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Process_2000_PUMS.R
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Process_2000_PUMS.R
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#=================#
#Process_2000_PUMS#
#=================#
#This script processes a Census 5% Public Use Microdata Sample Equivalency
#file (PUMEQ5) from the 2000 Census for a state to produce files that need
#to be included in the VESimHouseholds package of the VisionEval model
#system and used to estimate model parameters for the CreateHouseholds,
PredictWorkers, PredictIncome, and PredictHousing modules. The script
#references the file downloaded from the Census for the State of Oregon, but can
#be modified to process the data for another state. The user can download
#the data for another state from the census at the following URL:
#https://www2.census.gov/census_2000/datasets/PUMS/FivePercent/PUMS
#After downloading the desired state file, the user will need to identify the
#path to the downloaded file in the code in SECTION A below. The user may also
#identify specific PUMAs to extract if the data for a specific metropolitan
#area or metropolitan areas are to be used rather than the data for the whole
#state. See the code in SECTION A on how to do this. Maps identifying the PUMAs
#in each state for the 2000 Census are available at the following URL:
#https://www.census.gov/geographies/reference-maps/2000/geo/2000-pumas.html
#-----------------------------------------------------------
#SECTION A - IDENTIFY THE STATE PUMS FILE AND SELECTED PUMAS
#-----------------------------------------------------------
#Identify the PUMS file for the state
#------------------------------------
#Identify the full path name to the PUMEQ5 file that data is to be processed. See
#notes above on acquiring the Census file.
PumsFile <- "REVISEDPUMS5_41.TXT"
#Identify the PUMAs to select
#----------------------------
#If the whole state is to be selected use the following code
GetPumas <- "ALL"
#If specific PUMAs are to be selected, list them like in the following example
#code (Oregon example)
#GetPumas <- c("41501", "41502", "41503")
#---------------------------------------------------------
#SECTION B: READ IN AND EXTRACT HOUSEHOLD AND PERSONS DATA
#---------------------------------------------------------
#Read in file and split out household and person tables
#------------------------------------------------------
Pums_ <- readLines(PumsFile)
RecordType_ <-
as.vector(sapply(Pums_, function(x) {
substr(x, 1, 1)
}))
H_ <- Pums_[RecordType_ == "H"]
P_ <- Pums_[RecordType_ == "P"]
rm(Pums_, RecordType_, PumsFile)
#Define a function to extract specified PUMS data and put in data frame
#----------------------------------------------------------------------
extractFromPums <-
function(Pums_, Fields_ls) {
lapply(Fields_ls, function(x) {
x$typeFun(unlist(lapply(Pums_, function(y) {
substr(y, x$Start, x$Stop)
})))
})
}
#Identify the housing data to extract
#------------------------------------
#Define names, locations, and types for desired housing fields
HFields_ls <-
list(
SERIALNO = list(Start = 2, Stop = 8, typeFun = as.character),
PUMA5 = list(Start = 19, Stop = 23, typeFun = as.character),
HWEIGHT = list(Start = 102, Stop = 105, typeFun = as.numeric),
UNITTYPE = list(Start = 108, Stop = 108, typeFun = as.numeric),
PERSONS = list(Start = 106, Stop = 107, typeFun = as.numeric),
BLDGSZ = list(Start = 115, Stop = 116, typeFun = as.character),
HINC = list(Start = 251, Stop = 258, typeFun = as.numeric)
)
#Extract the housing data and clean up
#-------------------------------------
#Extract housing data fields and put in data frame
H_df <- data.frame(extractFromPums(H_, HFields_ls), stringsAsFactors = FALSE)
#Extract records for desired PUMAs
if (GetPumas[1] != "ALL") {
H_df <- H_df[H_df$PUMA5 %in% GetPumas,]
}
#Clean up
rm(H_, HFields_ls)
#Identify the person data to extract
#-----------------------------------
#Define names, locations, and types for desired person fields
PFields_ls <-
list(
SERIALNO = list(Start = 2, Stop = 8, typeFun = as.character),
AGE = list(Start = 25, Stop = 26, typeFun = as.numeric),
WRKLYR = list(Start = 236, Stop = 236, typeFun = as.character),
MILITARY = list(Start = 138, Stop = 138, typeFun = as.numeric),
INCTOT = list(Start = 297, Stop = 303, typeFun = as.numeric)
)
#Extract the person data and clean up
#------------------------------------
#Extract the person data fields and put in data frame
P_df <- data.frame(extractFromPums(P_, PFields_ls), stringsAsFactors = FALSE)
#If not getting data for entire state, limit person records to be consistent
#with the household records for the selected PUMAs
if (GetPumas[1] != "ALL") {
P_df <- P_df[P_df$SERIALNO %in% unique(H_df$SERIALNO),]
}
rm(P_, PFields_ls)
#---------------------------------------------------
#SECTION C: SAVE THE HOUSEHOLD AND PERSON DATA FILES
#---------------------------------------------------
#Save the household file
#-----------------------
write.table(
H_df,
file = "pums_households.csv",
row.names = FALSE,
col.names = TRUE,
sep = ",")
#Save the persons file
#---------------------
write.table(
P_df,
file = "pums_persons.csv",
row.names = FALSE,
col.names = TRUE,
sep = ",")