library(survey)
## Warning: package 'survey' was built under R version 4.1.3
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
## 
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
## 
##     dotchart
library(readxl)
library(caret)
## Loading required package: ggplot2
## Loading required package: lattice
## 
## Attaching package: 'caret'
## The following object is masked from 'package:survival':
## 
##     cluster
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(stringr)
sakernas2020_diy <- read_excel("sak0220(edit B5_R17A).xlsx")
sus1 <- read_excel("susenas 2020.xlsx", sheet = "RT")
sus2 <- read_excel("susenas 2020.xlsx", sheet = "Indo")
susenas_diy = merge(x = sus1, y = sus2, all.y = TRUE)
susenas_diy.15 <- subset(susenas_diy, R407>14)
sakernas2020_diy['KODE_KAB']=str_pad(sakernas2020_diy$KODE_KAB, width = 2, side = 'left', pad = '0')
sakernas2020_diy['NO_DSRT']=str_pad(sakernas2020_diy$NO_DSRT, width = 2, side = 'left', pad = '0')
sakernas2020_diy["psu"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$nks_ok, sep = "")
sakernas2020_diy["ssu"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$nks_ok, sakernas2020_diy$NO_DSRT, sep = "")
sakernas2020_diy["strata"]=paste(sakernas2020_diy$KODE_PROV, sakernas2020_diy$KODE_KAB, sakernas2020_diy$KLASIFIKAS, sep = "")
sakernas2020_diy$B4_K6=as.factor(sakernas2020_diy$B4_K6)
sakernas2020_diy$B4_K9=as.factor(sakernas2020_diy$B4_K9)
sakernas2020_diy$B4_K10=as.factor(sakernas2020_diy$B4_K10)
sakernas2020_diy$B5_R1A=as.factor(sakernas2020_diy$B5_R1A)
sakernas2020_diy$B5_R4A=as.factor(sakernas2020_diy$B5_R4A)
sakernas2020_diy$B5_R4B=as.factor(sakernas2020_diy$B5_R4B)
sakernas2020_diy$B5_R4C=as.factor(sakernas2020_diy$B5_R4C)
sakernas2020_diy$B5_R4D=as.factor(sakernas2020_diy$B5_R4D)
sakernas2020_diy$B5_R4E=as.factor(sakernas2020_diy$B5_R4E)
sakernas2020_diy$B5_R4F=as.factor(sakernas2020_diy$B5_R4F)
sakernas2020_diy$B5_R5A1=as.factor(sakernas2020_diy$B5_R5A1)
sakernas2020_diy$B5_R5A2=as.factor(sakernas2020_diy$B5_R5A2)
sakernas2020_diy$B5_R5A3=as.factor(sakernas2020_diy$B5_R5A3)
sakernas2020_diy$B5_R5A4=as.factor(sakernas2020_diy$B5_R5A4)
sakernas2020_diy$B5_R5B=as.factor(sakernas2020_diy$B5_R5B)
sakernas2020_diy$B5_R6=as.factor(sakernas2020_diy$B5_R6)
sakernas2020_diy$B5_R20_KAT=as.factor(sakernas2020_diy$B5_R20_KAT)
sakernas2020_diy$B5_R24A=as.factor(sakernas2020_diy$B5_R24A)
sakernas2020_diy$B5_R30A=as.factor(sakernas2020_diy$B5_R30A)
sakernas2020_diy$B5_R1F=as.factor(sakernas2020_diy$B5_R1F)
sakernas2020_diy$B5_R6=as.factor(sakernas2020_diy$B5_R6)
sakernas2020_diy$B5_R7A=as.factor(sakernas2020_diy$B5_R7A)
sakernas2020_diy$B5_R7B=as.factor(sakernas2020_diy$B5_R7B)
sakernas2020_diy$B5_R9=as.factor(sakernas2020_diy$B5_R9)
sakernas2020_diy$B5_R10=as.factor(sakernas2020_diy$B5_R10)
sakernas2020_diy$B5_R12A=as.factor(sakernas2020_diy$B5_R12A)
sakernas2020_diy$B5_R12B=as.factor(sakernas2020_diy$B5_R12B)
sakernas2020_diy$B5_R13A=as.factor(sakernas2020_diy$B5_R13A)
sakernas2020_diy$B5_R13B=as.factor(sakernas2020_diy$B5_R13B)
sakernas2020_diy$B5_R17A=as.factor(sakernas2020_diy$B5_R17A)
sakernas2020_diy$B5_R17B=as.factor(sakernas2020_diy$B5_R17B)
sakernas2020_diy$B5_R21_KJI=as.factor(sakernas2020_diy$B5_R21_KJI)
sakernas2020_diy$B5_R21_KBJ=as.factor(sakernas2020_diy$B5_R21_KBJ)
sakernas2020_diy$B5_R22A=as.factor(sakernas2020_diy$B5_R22A)
sakernas2020_diy$B5_R24B=as.factor(sakernas2020_diy$B5_R24B)
sakernas2020_diy$B5_R24C1=as.factor(sakernas2020_diy$B5_R24C1)
sakernas2020_diy$B5_R24C2=as.factor(sakernas2020_diy$B5_R24C2)
sakernas2020_diy$B5_R24D=as.factor(sakernas2020_diy$B5_R24D)
sakernas2020_diy$B5_R25A1=as.factor(sakernas2020_diy$B5_R25A1)
sakernas2020_diy$B5_R25A2=as.factor(sakernas2020_diy$B5_R25A2)
sakernas2020_diy$B5_R25A3=as.factor(sakernas2020_diy$B5_R25A3)
sakernas2020_diy$B5_R25B=as.factor(sakernas2020_diy$B5_R25B)
sakernas2020_diy$B5_R25C1=as.factor(sakernas2020_diy$B5_R25C1)
sakernas2020_diy$B5_R25C2=as.factor(sakernas2020_diy$B5_R25C2)
sakernas2020_diy$B5_R25C3=as.factor(sakernas2020_diy$B5_R25C3)
sakernas2020_diy$B5_R25C4=as.factor(sakernas2020_diy$B5_R25C4)
sakernas2020_diy$B5_R25C5=as.factor(sakernas2020_diy$B5_R25C5)
sakernas2020_diy$B5_R26=as.factor(sakernas2020_diy$B5_R26)
sakernas2020_diy$B5_R27=as.factor(sakernas2020_diy$B5_R27)
sakernas2020_diy$B5_R29=as.factor(sakernas2020_diy$B5_R29)
sakernas2020_diy$B5_R30A=as.factor(sakernas2020_diy$B5_R30A)
sakernas2020_diy$B5_R30B=as.factor(sakernas2020_diy$B5_R30B)
sakernas2020_diy$B5_R30C=as.factor(sakernas2020_diy$B5_R30C)
sakernas2020_diy$B5_R30D=as.factor(sakernas2020_diy$B5_R30D)
sakernas2020_diy$B5_R30E=as.factor(sakernas2020_diy$B5_R30E)
sakernas2020_diy$B5_R30F=as.factor(sakernas2020_diy$B5_R30F)
sakernas2020_diy$B5_R31=as.factor(sakernas2020_diy$B5_R31)
sakernas2020_diy$B5_R34=as.factor(sakernas2020_diy$B5_R34)
sakernas2020_diy$B5_R36E=as.factor(sakernas2020_diy$B5_R36E)
sakernas2020_diy$B5_R47=as.factor(sakernas2020_diy$B5_R47)
sakernas2020_diy$B5_R48=as.factor(sakernas2020_diy$B5_R48)

#Susenas #kalo susenas weight nya tetap gaada weight adjusted

susenas.kulon=filter(susenas_diy.15, KODE_KAB=="1")
susenas.bantul=filter(susenas_diy.15, KODE_KAB=="2")
susenas.gunung=filter(susenas_diy.15, KODE_KAB=="3")
susenas.sleman=filter(susenas_diy.15, KODE_KAB=="4")
susenas.jogja=filter(susenas_diy.15, KODE_KAB=="71")

#1. Kapita dan Status Penduduk Miskin

options(survey.lonely.psu = "adjust")
des12 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas_diy.15)
des12.1 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.kulon)
des12.2 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.bantul)
des12.3 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.gunung)
des12.4 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.sleman)
des12.5 = svydesign(ids=~PSU+SSU, strata=~STRATA, weights=~FWT, data = susenas.jogja)

svymean(~KAPITA, des12)
##           mean    SE
## KAPITA 1476320 42204
svymean(~KAPITA, des12.1)
##          mean    SE
## KAPITA 972560 56462
svymean(~KAPITA, des12.2)
##           mean    SE
## KAPITA 1407380 75401
svymean(~KAPITA, des12.3)
##          mean    SE
## KAPITA 893632 44251
svymean(~KAPITA, des12.4)
##           mean     SE
## KAPITA 1882128 107899
svymean(~KAPITA, des12.5)
##           mean     SE
## KAPITA 1981858 115799
svymean(~factor(STATUS), des12)
##                    mean     SE
## factor(STATUS)1 0.05026 0.0051
## factor(STATUS)2 0.94974 0.0051
svymean(~factor(STATUS), des12.1)
##                     mean    SE
## factor(STATUS)1 0.083258 0.012
## factor(STATUS)2 0.916742 0.012
svymean(~factor(STATUS), des12.2)
##                   mean     SE
## factor(STATUS)1 0.0563 0.0103
## factor(STATUS)2 0.9437 0.0103
svymean(~factor(STATUS), des12.3)
##                     mean     SE
## factor(STATUS)1 0.042956 0.0126
## factor(STATUS)2 0.957044 0.0126
svymean(~factor(STATUS), des12.4)
##                     mean     SE
## factor(STATUS)1 0.022013 0.0078
## factor(STATUS)2 0.977987 0.0078
svymean(~factor(STATUS), des12.5)
##                     mean     SE
## factor(STATUS)1 0.094743 0.0188
## factor(STATUS)2 0.905257 0.0188

#RSE dan Confident Interval Status Penduduk Miskin

(0.0051/0.05026)
## [1] 0.1014723
(0.0051/0.94974)
## [1] 0.005369891
(0.012/0.083258)
## [1] 0.1441303
(0.012/0.916742)
## [1] 0.01308983
(0.0103/0.0563)
## [1] 0.1829485
(0.0103/0.9437)
## [1] 0.01091449
(0.0126/0.042956)
## [1] 0.2933234
(0.0126/0.957044)
## [1] 0.01316554
(0.0078/0.022013)
## [1] 0.3543361
(0.0078/0.977987)
## [1] 0.007975566
(0.0188/0.094743)
## [1] 0.1984315
(0.0188/0.905257)
## [1] 0.02076758
confint(svymean(~factor(STATUS), des12), level = 0.95)
##                      2.5 %     97.5 %
## factor(STATUS)1 0.04027963 0.06023982
## factor(STATUS)2 0.93976018 0.95972037
confint(svymean(~factor(STATUS), des12.1), level = 0.95)
##                      2.5 %    97.5 %
## factor(STATUS)1 0.05968193 0.1068338
## factor(STATUS)2 0.89316624 0.9403181
confint(svymean(~factor(STATUS), des12.2), level = 0.95)
##                      2.5 %     97.5 %
## factor(STATUS)1 0.03610498 0.07649544
## factor(STATUS)2 0.92350456 0.96389502
confint(svymean(~factor(STATUS), des12.3), level = 0.95)
##                      2.5 %     97.5 %
## factor(STATUS)1 0.01831113 0.06760023
## factor(STATUS)2 0.93239977 0.98168887
confint(svymean(~factor(STATUS), des12.4), level = 0.95)
##                       2.5 %     97.5 %
## factor(STATUS)1 0.006736898 0.03728824
## factor(STATUS)2 0.962711764 0.99326310
confint(svymean(~factor(STATUS), des12.5), level = 0.95)
##                      2.5 %    97.5 %
## factor(STATUS)1 0.05799095 0.1314945
## factor(STATUS)2 0.86850553 0.9420090

#RSE dan Confident Interval Pengeluaran Rata-Rata Perkapita

(rse_kap=42204/1476320)
## [1] 0.0285873
(rse_kap.1=56462/972560)
## [1] 0.05805503
(rse_kap.2=75401/1407380)
## [1] 0.05357544
(rse_kap.3=44251/893632)
## [1] 0.04951815
(rse_kap.4=107899/1882128)
## [1] 0.05732819
(rse_kap.5=115799/1981858)
## [1] 0.05842951
confint(svymean(~KAPITA, des12), level = 0.95)
##          2.5 %  97.5 %
## KAPITA 1393602 1559039
confint(svymean(~KAPITA, des12.1), level = 0.95)
##           2.5 %  97.5 %
## KAPITA 861897.1 1083223
confint(svymean(~KAPITA, des12.2), level = 0.95)
##          2.5 %  97.5 %
## KAPITA 1259597 1555162
confint(svymean(~KAPITA, des12.3), level = 0.95)
##           2.5 %   97.5 %
## KAPITA 806901.5 980361.8
confint(svymean(~KAPITA, des12.4), level = 0.95)
##          2.5 %  97.5 %
## KAPITA 1670650 2093606
confint(svymean(~KAPITA, des12.5), level = 0.95)
##          2.5 %  97.5 %
## KAPITA 1754896 2208821

#Sakernas

#Variabel bekerja, pengangguran, , angkatan kerja, TPT, dan TPAK (Skenario 1)

#Filter buat TPAK
sakernas2020_diy$bekerja <- ifelse(sakernas2020_diy$B5_R5A1 == "1" | sakernas2020_diy$B5_R6=="1", 1, 0)

sakernas2020_diy$pengangguran <- ifelse(sakernas2020_diy$B5_R5A1=="2" & 
                                          sakernas2020_diy$B5_R6=="2" & 
                                          sakernas2020_diy$B5_R12A=="1" | 
                                          sakernas2020_diy$B5_R5A1=="2" & 
                                          sakernas2020_diy$B5_R6=="2" & 
                                          sakernas2020_diy$B5_R12A=="2" & 
                                          sakernas2020_diy$B5_R12B=="1" | 
                                          sakernas2020_diy$B5_R5A1=="2" & 
                                          sakernas2020_diy$B5_R6=="2" & 
                                          sakernas2020_diy$B5_R12A=="2" & 
                                          sakernas2020_diy$B5_R12B=="2" &
                                          between(sakernas2020_diy$B5_R17A, 0, 4), 1, 0)
## Warning: between() called on numeric vector with S3 class
sakernas2020_diy$TPAK = ifelse(sakernas2020_diy$pengangguran=="1"| sakernas2020_diy$bekerja=="1", 1, 0)

#FIlter buat TPT
sakernas2020_diy$TPT[sakernas2020_diy$pengangguran=="1"]=1
## Warning: Unknown or uninitialised column: `TPT`.
sakernas2020_diy$TPT[sakernas2020_diy$bekerja=="1"]=0

sakernas = sakernas2020_diy %>% filter(TPT==0 | TPT==1) 
a1=filter(sakernas, KODE_KAB=="01")
a2=filter(sakernas, KODE_KAB=="02")
a3=filter(sakernas, KODE_KAB=="03")
a4=filter(sakernas, KODE_KAB=="04")
a5=filter(sakernas, KODE_KAB=="71")

b1=filter(sakernas2020_diy, KODE_KAB=="01")
b2=filter(sakernas2020_diy, KODE_KAB=="02")
b3=filter(sakernas2020_diy, KODE_KAB=="03")
b4=filter(sakernas2020_diy, KODE_KAB=="04")
b5=filter(sakernas2020_diy, KODE_KAB=="71")
des15 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas)
des15.1=svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = a1)
des15.2=svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = a2)
des15.3=svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = a3)
des15.4=svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = a4)
des15.5=svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = a5)

des16 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas2020_diy)
des16.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = b1)
des16.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = b2)
des16.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = b3)
des16.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = b4)
des16.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = b5)

#Provinsi DIY

svytotal(~pengangguran, des15)
##              total    SE
## pengangguran 74547 11871
svytotal(~bekerja, des16)
##           total    SE
## bekerja 2130489 74967
svytotal(~TPT, des15)
##     total    SE
## TPT 74547 11871
svymean(~TPT, des15)
##         mean     SE
## TPT 0.033808 0.0052
svymean(~TPAK, des16)
##        mean     SE
## TPAK 0.7079 0.0128
svytotal(~TPAK, des16)
##        total    SE
## TPAK 2205036 76981
(11871/74547)
## [1] 0.1592418
(74967/2130489)
## [1] 0.0351877
(0.0052/0.033808)
## [1] 0.1538097
(0.0128/0.7079)
## [1] 0.01808165
(76981/2205036)
## [1] 0.03491145
confint(svytotal(~bekerja, des16))
##           2.5 %  97.5 %
## bekerja 1983556 2277422
confint(svytotal(~pengangguran, des15))
##                 2.5 %   97.5 %
## pengangguran 51280.87 97813.13
confint(svytotal(~TPAK, des16))
##        2.5 %  97.5 %
## TPAK 2054156 2355916
confint(svymean(~TPT, des15))
##          2.5 %     97.5 %
## TPT 0.02357308 0.04404214
confint(svymean(~TPAK, des16))
##          2.5 %    97.5 %
## TPAK 0.6827546 0.7330366

#Kabupaten Kulonprogo

svytotal(~pengangguran, des15.1)
##              total     SE
## pengangguran  5977 2425.4
svytotal(~bekerja, des16.1)
##          total    SE
## bekerja 364123 18897
svytotal(~TPT, des15.1)
##     total     SE
## TPT  5977 2425.4
svymean(~TPT, des15.1)
##        mean     SE
## TPT 0.01615 0.0063
svymean(~TPAK, des16.1)
##         mean     SE
## TPAK 0.74772 0.0263
svytotal(~TPAK, des16.1)
##       total    SE
## TPAK 370100 19525
(2425.4/5977)
## [1] 0.4057889
(18897/364123)
## [1] 0.0518973
(0.0063/0.01615)
## [1] 0.3900929
(0.0263/0.74772)
## [1] 0.03517359
(19525/370100)
## [1] 0.05275601
confint(svytotal(~bekerja, des16.1))
##            2.5 %   97.5 %
## bekerja 327085.9 401160.1
confint(svytotal(~pengangguran, des15.1))
##                 2.5 %   97.5 %
## pengangguran 1223.222 10730.78
confint(svytotal(~TPAK, des16.1))
##         2.5 %   97.5 %
## TPAK 331830.9 408369.1
confint(svymean(~TPT, des15.1))
##          2.5 %     97.5 %
## TPT 0.00373323 0.02856615
confint(svymean(~TPAK, des16.1))
##          2.5 %    97.5 %
## TPAK 0.6962002 0.7992439

#Kabupaten Bantul

svytotal(~pengangguran, des15.2)
##              total     SE
## pengangguran 22359 6597.3
svytotal(~bekerja, des16.2)
##          total    SE
## bekerja 531182 47518
svytotal(~TPT, des15.2)
##     total     SE
## TPT 22359 6597.3
svymean(~TPT, des15.2)
##         mean     SE
## TPT 0.040393 0.0115
svymean(~TPAK, des16.2)
##         mean     SE
## TPAK 0.72071 0.0358
svytotal(~TPAK, des16.2)
##       total    SE
## TPAK 553541 48899
(6597.3/22359)
## [1] 0.2950624
(47518/531182)
## [1] 0.0894571
(0.0115/0.040393)
## [1] 0.2847028
(0.0358/0.72071)
## [1] 0.04967324
(48899/553541)
## [1] 0.08833853
confint(svytotal(~bekerja, des16.2))
##            2.5 %   97.5 %
## bekerja 438048.3 624315.7
confint(svytotal(~pengangguran, des15.2))
##                 2.5 %   97.5 %
## pengangguran 9428.474 35289.53
confint(svytotal(~TPAK, des16.2))
##         2.5 %   97.5 %
## TPAK 457700.8 649381.2
confint(svymean(~TPT, des15.2))
##          2.5 %     97.5 %
## TPT 0.01791784 0.06286751
confint(svymean(~TPAK, des16.2))
##          2.5 %    97.5 %
## TPAK 0.6505378 0.7908795

#Kabupaten Gunung Kidul

svytotal(~pengangguran, des15.3)
##              total     SE
## pengangguran  8913 2856.8
svytotal(~bekerja, des16.3)
##          total    SE
## bekerja 432716 18294
svytotal(~TPT, des15.3)
##     total     SE
## TPT  8913 2856.8
svymean(~TPT, des15.3)
##         mean     SE
## TPT 0.020182 0.0065
svymean(~TPAK, des16.3)
##         mean     SE
## TPAK 0.76821 0.0152
svytotal(~TPAK, des16.3)
##       total    SE
## TPAK 441629 18225
(2856.8/8913)
## [1] 0.3205206
(18294/432716)
## [1] 0.04227715
(0.0065/0.020182)
## [1] 0.3220692
(0.0152/0.76821)
## [1] 0.01978626
(18225/441629)
## [1] 0.04126767
confint(svytotal(~bekerja, des16.3))
##          2.5 % 97.5 %
## bekerja 396861 468571
confint(svytotal(~pengangguran, des15.3))
##                 2.5 %  97.5 %
## pengangguran 3313.697 14512.3
confint(svytotal(~TPAK, des16.3))
##         2.5 %   97.5 %
## TPAK 405909.2 477348.8
confint(svymean(~TPT, des15.3))
##           2.5 %     97.5 %
## TPT 0.007486726 0.03287747
confint(svymean(~TPAK, des16.3))
##         2.5 %    97.5 %
## TPAK 0.738364 0.7980655

#Kabupaten Sleman

svytotal(~pengangguran, des15.4)
##              total     SE
## pengangguran 23214 7788.8
svytotal(~bekerja, des16.4)
##          total    SE
## bekerja 475976 42138
svytotal(~TPT, des15.4)
##     total     SE
## TPT 23214 7788.8
svymean(~TPT, des15.4)
##         mean     SE
## TPT 0.046503 0.0149
svymean(~TPAK, des16.4)
##         mean     SE
## TPAK 0.67681 0.0241
svytotal(~TPAK, des16.4)
##       total    SE
## TPAK 499190 43864
(7788.8/23214)
## [1] 0.3355217
(42138/475976)
## [1] 0.08852967
(0.0149/0.046503)
## [1] 0.3204094
(0.0241/0.67681)
## [1] 0.03560822
(43864/499190)
## [1] 0.08787035
confint(svytotal(~bekerja, des16.4))
##            2.5 %   97.5 %
## bekerja 393387.4 558564.6
confint(svytotal(~pengangguran, des15.4))
##                 2.5 %   97.5 %
## pengangguran 7948.324 38479.68
confint(svytotal(~TPAK, des16.4))
##         2.5 %   97.5 %
## TPAK 413218.9 585161.1
confint(svymean(~TPT, des15.4))
##          2.5 %     97.5 %
## TPT 0.01735816 0.07564851
confint(svymean(~TPAK, des16.4))
##          2.5 %    97.5 %
## TPAK 0.6295413 0.7240805

#Kota Yogyakarta

svytotal(~pengangguran, des15.5)
##              total     SE
## pengangguran 14084 4762.3
svytotal(~bekerja, des16.5)
##          total    SE
## bekerja 326492 29913
svytotal(~TPT, des15.5)
##     total     SE
## TPT 14084 4762.3
svymean(~TPT, des15.5)
##         mean     SE
## TPT 0.041353 0.0141
svymean(~TPAK, des16.5)
##         mean     SE
## TPAK 0.63133 0.0314
svytotal(~TPAK, des16.5)
##       total    SE
## TPAK 340576 29959
(4762.3/14084)
## [1] 0.3381355
(29913/326492)
## [1] 0.0916194
(0.0141/0.041353)
## [1] 0.3409668
(0.0314/0.63133)
## [1] 0.04973627
(29959/340576)
## [1] 0.08796568
confint(svytotal(~bekerja, des16.5))
##            2.5 %   97.5 %
## bekerja 267864.5 385119.5
confint(svytotal(~pengangguran, des15.5))
##                 2.5 %   97.5 %
## pengangguran 4750.049 23417.95
confint(svytotal(~TPAK, des16.5))
##         2.5 %   97.5 %
## TPAK 281857.2 399294.8
confint(svymean(~TPT, des15.5))
##          2.5 %    97.5 %
## TPT 0.01365764 0.0690493
confint(svymean(~TPAK, des16.5))
##          2.5 %    97.5 %
## TPAK 0.5697034 0.6929589

#Variabel bekerja, pengangguran, , angkatan kerja, TPT, dan TPAK (Skenario 2)

sakernas2020_diy$jk=ifelse(sakernas2020_diy$B5_R5A1 == "1" | sakernas2020_diy$B5_R6=="1", 1, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="1" |sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="1" | sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & between(sakernas2020_diy$B5_R17A, 0, 4), 2, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="2" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 4, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="3" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 5, ifelse(sakernas2020_diy$B5_R5A1=="2" & sakernas2020_diy$B5_R5B=="4" & sakernas2020_diy$B5_R6=="2" & sakernas2020_diy$B5_R12A=="2" & sakernas2020_diy$B5_R12B=="2" & sakernas2020_diy$B5_R17A=="4", 6, 0)))))
## Warning: between() called on numeric vector with S3 class
sakernas2020_diy$TPAK = ifelse(sakernas2020_diy$jk == "1" | sakernas2020_diy$jk=="2", 1, 0)

sakernas2020_diy$bekerja = ifelse(sakernas2020_diy$jk == "1", 1, 0)

sakernas5 = sakernas2020_diy %>% filter(jk=="1" | jk=="2")
sakernas5$TPT[sakernas5$jk=="2"]=1
sakernas5$TPT[sakernas5$jk=="1"]=0

des7.3.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas5)
des7.3.6 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas2020_diy)
c1=filter(sakernas5, KODE_KAB=="01")
c2=filter(sakernas5, KODE_KAB=="02")
c3=filter(sakernas5, KODE_KAB=="03")
c4=filter(sakernas5, KODE_KAB=="04")
c5=filter(sakernas5, KODE_KAB=="71")

d1=filter(sakernas2020_diy, KODE_KAB=="01")
d2=filter(sakernas2020_diy, KODE_KAB=="02")
d3=filter(sakernas2020_diy, KODE_KAB=="03")
d4=filter(sakernas2020_diy, KODE_KAB=="04")
d5=filter(sakernas2020_diy, KODE_KAB=="71")

des7.3.4_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = c1)
des7.3.6_1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = d1)
des7.3.4_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = c2)
des7.3.6_2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = d2)
des7.3.4_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = c3)
des7.3.6_3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = d3)
des7.3.4_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = c4)
des7.3.6_4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = d4)
des7.3.4_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = c5)
des7.3.6_5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = d5)

#Provinsi DIY

svytotal(~TPT, des7.3.4)
##     total    SE
## TPT 74547 11871
svymean(~TPT, des7.3.4)
##         mean     SE
## TPT 0.033808 0.0052
svytotal(~bekerja, des7.3.6)
##           total    SE
## bekerja 2130489 74967
svytotal(~TPAK, des7.3.6)
##        total    SE
## TPAK 2205036 76981
svymean(~TPAK, des7.3.6)
##        mean     SE
## TPAK 0.7079 0.0128
(11871/74547)
## [1] 0.1592418
(0.0052/0.033808)
## [1] 0.1538097
(74967/2130489)
## [1] 0.0351877
(76981/2205036)
## [1] 0.03491145
(0.0128/0.7079)
## [1] 0.01808165
confint(svytotal(~bekerja, des7.3.6), level = 0.95)
##           2.5 %  97.5 %
## bekerja 1983556 2277422
confint(svytotal(~TPT, des7.3.4), level = 0.95)
##        2.5 %   97.5 %
## TPT 51280.87 97813.13
confint(svytotal(~TPAK, des7.3.6), level = 0.95)
##        2.5 %  97.5 %
## TPAK 2054156 2355916
confint(svymean(~TPAK, des7.3.6), level = 0.95)
##          2.5 %    97.5 %
## TPAK 0.6827546 0.7330366
confint(svymean(~TPT, des7.3.4), level = 0.95)
##          2.5 %     97.5 %
## TPT 0.02357308 0.04404214

#Kabupaten Kulonprogo

svytotal(~TPT, des7.3.4_1)
##     total     SE
## TPT  5977 2425.4
svymean(~TPT, des7.3.4_1)
##        mean     SE
## TPT 0.01615 0.0063
svytotal(~bekerja, des7.3.6_1)
##          total    SE
## bekerja 364123 18897
svytotal(~TPAK, des7.3.6_1)
##       total    SE
## TPAK 370100 19525
svymean(~TPAK, des7.3.6_1)
##         mean     SE
## TPAK 0.74772 0.0263
(2425.4/5977)
## [1] 0.4057889
(0.0063/0.01615)
## [1] 0.3900929
(18897/364123)
## [1] 0.0518973
(19525/370100)
## [1] 0.05275601
(0.0263/0.74772)
## [1] 0.03517359
confint(svytotal(~bekerja, des7.3.6_1), level = 0.95)
##            2.5 %   97.5 %
## bekerja 327085.9 401160.1
confint(svytotal(~TPT, des7.3.4_1), level = 0.95)
##        2.5 %   97.5 %
## TPT 1223.222 10730.78
confint(svytotal(~TPAK, des7.3.6_1), level = 0.95)
##         2.5 %   97.5 %
## TPAK 331830.9 408369.1
confint(svymean(~TPAK, des7.3.6_1), level = 0.95)
##          2.5 %    97.5 %
## TPAK 0.6962002 0.7992439
confint(svymean(~TPT, des7.3.4_1), level = 0.95)
##          2.5 %     97.5 %
## TPT 0.00373323 0.02856615

#Kabupaten Bantul

svytotal(~TPT, des7.3.4_2)
##     total     SE
## TPT 22359 6597.3
svymean(~TPT, des7.3.4_2)
##         mean     SE
## TPT 0.040393 0.0115
svytotal(~bekerja, des7.3.6_2)
##          total    SE
## bekerja 531182 47518
svytotal(~TPAK, des7.3.6_2)
##       total    SE
## TPAK 553541 48899
svymean(~TPAK, des7.3.6_2)
##         mean     SE
## TPAK 0.72071 0.0358
(6597.3/22359)
## [1] 0.2950624
(0.0115/0.040393)
## [1] 0.2847028
(47518/531182)
## [1] 0.0894571
(48899/553541)
## [1] 0.08833853
(0.0358/0.72071)
## [1] 0.04967324
confint(svytotal(~bekerja, des7.3.6_2), level = 0.95)
##            2.5 %   97.5 %
## bekerja 438048.3 624315.7
confint(svytotal(~TPT, des7.3.4_2), level = 0.95)
##        2.5 %   97.5 %
## TPT 9428.474 35289.53
confint(svytotal(~TPAK, des7.3.6_2), level = 0.95)
##         2.5 %   97.5 %
## TPAK 457700.8 649381.2
confint(svymean(~TPAK, des7.3.6_2), level = 0.95)
##          2.5 %    97.5 %
## TPAK 0.6505378 0.7908795
confint(svymean(~TPT, des7.3.4_2), level = 0.95)
##          2.5 %     97.5 %
## TPT 0.01791784 0.06286751

#Kabupaten Gunung Kidul

svytotal(~TPT, des7.3.4_3)
##     total     SE
## TPT  8913 2856.8
svymean(~TPT, des7.3.4_3)
##         mean     SE
## TPT 0.020182 0.0065
svytotal(~bekerja, des7.3.6_3)
##          total    SE
## bekerja 432716 18294
svytotal(~TPAK, des7.3.6_3)
##       total    SE
## TPAK 441629 18225
svymean(~TPAK, des7.3.6_3)
##         mean     SE
## TPAK 0.76821 0.0152
(2856.8/8913)
## [1] 0.3205206
(0.0065/0.020182)
## [1] 0.3220692
(18294/432716)
## [1] 0.04227715
(18225/441629)
## [1] 0.04126767
(0.0152/0.76821)
## [1] 0.01978626
confint(svytotal(~bekerja, des7.3.6_3), level = 0.95)
##          2.5 % 97.5 %
## bekerja 396861 468571
confint(svytotal(~TPT, des7.3.4_3), level = 0.95)
##        2.5 %  97.5 %
## TPT 3313.697 14512.3
confint(svytotal(~TPAK, des7.3.6_3), level = 0.95)
##         2.5 %   97.5 %
## TPAK 405909.2 477348.8
confint(svymean(~TPAK, des7.3.6_3), level = 0.95)
##         2.5 %    97.5 %
## TPAK 0.738364 0.7980655
confint(svymean(~TPT, des7.3.4_3), level = 0.95)
##           2.5 %     97.5 %
## TPT 0.007486726 0.03287747

#Kabupaten Sleman

svytotal(~TPT, des7.3.4_4)
##     total     SE
## TPT 23214 7788.8
svymean(~TPT, des7.3.4_4)
##         mean     SE
## TPT 0.046503 0.0149
svytotal(~bekerja, des7.3.6_4)
##          total    SE
## bekerja 475976 42138
svytotal(~TPAK, des7.3.6_4)
##       total    SE
## TPAK 499190 43864
svymean(~TPAK, des7.3.6_4)
##         mean     SE
## TPAK 0.67681 0.0241
(7788.8/23214)
## [1] 0.3355217
(0.0149/0.046503)
## [1] 0.3204094
(42138/475976)
## [1] 0.08852967
(43864/499190)
## [1] 0.08787035
(0.0241/0.67681)
## [1] 0.03560822
confint(svytotal(~bekerja, des7.3.6_4), level = 0.95)
##            2.5 %   97.5 %
## bekerja 393387.4 558564.6
confint(svytotal(~TPT, des7.3.4_4), level = 0.95)
##        2.5 %   97.5 %
## TPT 7948.324 38479.68
confint(svytotal(~TPAK, des7.3.6_4), level = 0.95)
##         2.5 %   97.5 %
## TPAK 413218.9 585161.1
confint(svymean(~TPAK, des7.3.6_4), level = 0.95)
##          2.5 %    97.5 %
## TPAK 0.6295413 0.7240805
confint(svymean(~TPT, des7.3.4_4), level = 0.95)
##          2.5 %     97.5 %
## TPT 0.01735816 0.07564851

#Kota Yogyakarta

svytotal(~TPT, des7.3.4_5)
##     total     SE
## TPT 14084 4762.3
svymean(~TPT, des7.3.4_5)
##         mean     SE
## TPT 0.041353 0.0141
svytotal(~bekerja, des7.3.6_5)
##          total    SE
## bekerja 326492 29913
svytotal(~TPAK, des7.3.6_5)
##       total    SE
## TPAK 340576 29959
svymean(~TPAK, des7.3.6_5)
##         mean     SE
## TPAK 0.63133 0.0314
(4762.3/14084)
## [1] 0.3381355
(0.0141/0.041353)
## [1] 0.3409668
(29913/326492)
## [1] 0.0916194
(29959/340576)
## [1] 0.08796568
(0.0314/0.63133)
## [1] 0.04973627
confint(svytotal(~bekerja, des7.3.6_5), level = 0.95)
##            2.5 %   97.5 %
## bekerja 267864.5 385119.5
confint(svytotal(~TPT, des7.3.4_5), level = 0.95)
##        2.5 %   97.5 %
## TPT 4750.049 23417.95
confint(svytotal(~TPAK, des7.3.6_5), level = 0.95)
##         2.5 %   97.5 %
## TPAK 281857.2 399294.8
confint(svymean(~TPAK, des7.3.6_5), level = 0.95)
##          2.5 %    97.5 %
## TPAK 0.5697034 0.6929589
confint(svymean(~TPT, des7.3.4_5), level = 0.95)
##          2.5 %    97.5 %
## TPT 0.01365764 0.0690493

#Variabel Pendapatan Status Berusaha Sendiri

sakernas_diy.156.1=filter(sakernas2020_diy, B5_R24A %in% c("1", "5"))
sakernas_diy.buruh1=filter(sakernas2020_diy, B5_R24A %in% c("4"))

#Provinsi Yogyakarta

options(survey.lonely.psu = "adjust")
des17 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas_diy.156.1)
svymean(~B5_R28B1, des17)
##             mean    SE
## B5_R28B1 1393964 92650
svymean(~B5_R28B2, des17)
##           mean    SE
## B5_R28B2 71704 25847
(92650/1393964)
## [1] 0.06646513
(25847/71704)
## [1] 0.360468
confint(svymean(~B5_R28B1, des17), level = 0.95)
##            2.5 %  97.5 %
## B5_R28B1 1212373 1575556
confint(svymean(~B5_R28B2, des17), level = 0.95)
##            2.5 %   97.5 %
## B5_R28B2 21044.6 122363.7
e1=filter(sakernas_diy.156.1, KODE_KAB=="01")
e2=filter(sakernas_diy.156.1, KODE_KAB=="02")
e3=filter(sakernas_diy.156.1, KODE_KAB=="03")
e4=filter(sakernas_diy.156.1, KODE_KAB=="04")
e5=filter(sakernas_diy.156.1, KODE_KAB=="71")

des17.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = e1)
des17.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = e2)
des17.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = e3)
des17.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = e4)
des17.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = e5)

#Kabupaten Kulonprogo

svymean(~B5_R28B1, des17.1)
##             mean     SE
## B5_R28B1 1007061 118406
svymean(~B5_R28B2, des17.1)
##           mean    SE
## B5_R28B2 44815 13549
(118406/1007061)
## [1] 0.1175758
(13549/44815)
## [1] 0.3023318
confint(svymean(~B5_R28B1, des17.1), level = 0.95)
##             2.5 %  97.5 %
## B5_R28B1 774989.4 1239132
confint(svymean(~B5_R28B2, des17.1), level = 0.95)
##             2.5 %   97.5 %
## B5_R28B2 18259.58 71371.23

#Kabupaten Bantul

svymean(~B5_R28B1, des17.2)
##             mean     SE
## B5_R28B1 1481463 277562
svymean(~B5_R28B2, des17.2)
##           mean    SE
## B5_R28B2 20575 12176
(277562/1481463)
## [1] 0.1873567
(12176/20575)
## [1] 0.5917861
confint(svymean(~B5_R28B1, des17.2), level = 0.95)
##             2.5 %  97.5 %
## B5_R28B1 937450.9 2025475
confint(svymean(~B5_R28B2, des17.2), level = 0.95)
##              2.5 %   97.5 %
## B5_R28B2 -3289.984 44440.21

#Kabupaten Gunung Kidul

svymean(~B5_R28B1, des17.3)
##             mean     SE
## B5_R28B1 1097650 150712
svymean(~B5_R28B2, des17.3)
##           mean    SE
## B5_R28B2 45489 21969
(150712/1097650)
## [1] 0.1373042
(21969/45489)
## [1] 0.4829519
confint(svymean(~B5_R28B1, des17.3), level = 0.95)
##             2.5 %  97.5 %
## B5_R28B1 802260.2 1393039
confint(svymean(~B5_R28B2, des17.3), level = 0.95)
##             2.5 %   97.5 %
## B5_R28B2 2430.715 88546.24

#Kabupaten Sleman

svymean(~B5_R28B1, des17.4)
##             mean     SE
## B5_R28B1 1700084 139456
svymean(~B5_R28B2, des17.4)
##            mean     SE
## B5_R28B2 208620 111352
(139456/1700084)
## [1] 0.08202889
(111352/208620)
## [1] 0.5337552
confint(svymean(~B5_R28B1, des17.4), level = 0.95)
##            2.5 %  97.5 %
## B5_R28B1 1426755 1973412
confint(svymean(~B5_R28B2, des17.4), level = 0.95)
##              2.5 %   97.5 %
## B5_R28B2 -9626.586 426866.5

#Kota Yogyakarta

svymean(~B5_R28B1, des17.5)
##             mean     SE
## B5_R28B1 1597752 182635
svymean(~B5_R28B2, des17.5)
##           mean    SE
## B5_R28B2 27571 14840
(182635/1597752)
## [1] 0.1143075
(14840/27571)
## [1] 0.5382467
confint(svymean(~B5_R28B1, des17.5), level = 0.95)
##            2.5 %  97.5 %
## B5_R28B1 1239794 1955710
confint(svymean(~B5_R28B2, des17.5), level = 0.95)
##              2.5 %   97.5 %
## B5_R28B2 -1514.797 56657.06

#Variabel Pendapatan Status Buruh #Provinsi DIY

options(survey.lonely.psu = "adjust")
des18 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = sakernas_diy.buruh1)
svymean(~B5_R28C1, des18)
##             mean     SE
## B5_R28C1 2337952 104460
svymean(~B5_R28C2, des18)
##            mean    SE
## B5_R28C2 104503 24344
se_28c1 = 104460
se_28c2 = 24344
myu_28c1 = 2337952
myu_28c2 = 104503

(rse_28c1 = se_28c1/myu_28c1)
## [1] 0.04468013
confint(svymean(~B5_R28C1, des18), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 2133214 2542691
(rse_28c2 = se_28c2/myu_28c2)
## [1] 0.2329503
confint(svymean(~B5_R28C2, des18), level = 0.95)
##            2.5 %   97.5 %
## B5_R28C2 56790.3 152215.2
f1=filter(sakernas_diy.buruh1, KODE_KAB=="01")
f2=filter(sakernas_diy.buruh1, KODE_KAB=="02")
f3=filter(sakernas_diy.buruh1, KODE_KAB=="03")
f4=filter(sakernas_diy.buruh1, KODE_KAB=="04")
f5=filter(sakernas_diy.buruh1, KODE_KAB=="71")

des18.1 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = f1)
des18.2 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = f2)
des18.3 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = f3)
des18.4 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = f4)
des18.5 = svydesign(ids=~psu+ssu, strata=~strata, weights=~FINAL_WEIG, data = f5)

#Kabupaten Kulonprogo

svymean(~B5_R28C1, des18.1)
##             mean     SE
## B5_R28C1 2009983 214225
svymean(~B5_R28C2, des18.1)
##            mean    SE
## B5_R28C2 102012 29328
(214225/2009983)
## [1] 0.1065805
(29328/102012)
## [1] 0.2874956
confint(svymean(~B5_R28C1, des18.1), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 1590109 2429857
confint(svymean(~B5_R28C2, des18.1), level = 0.95)
##             2.5 %   97.5 %
## B5_R28C2 44530.73 159492.8

#Kabupaten Bantul

svymean(~B5_R28C1, des18.2)
##             mean     SE
## B5_R28C1 2252428 187725
svymean(~B5_R28C2, des18.2)
##           mean     SE
## B5_R28C2 11330 7147.3
(187725/2252428)
## [1] 0.0833434
(7147.3/11330)
## [1] 0.6308297
confint(svymean(~B5_R28C1, des18.2), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 1884494 2620362
confint(svymean(~B5_R28C2, des18.2), level = 0.95)
##              2.5 %   97.5 %
## B5_R28C2 -2679.024 25337.98

#Kabupaten Gunung Kidul

svymean(~B5_R28C1, des18.3)
##             mean     SE
## B5_R28C1 1811652 143591
svymean(~B5_R28C2, des18.3)
##           mean    SE
## B5_R28C2 39639 11392
(143591/1811652)
## [1] 0.0792597
(11392/39639)
## [1] 0.2873937
confint(svymean(~B5_R28C1, des18.3), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 1530219 2093084
confint(svymean(~B5_R28C2, des18.3), level = 0.95)
##             2.5 %   97.5 %
## B5_R28C2 17310.43 61967.48

#Kabupaten Sleman

svymean(~B5_R28C1, des18.4)
##             mean     SE
## B5_R28C1 2644868 188992
svymean(~B5_R28C2, des18.4)
##            mean    SE
## B5_R28C2 229880 71774
(188992/2644868)
## [1] 0.07145612
(71774/229880)
## [1] 0.3122238
confint(svymean(~B5_R28C1, des18.4), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 2274451 3015285
confint(svymean(~B5_R28C2, des18.4), level = 0.95)
##             2.5 %   97.5 %
## B5_R28C2 89205.52 370555.2

#Kota Yogyakarta

svymean(~B5_R28C1, des18.5)
##             mean     SE
## B5_R28C1 2629248 341247
svymean(~B5_R28C2, des18.5)
##            mean    SE
## B5_R28C2 104591 61009
(341247/2629248)
## [1] 0.1297888
(61009/104591)
## [1] 0.5833102
confint(svymean(~B5_R28C1, des18.5), level = 0.95)
##            2.5 %  97.5 %
## B5_R28C1 1960416 3298080
confint(svymean(~B5_R28C2, des18.5), level = 0.95)
##              2.5 %   97.5 %
## B5_R28C2 -14984.48 224165.7