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importing data and weighting
dtat=dta%>%
select("hhid" ,"hvidx","hv000","hv001","hv002" ,"hv003","hv005","hv011", "hv012", "hv021","hv023" ,"hv024", "hv025" ,"hv104" , "hv105" , "hv102","hv106","hv270" , "hv271" , "hv270a", "hv271a", "sh73","sh74a","sh74b", "sh74c" ,"sh74d" ,"sh74e" ,"sh74x", wt )Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
selecting only dejure members
Survey design# Not sure if the weights will work as they are not divded by 1000000
survey_design0=as_survey_design (
.data= dtat1,
ids = "hv001",
strata = "hv023",
weights = wt
)
options(survey.lonely.psu = "adjust")Recode:
survey_design0<- survey_design0%>% mutate(
residence = factor(hv025,
levels = c(1, 2),
labels = c("Urban",
"Rural")))
survey_design0<- survey_design0 %>% mutate(
sex=factor(hv104,
levels= c(1,2,9),
labels= c("Male",
"Female",
"Missing")))
survey_design0<- survey_design0%>% mutate(
Education =factor(hv106,
levels= c(0,1,2,3,8,9),
labels= c("No education, preschool",
"Primary",
"Secondary",
"Higher",
"Don't Know",
"Missing")))
survey_design0<- survey_design0%>% mutate(
Wealth =factor(hv270a,
levels= c(1,2,3,4,5),
labels= c("Poorest",
"Poorer",
"Middle",
"Richer",
"Richest")))
survey_design0 <-survey_design0%>% mutate(
State=factor(hv024,
levels=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36,37),
labels=c("jammu & kashmir",
"himachal pradesh ",
" punjab",
"chandigarh",
"uttarakhand",
"haryana ",
" nct of delhi",
"rajasthan ",
"uttar pradesh ",
" bihar ",
" sikkim ",
"arunachal pradesh ",
" nagaland ",
"manipur ",
" mizoram " ,
" tripura ",
"meghalaya ",
"assam ",
"west bengal",
"jharkhand ",
"odisha",
"chhattisgarh ",
"madhya pradesh ",
" gujarat ",
"dadra & nagar haveli and daman & diu ",
"maharashtra",
"andhra pradesh ",
" karnataka ",
" goa ",
"lakshadweep",
" kerala",
" tamil nadu",
"puducherry ",
"andaman & nicobar islands",
" telangana ",
" ladakh " )))
survey_design0<- survey_design0%>% mutate(
use_publicfacility = factor(sh73,
levels = c(11, 112,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,31,41,42,43,44,45,46,47,48,49,50,51,52,53,54,61,62,96),
labels = c("public: government / municipal hospital",
"public: government dispensary",
" public: uhc / uhp / ufwc",
"public: chc / rural hospital / block phc ",
"public: phc / additional phc ",
"public: sub-centre ",
"public ayush: ayurveda ",
"public ayush: yoga and naturopathy ",
"public ayush: unani ",
"public ayush: siddha ",
"public ayush: homeopathy ",
" public ayush: sowa rigpa (ttm) ",
"public ayush: other ",
"public: anganwadi/icds centre ",
" public: asha ",
" public: government mobile clinic ",
"other public sector ",
"ngo or trust hospital / clinic",
"private hospital ",
" private doctor / clinic ",
"private paramedic",
"private ayush: ayurveda ",
"private ayush: yoga and naturopathy ",
"private ayush: unani ",
"private ayush: siddha ",
"private ayush: homeopathy ",
"private ayush: sowa rigpa (ttm) ",
"private ayush: other ",
"private: traditional healer ",
"private: pharmacy / drugstore ",
"private: dai (tba) ",
"private: dai (tba) ",
"shop ",
"home treatment ",
"other ")))
survey_design0<- survey_design0 %>% mutate(
nonearby_facility = factor(sh74a,
levels = c(0, 1),
labels = c("No",
"Yes"
)))
survey_design0<- survey_design0%>% mutate(
timing_notconvenient = factor(sh74b,
levels = c(0, 1),
labels = c("No",
"Yes")))
survey_design0<- survey_design0%>% mutate(
healthpersonel_absent = factor(sh74c,
levels = c(0, 1),
labels = c("No",
"Yes")))
survey_design0<- survey_design0%>% mutate(
toolong_waitingtime= factor(sh74d,
levels = c(0, 1),
labels = c("No",
"Yes")))
survey_design0<- survey_design0%>% mutate(
poor_quality = factor(sh74e,
levels = c(0, 1),
labels = c("No",
"Yes")))
survey_design0<- survey_design0%>% mutate(
other = factor(sh74x,
levels = c(0, 1),
labels = c("No",
"Yes")))
#survey_design0<- survey_design0 %>% mutate (
#donotuse_ghf=factor(donotuse_ghf,
#levels=c(0,1),
#labels=c("No","Yes")))Check
View (survey_design0)survey_design0 %>%
filter(!is.na(sex)) %>%
filter(!is.na(residence)) %>%
group_by(sex,residence) %>%
summarise(mean = survey_mean(vartype = "ci"))survey_design0 %>%
filter(!is.na(State)) %>%
filter(!is.na(nonearby_facility)) %>%
group_by(State,nonearby_facility) %>%
summarise(mean = survey_mean(vartype = "ci"))