Datasets:

chuxuan
/

REPRO-Bench

#Table 4: Comparison of most common occupations for network and household data sets. 

#install packages
# install.packages('plyr')
# install.packages('dplyr')
# install.packages('tidyr')
# install.packages('ggplot2')
# install.packages('multiwayvcov')
# install.packages('lmtest')
# install.packages('stargazer')

rm(list=ls())

#setwd() #set working directory

library(plyr);library(dplyr, warn.conflicts = FALSE)
library(tidyr);library(ggplot2)
suppressMessages(library(multiwayvcov, warn.conflicts = F))
suppressMessages(library(lmtest, warn.conflicts = F))
suppressMessages(library(stargazer))

s = function(x){summary(factor(x))}

#Read network data
A = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
#Read and clean household sample data
H = read.csv('4-20-20_deid_nearestK.csv',
             na.strings=c('','NA'),strip.white=T,stringsAsFactors = F)
Hprime = H[H$A.A7_Area.Neighborhood %in% names(which(table(H$A.A7_Area.Neighborhood) >= 30)),]#Drop neighborhoods with < 30 observations
Hprime = Hprime[which(!is.na(Hprime$A.A7_Area.Neighborhood)),] #drop NA neighborhoods
Hprime = Hprime[which(Hprime$A.A7_Area.Neighborhood != 'Bangalore NA'),]
H = Hprime; rm(Hprime)

########################################################################################################################

############################################################################################################
#Map values to occupations
A$Job = mapvalues(A$D.D1_Occupation,
                  from = c(12,
                           13,
                           19,
                           2,
                           20,
                           21,
                           3,
                           4,
                           6,
                           7,
                           9),
                  to = c('Garbage',
                         'Gardener',
                         'Security',
                         'Butcher',
                         'Tailor',
                         'Vendor',
                         'Carpenter',
                         'Construction',
                         'Cook',
                         'Corporate', 
                         'Electrician'))

H$Job = mapvalues(H$D.D1_Occupation.,
                  from = c(1,
                           2,
                           3,
                           4,
                           5,
                           6,
                           7,
                           8,
                           9,
                           10,
                           11,
                           12,
                           13,
                           14,
                           15,
                           16,
                           17,
                           18,
                           19,
                           20,
                           21,
                           22,
                           23,
                           24,
                           25,
                           26, #B17: retired
                           27), #B17: unemployed
                  to = c('Agriculture',
                         'Butcher',
                         'Carpenter',
                         'Construction',
                         'Labour',
                         'Cook',
                         'Corporation', 
                         'Driver',
                         'Electrical',
                         'Factory',
                         'Flower',
                         'Garbage',
                         'Gardener',
                         'Maid',
                         'Mechanic',
                         'Painter',
                         'ProfessionalSvc',
                         'Grocessory',
                         'Security',
                         'Tailor',
                         'Vendor',
                         'Government',
                         'Housewife',
                         'Student',
                         'Other',
                         NA, NA )) #Map retired/unemployed to NA so they don't affect denominator

#Note that B17 includes retired/unemployed; these are mapped above to NA and dropped from table (and not included in denominator)

#Network
sort (s(A$Job) / sum(!is.na(A$Job )) * 100  , decreasing = T) %>% round(2)
#Household
sort (s(H$Job) / sum(!is.na(H$Job )) * 100  , decreasing = T) %>% round(2)
#These are basis for manually-created Table 4

################################################################################################

	#Table 4: Comparison of most common occupations for network and household data sets.

	#install packages
	# install.packages('plyr')
	# install.packages('dplyr')
	# install.packages('tidyr')
	# install.packages('ggplot2')
	# install.packages('multiwayvcov')
	# install.packages('lmtest')
	# install.packages('stargazer')

	rm(list=ls())

	#setwd() #set working directory

	library(plyr);library(dplyr, warn.conflicts = FALSE)
	library(tidyr);library(ggplot2)
	suppressMessages(library(multiwayvcov, warn.conflicts = F))
	suppressMessages(library(lmtest, warn.conflicts = F))
	suppressMessages(library(stargazer))

	s = function(x){summary(factor(x))}

	#Read network data
	A = readRDS('4-20-20_Network-KNearest_DeID_demed.RDS')
	#Read and clean household sample data
	H = read.csv('4-20-20_deid_nearestK.csv',
	na.strings=c('','NA'),strip.white=T,stringsAsFactors = F)
	Hprime = H[H$A.A7_Area.Neighborhood %in% names(which(table(H$A.A7_Area.Neighborhood) >= 30)),]#Drop neighborhoods with < 30 observations
	Hprime = Hprime[which(!is.na(Hprime$A.A7_Area.Neighborhood)),] #drop NA neighborhoods
	Hprime = Hprime[which(Hprime$A.A7_Area.Neighborhood != 'Bangalore NA'),]
	H = Hprime; rm(Hprime)

	########################################################################################################################

	############################################################################################################
	#Map values to occupations
	A$Job = mapvalues(A$D.D1_Occupation,
	from = c(12,
	13,
	19,
	2,
	20,
	21,
	3,
	4,
	6,
	7,
	9),
	to = c('Garbage',
	'Gardener',
	'Security',
	'Butcher',
	'Tailor',
	'Vendor',
	'Carpenter',
	'Construction',
	'Cook',
	'Corporate',
	'Electrician'))

	H$Job = mapvalues(H$D.D1_Occupation.,
	from = c(1,
	2,
	3,
	4,
	5,
	6,
	7,
	8,
	9,
	10,
	11,
	12,
	13,
	14,
	15,
	16,
	17,
	18,
	19,
	20,
	21,
	22,
	23,
	24,
	25,
	26, #B17: retired
	27), #B17: unemployed
	to = c('Agriculture',
	'Butcher',
	'Carpenter',
	'Construction',
	'Labour',
	'Cook',
	'Corporation',
	'Driver',
	'Electrical',
	'Factory',
	'Flower',
	'Garbage',
	'Gardener',
	'Maid',
	'Mechanic',
	'Painter',
	'ProfessionalSvc',
	'Grocessory',
	'Security',
	'Tailor',
	'Vendor',
	'Government',
	'Housewife',
	'Student',
	'Other',
	NA, NA )) #Map retired/unemployed to NA so they don't affect denominator

	#Note that B17 includes retired/unemployed; these are mapped above to NA and dropped from table (and not included in denominator)

	#Network
	sort (s(A$Job) / sum(!is.na(A$Job )) * 100 , decreasing = T) %>% round(2)
	#Household
	sort (s(H$Job) / sum(!is.na(H$Job )) * 100 , decreasing = T) %>% round(2)
	#These are basis for manually-created Table 4

	################################################################################################