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#Create balance tables and histograms for Appendix
#package installation
# install.packages('plyr')
# install.packages('dplyr')
# install.packages('tidyr')
# install.packages('ggplot2')
# install.packages('lmtest')
# install.packages('multiwayvcov')
# install.packages('stargazer')
rm(list=ls())
library(plyr);library(dplyr, warn.conflicts = F)
library(tidyr)
library(ggplot2)
suppressMessages( library(lmtest) )
suppressMessages( library(multiwayvcov) )
suppressMessages(library(stargazer))
s = function(x){summary(factor(x))}
#setwd() #set working directory
dir.create(paste0(getwd(), '/Output/'))
dir.create(paste0(getwd(), '/Output/Balance-tables_histograms/'))
path0 = paste0(getwd(), '/Output/Balance-tables_histograms/', Sys.Date(),'/') #Directory for output files
dir.create(path0)
A = readRDS('4-20-20_HH-KNearest_DeID_demed.RDS') #
##############################################################################################################################
#Balance table
A = A[A$A.A7_Area.Neighborhood %in% names(which(table(A$A.A7_Area.Neighborhood) >= 30)),]#DROP PLACES WITH <30 OBSERVATIONS
CandConjoint = A %>% filter(Wave %in% c('Bangalore 2016','Jai-Pat 2015'), is.na(L.Candidate_Question_1) == F) #just the people in the analysis.
CandConjoint$HiSeg = CandConjoint$Nearest10_OwnReligion == 10
CandConjoint$HiSeg_DeMed = CandConjoint$DeMedNearest10_OwnReligion >= 0
CandConjoint$LoSeg_DeMed = CandConjoint$DeMedNearest10_OwnReligion < 0
CandConjoint$LowCaste = CandConjoint$C.C8_Caste == 'SC/ST/RM'
CandConjoint$Muslim = CandConjoint$C.C6_Religion == 'Muslim'
CandConjoint$Male = CandConjoint$C.C5_Gender == 1
CandConjoint$Migrant = CandConjoint$C.C14_Permanent.Residence.of.Jaipur. == 0
CandConjoint$Jaipur = CandConjoint$City == 'Jaipur'
CandConjoint$Patna = CandConjoint$City == 'Patna'
CandConjoint$C.C4_Age = as.numeric(as.character(CandConjoint$C.C4_Age))
bal.vars = c('AssetSum','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur','Patna')
bal.table = data.frame('Segregated' = apply(CandConjoint[CandConjoint$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
'Integrated' = apply(CandConjoint[!CandConjoint$HiSeg,bal.vars],2,function(x){mean(x,na.rm=T)}),
'p' = apply(CandConjoint[,bal.vars],2,function(x){t.test(x[CandConjoint$HiSeg],
x[!CandConjoint$HiSeg])[['p.value']]}) ) %>%
round(2)
bal.table = rbind(bal.table, data.frame('Segregated' = sum(CandConjoint$HiSeg == 1, na.rm = T),
'Integrated' = sum(CandConjoint$HiSeg == 0, na.rm = T), 'p' = ''))
row.names(bal.table) = c('Asset Index','Low Caste','Muslim','Male','Age','Migrant','Jaipur','Patna','n')
out = stargazer(bal.table, summary = F, digits = 2,
title = 'Balance Table, Segregated vs. Integrated',
label = 'table:Nearest10Religion_Balance')
writeLines(out,con = paste0(path0,'Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
#De-medianed
bal.vars = c('AssetSum','LowCaste','Muslim','Male','C.C4_Age', 'Migrant','Jaipur','Patna')
bal.table = data.frame('Segregated' = apply(CandConjoint[CandConjoint$HiSeg_DeMed,bal.vars],2,function(x){mean(x,na.rm=T)}),
'Integrated' = apply(CandConjoint[CandConjoint$LoSeg_DeMed,bal.vars],2,function(x){mean(x,na.rm=T)}),
'p' = apply(CandConjoint[,bal.vars],2,function(x){t.test(x[CandConjoint$HiSeg_DeMed],
x[CandConjoint$LoSeg_DeMed])[['p.value']]}) ) %>%
round(2)
bal.table = rbind(bal.table, data.frame('Segregated' = sum(CandConjoint$HiSeg_DeMed == 1, na.rm = T),
'Integrated' = sum(CandConjoint$LoSeg_DeMed == 1, na.rm = T), 'p' = ''))
row.names(bal.table) = c('Asset Index','Low Caste','Muslim','Male','Age','Migrant','Jaipur','Patna','n')
bal.table
out = stargazer(bal.table, summary = F, digits = 2,
title = 'Balance Table, Segregated vs. Integrated (De-Medianed)',
label = 'table:Nearest10Religion_Balance_DeMed')
writeLines(out,con = paste0(path0,'DeMed_Nearest10Religion_Balance.tex'));rm(out, bal.vars,bal.table)
########################################################################################
#Histograms
#Nearest 10 religion, full sample
ggplot(data=CandConjoint, aes(CandConjoint$Nearest10_OwnReligion)) + geom_bar(aes(y = (..count..)/sum(..count..))) + theme_minimal() +
labs(x = '10-nearest same religion', y = 'Proportion') + theme(axis.title=element_text(size=14),
axis.text = element_text(size = 12)) +
ggtitle('10-nearest same religion, Full sample') + theme(plot.title = element_text(hjust = 0.5, size = 16))
ggsave(filename = paste0(path0,'/Nearest10SameReligion.jpg'), height = 150, width = 150, units = 'mm')
#De-Medianned Nearest 10 religion, full sample
ggplot(data=CandConjoint, aes(CandConjoint$DeMedNearest10_OwnReligion)) + geom_bar(aes(y = (..count..)/sum(..count..))) + theme_minimal() +
labs(x = '10-nearest same religion (de-medianed)', y = 'Proportion') + theme(axis.title=element_text(size=14),
axis.text = element_text(size = 12)) +
ggtitle('De-Medianed 10-nearest same religion,\n Full sample') + theme(plot.title = element_text(hjust = 0.5, size = 16))
ggsave(filename = paste0(path0,'/DeMedNearest10SameReligion.jpg'), height = 150, width = 150, units = 'mm')
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