recoding # #
# # # # # # # # # # # # # # # # # #
# number of minority races of applicant and co-applicant
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN app_min_cnt INTEGER' ) )
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN co_min_cnt INTEGER' ) )
# sum up all four possibilities
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
app_min_cnt =
(
( applicantrace1 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace2 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace3 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace4 IN ( 1 , 2 , 3 , 4 ) )*1 +
( applicantrace5 IN ( 1 , 2 , 3 , 4 ) )*1
)'
)
)
# same for the co-applicant
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
co_min_cnt =
(
( coapplicantrace1 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace2 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace3 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace4 IN ( 1 , 2 , 3 , 4 ) )*1 +
( coapplicantrace5 IN ( 1 , 2 , 3 , 4 ) )*1
)'
)
)
# zero-one test of whether the applicant or co-applicant indicated white
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN appwhite INTEGER' ) )
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN cowhite INTEGER' ) )
# check all five race categories for the answer
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
appwhite =
(
( ( applicantrace1 ) IN ( 5 ) )*1 +
( ( applicantrace2 ) IN ( 5 ) )*1 +
( ( applicantrace3 ) IN ( 5 ) )*1 +
( ( applicantrace4 ) IN ( 5 ) )*1 +
( ( applicantrace5 ) IN ( 5 ) )*1
)'
)
)
# same for the co-applicant
DBI::dbSendQuery(
db ,
paste(
'UPDATE' ,
new.tablename ,
'SET
cowhite =
(
( ( coapplicantrace1 ) IN ( 5 ) )*1 +
( ( coapplicantrace2 ) IN ( 5 ) )*1 +
( ( coapplicantrace3 ) IN ( 5 ) )*1 +
( ( coapplicantrace4 ) IN ( 5 ) )*1 +
( ( coapplicantrace5 ) IN ( 5 ) )*1
)'
)
)
# if the applicant or co-applicant has a missing first race, set the above variables to missing as well
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET app_min_cnt = NULL WHERE applicantrace1 IN ( 6 , 7 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET appwhite = NULL WHERE applicantrace1 IN ( 6 , 7 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET co_min_cnt = NULL WHERE coapplicantrace1 IN ( 6 , 7 , 8 )' ) )
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET cowhite = NULL WHERE coapplicantrace1 IN ( 6 , 7 , 8 )' ) )
# main race variable
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN race INTEGER' ) )
# 7 indicates a loan by a white applicant and non-white co-applicant or vice-versa
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET race = 7 WHERE ( appwhite = 1 AND app_min_cnt = 0 AND co_min_cnt > 0 ) OR ( cowhite = 1 AND co_min_cnt = 0 AND app_min_cnt > 0 )' ) )
# 6 indicates the main applicant listed multiple non-white races
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , 'SET race = 6 WHERE ( app_min_cnt > 1 ) AND ( race IS NULL )' ) )
# for everybody else: if the first race listed by the applicant isn't white, use that.
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET race = applicantrace1 WHERE ( applicantrace1 IN ( '1' , '2' , '3' , '4' ) ) AND ( app_min_cnt = 1 ) AND ( race IS NULL )" ) )
# otherwise look to the second listed race
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET race = applicantrace2 WHERE ( applicantrace2 IN ( '1' , '2' , '3' , '4' ) ) AND ( app_min_cnt = 1 ) AND ( race IS NULL )" ) )
# otherwise confirm the applicant indicated he or she was white
DBI::dbSendQuery( db , paste( 'UPDATE' , new.tablename , "SET race = 5 WHERE ( appwhite = 1 ) AND ( race IS NULL )" ) )
# main ethnicity variable
DBI::dbSendQuery( db , paste( 'ALTER TABLE' , new.tablename , 'ADD COLUMN ethnicity VARCHAR (255)' ) )
# simple. check the applicant's ethnicity
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Not Hispanic' WHERE applicantethnicity IN ( 2 )" ) )
# simple. check the applicant's ethnicity again
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Hispanic' WHERE applicantethnicity IN ( 1 )" ) )
# overwrite the ethnicity variable if the main applicant indicates hispanic but the co-applicant does not. or vice versa.
DBI::dbSendQuery( db , paste( "UPDATE" , new.tablename , "SET ethnicity = 'Joint' WHERE ( applicantethnicity IN ( 1 ) AND coapplicantethnicity IN ( 2 ) ) OR ( applicantethnicity IN ( 2 ) AND coapplicantethnicity IN ( 1 ) )" ) )
# # # # # # # # # # # # # # # # # # # # # # # # #
# # finished with race and ethnicity recoding # #
# # # # # # # # # # # # # # # # # # # # # # # # #
}
on.exit()
catalog
}
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