######################################################################
# R Script for Analysis of the 2012 NORC Presidential Election Study #
###################################################################### 
#
library(survey)
library(Hmisc)
#
# Read in data
#
setwd("d:/data") #Set the path
npes<-read.csv("electionstudy2012.csv",header=TRUE) #Read comma delimited data file
names(npes)
#
# Establish factor variables
#
CAN_PREFlevels=c(1:4,77,99)
CAN_PREFlabels=c(" Barack Obama"," Mitt Romney"," Some Other Candidate"," Undecided"," DK"," Refused")
npes$CAN_PREF_I=factor(npes$CAN_PREF_I,levels=CAN_PREFlevels,labels=CAN_PREFlabels,exclude=NULL)
PARTY_AFFIL_Rlevels=c(1:4)
PARTY_AFFIL_Rlabels=c("Republican","Democrat","Independent","Other/DK/RF")
npes$PARTY_AFFIL_R_I=factor(npes$PARTY_AFFIL_R_I, levels=PARTY_AFFIL_Rlevels,labels=PARTY_AFFIL_Rlabels,exclude=NULL)
AGElevels=c(1:4,77)
AGElabels=c("18-29 Years","30-54 Years","55-64 Years","65+ Years","DK")
npes$AGE_CAT_I=factor(npes$AGE_CAT_I,levels=AGElevels,labels=AGElabels,exclude=NULL)
SOC_SEClevels=c(1:2,77,99)
SOC_SEClabels=c("Change the Program","Keep Program as Is","DK","Refused")
npes$SOC_SEC_I=factor(npes$SOC_SEC_I,levels=SOC_SEClevels,labels=SOC_SEClabels,exclude=NULL)
npes$MED_FUTURE_II[npes$MED_FUTURE_II==2] = 3 #Recode Medicare so that it and Social Security are harmonized
npes$MED_FUTURE_II[npes$MED_FUTURE_II==1] = 2
npes$MED_FUTURE_II[npes$MED_FUTURE_II==3] = 1
MED_FUTURElevels=c(1:2,77,99)
MED_FUTURElabels=c("Change the Program","Keep Program as Is","DK","Refused")
npes$MED_FUTURE_II=factor(npes$MED_FUTURE_II,levels=MED_FUTURElevels,labels=MED_FUTURElabels,exclude=NULL)
#
# Specify the design: strata, PSUs, and weights. 
# Note that cases are PSUs. 
# Case identifiers are unique across all strata. 
# Reponses to Wave I and Wave II are represented by different variables within a case.
#
npes.design<-svydesign(ids=~CASE_ID,weights=~WAVE1_WT,strata=~VSTRATUM,data=npes)
#
#Calculate and display estimated percents and standard errors for Table 1
#
t1=svymean(~CAN_PREF_I,design=npes.design)
Table1=round(ftable(t1)*100, 2)
attr(Table1, "dimnames")[[1]] <-  gsub("CAN_PREF_I", "", attr(Table1, "dimnames")[[1]])
attr(Table1, "dimnames")[[2]] <-  c("Mean", "SE")
prn(Table1, "Table 1. Candidate Preference: 2012 NORC Presidential Election Study\n\n")
#
#Calculate and display estimated percents and standard errors for Table 1
#
t2=svyby( ~CAN_PREF_I, by = ~PARTY_AFFIL_R_I,design=npes.design,FUN=svymean)
Table2=round(ftable(t2)*100,2)
attr(Table2, "col.vars")[[1]] <-  gsub("CAN_PREF_I", "   ", attr(Table2, "col.vars")[[1]])
names(attr(Table2, "row.vars"))  <- c("", "") 
attr(Table2, "row.vars")[[2]]  <- c("Mean", "SE")
prn(Table2,"Table 2. Candidate Preference by Party Affiliation: 2012 NORC Presidential Election Study\n\n")
#
#Calculate and display estimated percents and standard errors for Table 3
#
t3<-svymean(~SOC_SEC_I,design=npes.design)
Table3=round(ftable(t3)*100, 2)
attr(Table3, "dimnames")[[1]] <-  gsub("SOC_SEC_I", "", attr(Table3, "dimnames")[[1]])
attr(Table3, "dimnames")[[2]] <-  c("Mean", "SE")
prn(Table3, "Table 3. Social Security Reform: 2012 NORC Presidential Election Study\n\n")
#
#Calculate and display estimated percents and standard errors for Table 4
#
npesII<-subset(npes,WAVE2_WT>0)
npesII.design<-svydesign(ids=~CASE_ID,weights=~WAVE2_WT,strata=~VSTRATUM,data=npesII)
t4<-svymean(~MED_FUTURE_II,design=npesII.design)
Table4=round(ftable(t4)*100, 2)
attr(Table4, "dimnames")[[1]] <-  gsub("MED_FUTURE_II", "", attr(Table4, "dimnames")[[1]])
attr(Table4, "dimnames")[[2]] <-  c("Mean", "SE")
prn(Table4, "Table 4. Medicare Reform: 2012 NORC Presidential Election Study\n\n")
#
#Calculate and display estimated percents and standard errors for Table 5
#
t5=svyby(~SOC_SEC_I,by=~AGE_CAT_I,design=npes.design, FUN=svymean)
Table5=round(ftable(t5)*100,2)
attr(Table5, "col.vars")[[1]] <-  gsub("SOC_SEC_I", "", attr(Table5, "col.vars")[[1]])
names(attr(Table5, "row.vars"))  <- c("", "") 
attr(Table5, "row.vars")[[2]]  <- c("Mean", "SE")
prn(Table5,"Table 5. Social Security Reform by Respondent Age: 2012 NORC Presidential Election Study\n\n")
#
#Calculate and display estimated percents and standard errors for Table 6
#
t6=svyby(~MED_FUTURE_II,by=~AGE_CAT_I,design=npesII.design, FUN=svymean)
Table6=round(ftable(t6)*100,2)
attr(Table6, "col.vars")[[1]] <-  gsub("MED_FUTURE_II", "", attr(Table6, "col.vars")[[1]])
names(attr(Table6, "row.vars"))  <- c("", "") 
attr(Table6, "row.vars")[[2]]  <- c("Mean", "SE")
prn(Table6,"Table 6. Medicare Reform by Respondent Age: 2012 NORC Presidential Election Study\n\n")
#
#Exit R
#
q()