library(openxlsx)
library(rmarkdown)
library(tidyverse)
library(haven)
library(foreign)
library(survey) Una ruta hacia una carpeta donde almacenaremos los excel que se elaborarán posteriormente, que contarán con dataset de información construida.
ruta <- "C:/Users/Trabajo/Desktop/RDATA" #choose.files()
#sumaria2018 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SUMARIA - 759-Modulo34\\Sumaria-2021.sav")
#sumaria2019 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SUMARIA - 759-Modulo34\\Sumaria-2021.sav")
#sumaria2020 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SUMARIA - 759-Modulo34\\Sumaria-2021.sav")
#sumaria2021 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SUMARIA - 759-Modulo34\\Sumaria-2021.sav")
sumaria2022 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO\\SUMARIA - 759-Modulo34\\Sumaria-2022.sav")
#empleo2018 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\EMPLEO - 759-Modulo05\\Enaho01A-2021-500.sav")
#empleo2019 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\EMPLEO - 759-Modulo05\\Enaho01A-2021-500.sav")
#empleo2020 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\EMPLEO - 759-Modulo05\\Enaho01A-2021-500.sav")
#empleo2021 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\EMPLEO - 759-Modulo05\\Enaho01A-2021-500.sav")
empleo2022 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO\\EMPLEO - 759-Modulo05\\Enaho01a-2022-500.sav")
#educacion2018 <- read_spss("C:\\Users\\DINDES08\\Desktop\\ENAHO\\2022\\Educacion - 2022 - 784-Modulo03\\Enaho01A-2022-300.sav")
#educacion2019 <- read_spss("C:\\Users\\DINDES08\\Desktop\\ENAHO\\2022\\Educacion - 2022 - 784-Modulo03\\Enaho01A-2022-300.sav")
#educacion2020 <- read_spss("C:\\Users\\DINDES08\\Desktop\\ENAHO\\2022\\Educacion - 2022 - 784-Modulo03\\Enaho01A-2022-300.sav")
#educacion2021 <- read_spss("C:\\Users\\DINDES08\\Desktop\\ENAHO\\2022\\Educacion - 2022 - 784-Modulo03\\Enaho01A-2022-300.sav")
educacion2022 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO\\EDUACIÓN - 759-Modulo03\\Enaho01A-2022-300.sav")
#alud2018 <- read_dta("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SALUD - 759-Modulo04\\enaho01a-2018-400.dta")
#salud2019 <- read_dta("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SALUD - 759-Modulo04\\enaho01a-2019-400.dta")
#salud2020 <- read_dta("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SALUD - 759-Modulo04\\enaho01a-2020-400.dta")
#salud2021 <- read_dta("C:\\Users\\Trabajo\\Desktop\\ENAHO 2021\\SALUD - 759-Modulo04\\enaho01a-2021-400.dta")
salud2022 <- read_spss("C:\\Users\\Trabajo\\Desktop\\ENAHO\\SALUD - 759-Modulo04\\Enaho01A-2022-400.sav")
educacion2022 <- subset(educacion2022, select = c(CODPERSO,CONGLOME,VIVIENDA,HOGAR,P300A))
salud2022 <- subset(salud2022, select = c(CODPERSO,CONGLOME,VIVIENDA,HOGAR,P401H1,P401H2,P401H3,P401H4,P401H5,P401H6))
sumaria2022 <- subset(sumaria2022, select = c(CONGLOME,VIVIENDA,HOGAR,POBREZA))
enaho0 <- left_join(empleo2022, educacion2022, by=c("CODPERSO","CONGLOME", "VIVIENDA", "HOGAR"))
enaho1 <- left_join(enaho0, salud2022, by=c("CODPERSO","CONGLOME", "VIVIENDA", "HOGAR"))
enaho <- left_join(enaho1,sumaria2022, by =c("CONGLOME","VIVIENDA","HOGAR"))Aquellas variables que nos sirven para realizar las desagregaciones posteriores.
Hay que convertir la variable ubigeo.x (el “.x” es producto de la unión de bases de datos) a numérico para no encontrar problemas al momento de recodificar
enaho$ubigeonum <- as.numeric(enaho$UBIGEO)
enaho <- enaho %>%
mutate(regiones2 =
ifelse(ubigeonum >= 010101 & ubigeonum <= 010707, "Amazonas",
ifelse(ubigeonum >= 020101 & ubigeonum <= 022008, "Ancash",
ifelse(ubigeonum >= 030101 & ubigeonum <= 030714, "Apurimac",
ifelse(ubigeonum >= 040101 & ubigeonum <= 040811, "Arequipa",
ifelse(ubigeonum >= 050101 & ubigeonum <= 051108, "Ayacucho",
ifelse(ubigeonum >= 060101 & ubigeonum <= 061311, "Cajamarca",
ifelse(ubigeonum >= 070101 & ubigeonum <= 070107, "Callao",
ifelse(ubigeonum >= 080101 & ubigeonum <= 081307, "Cusco",
ifelse(ubigeonum >= 090101 & ubigeonum <= 090723, "Huancavelica",
ifelse(ubigeonum >= 100101 & ubigeonum <= 101108, "Huanuco",
ifelse(ubigeonum >= 110101 & ubigeonum <= 110508, "Ica",
ifelse(ubigeonum >= 120101 & ubigeonum <= 120909, "Junin",
ifelse(ubigeonum >= 130101 & ubigeonum <= 131203, "La Libertad",
ifelse(ubigeonum >= 140101 & ubigeonum <= 140312, "Lambayeque",
ifelse(ubigeonum >= 150101 & ubigeonum <= 150143, "Lima Metropolitana",
ifelse(ubigeonum >= 150201 & ubigeonum <= 151033, "Lima Region",
ifelse(ubigeonum >= 160101 & ubigeonum <= 160804, "Loreto",
ifelse(ubigeonum >= 170101 & ubigeonum <= 170303, "Madre de Dios",
ifelse(ubigeonum >= 180101 & ubigeonum <= 180303, "Moquegua",
ifelse(ubigeonum >= 190101 & ubigeonum <= 190308, "Pasco",
ifelse(ubigeonum >= 200101 & ubigeonum <= 200806, "Piura",
ifelse(ubigeonum >= 210101 & ubigeonum <= 211307, "Puno",
ifelse(ubigeonum >= 220101 & ubigeonum <= 221005, "San Martín",
ifelse(ubigeonum >= 230101 & ubigeonum <= 230408, "Tacna",
ifelse(ubigeonum >= 240101 & ubigeonum <= 240304, "Tumbes",
ifelse(ubigeonum >= 250101 & ubigeonum <= 250401,"Ucayali",NA)))))))))))))))))))))))))))
table(enaho$regiones2, useNA = "alw")##
## Amazonas Ancash Apurimac Arequipa
## 3126 3578 2313 3942
## Ayacucho Cajamarca Callao Cusco
## 2593 3527 2804 3069
## Huancavelica Huanuco Ica Junin
## 2584 3043 3812 3660
## La Libertad Lambayeque Lima Metropolitana Lima Region
## 4065 3991 8548 3678
## Loreto Madre de Dios Moquegua Pasco
## 4108 1437 2291 2131
## Piura Puno San Martín Tacna
## 4447 2667 3569 3221
## Tumbes Ucayali <NA>
## 2283 3174 0enaho <- enaho %>%
mutate(regnat = ifelse(DOMINIO>=1 & DOMINIO<=3 | DOMINIO==8,"Costa",
ifelse(DOMINIO>=4 & DOMINIO<=6,"Sierra",
ifelse(DOMINIO==7,"Selva",NA))))
table(enaho$regnat, useNA = "alw")##
## Costa Selva Sierra <NA>
## 38829 18847 29985 0enaho <- enaho %>%
mutate(area = ifelse((DOMINIO==8 |
(DOMINIO>=1 & DOMINIO<=7) &
(ESTRATO>=1 & ESTRATO<=5)), "Urbano",
ifelse(((DOMINIO>=1 & DOMINIO<=7) &
(ESTRATO>=6 & ESTRATO<=8)), "Rural", NA)))
table(enaho$area, useNA = "alw")##
## Rural Urbano <NA>
## 28845 58816 0enaho <- enaho %>%
mutate(pobreza3 = ifelse(POBREZA==1, "Pobre extremo",
ifelse(POBREZA==2, "Pobre no extremo",
ifelse(POBREZA==3, "No pobre", NA
))))
table(enaho$pobreza3, useNA = "alw")##
## No pobre Pobre extremo Pobre no extremo <NA>
## 67956 4159 15546 0enaho <- enaho %>%
mutate(lengua = ifelse(P300A==4, "Castellano",
ifelse(P300A==1 | P300A==2 | P300A==3, "Originaria", NA)))
enaho$lengua <- as.factor(enaho$lengua)
table(enaho$lengua, useNA = "alw")##
## Castellano Originaria <NA>
## 68370 17789 1502enaho <- enaho %>%
mutate(discapacidad =ifelse(P401H1==1|P401H2==1|P401H3==1|
P401H4==1|P401H5==1|P401H6==1,1,0))
table(enaho$discapacidad, useNA = "alw")##
## 0 1 <NA>
## 82328 5266 67table(enaho$P558C, useNA = "alw")##
## 1 2 3 4 5 6 7 8 9 <NA>
## 21160 2883 1883 6047 3701 44600 3626 3517 117 127enaho <- enaho %>%
mutate(defiet2 = case_when(
P558C == 1 ~ "Quechua",
P558C == 2 ~ "Aimara",
P558C == 3 ~ "Nativo o indigena de la Amazonia",
P558C == 4 ~ "Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente",
P558C == 5 ~ "Blanco",
P558C == 6 ~ "Mestizo",
P558C == 7 ~ "otro",
P558C == 8 ~ "No sabe/No responde",
P558C == 9 ~ "Nativo o indigena de la Amazonia",
TRUE ~ NA_character_
))
enaho$defiet2 <- as.factor(enaho$defiet2)
table(enaho$defiet2, useNA = "alw")##
## Aimara
## 2883
## Blanco
## 3701
## Mestizo
## 44600
## Nativo o indigena de la Amazonia
## 2000
## Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente
## 6047
## No sabe/No responde
## 3517
## otro
## 3626
## Quechua
## 21160
## <NA>
## 127table(enaho$P204)##
## 1 2
## 87156 505enaho <- enaho %>%
mutate(res = ifelse((P204==1 & P205==2)|(P204==2 & P206==1),"Residente","No residente"))
enaho$res <- as.factor(enaho$res)
table(enaho$res, useNA = "alw")##
## No residente Residente <NA>
## 1390 86271 0enaho <- enaho %>%
mutate(pea = ifelse(P208A>=14 & res=="Residente" & (OCU500==1 | OCU500==2),"PEA","NO PEA"))
enaho$pea <- as.factor(enaho$pea)
table(enaho$pea, useNA = "alw")##
## NO PEA PEA <NA>
## 25294 62367 0enaho <- enaho %>%
mutate(ocupado = ifelse(P208A>=14 & P208A<=98 & res=="Residente" & OCU500==1,"Ocupado","No Ocupado"))
enaho$ocupado <- as.factor(enaho$ocupado)
table(enaho$ocupado, useNA = "alw")##
## No Ocupado Ocupado <NA>
## 27221 60440 0enaho$resid14 <-
ifelse(((enaho$P204==1 & enaho$P205==2)|(enaho$P204==2 & enaho$P206==1)) & enaho$P208A>=14 & enaho$CODINFOR != "00", 1, 0)
table(enaho$resid14, useNA = "alw")##
## 0 1 <NA>
## 1513 86148 0#OCUPADO FORMAL
enaho <- enaho %>%
mutate (ocuformal = ifelse(OCUPINF==2 & resid14==1,1,0))
table(enaho$ocuformal, useNA = "alw")##
## 0 1 <NA>
## 49265 12688 25708enaho_filtrado <- enaho %>%
filter(P208A >= 15 & P208A <= 29)
table(enaho_filtrado$ocuformal, enaho_filtrado$pea)##
## NO PEA PEA
## 0 742 11897
## 1 0 2161encuesta = svydesign(data=enaho_filtrado, id=~CONGLOME, strata=~ESTRATO,
weights=~FAC500A)tabla0 <- svyby(~ocuformal, ~pea, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla0ic0 <-as.table(confint(tabla0)) #INTERVALOS DE CONFIANZA
ic0## 2.5 % 97.5 %
## NO PEA 0.0000000 0.0000000
## PEA 0.1811692 0.2039100cv0 <-cv(tabla0) #COEFICIENTE DE VARIACIÓN
cv0## NO PEA PEA
## NaN 0.03013061workbook0 <- createWorkbook()
addWorksheet(workbook0, sheetName = "Tabla 0")
addWorksheet(workbook0, sheetName = "IC 0")
addWorksheet(workbook0, sheetName = "CV 0")
writeData(workbook0, sheet = "Tabla 0", x = tabla0, colNames = TRUE)
writeData(workbook0, sheet = "IC 0", x = ic0, colNames = TRUE)
writeData(workbook0, sheet = "CV 0", x = cv0, colNames = TRUE)
saveWorkbook(workbook0, "datos0.xlsx")tabla1 <- svyby(~ocuformal, ~pea+P207, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla1ic1 <-as.table(confint(tabla1)) #INTERVALOS DE CONFIANZA
ic1## 2.5 % 97.5 %
## NO PEA.1 0.0000000 0.0000000
## PEA.1 0.1889842 0.2191531
## NO PEA.2 0.0000000 0.0000000
## PEA.2 0.1613823 0.1926838cv1 <-cv(tabla1) #COEFICIENTE DE VARIACIÓN
cv1## NO PEA.1 PEA.1 NO PEA.2 PEA.2
## NaN 0.03771411 NaN 0.04510588workbook1 <- createWorkbook()
addWorksheet(workbook1, sheetName = "Tabla 1")
addWorksheet(workbook1, sheetName = "IC 1")
addWorksheet(workbook1, sheetName = "CV 1")
writeData(workbook1, sheet = "Tabla 1", x = tabla1, colNames = TRUE)
writeData(workbook1, sheet = "IC 1", x = ic1, colNames = TRUE)
writeData(workbook1, sheet = "CV 1", x = cv1, colNames = TRUE)
saveWorkbook(workbook1, "datos1.xlsx")tabla2 <- svyby(~ocuformal, ~pea+area, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla2ic2 <-as.table(confint(tabla2)) #INTERVALOS DE CONFIANZA
ic2## 2.5 % 97.5 %
## NO PEA.Rural 0.00000000 0.00000000
## PEA.Rural 0.03210907 0.04732532
## NO PEA.Urbano 0.00000000 0.00000000
## PEA.Urbano 0.22271352 0.25102552cv2 <-cv(tabla2) #COEFICIENTE DE VARIACIÓN
cv2## NO PEA.Rural PEA.Rural NO PEA.Urbano PEA.Urbano
## NaN 0.09773521 NaN 0.03049182workbook2 <- createWorkbook()
addWorksheet(workbook2, sheetName = "Tabla 2")
addWorksheet(workbook2, sheetName = "IC 2")
addWorksheet(workbook2, sheetName = "CV 2")
writeData(workbook2, sheet = "Tabla 2", x = tabla2, colNames = TRUE)
writeData(workbook2, sheet = "IC 2", x = ic2, colNames = TRUE)
writeData(workbook2, sheet = "CV 2", x = cv2, colNames = TRUE)
saveWorkbook(workbook2, "datos2.xlsx")tabla3 <- svyby(~ocuformal, ~pea+regnat, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla3ic3 <-as.table(confint(tabla3)) #INTERVALOS DE CONFIANZA
ic3## 2.5 % 97.5 %
## NO PEA.Costa 0.00000000 0.00000000
## PEA.Costa 0.25695698 0.29315250
## NO PEA.Selva 0.00000000 0.00000000
## PEA.Selva 0.06257920 0.08898577
## NO PEA.Sierra 0.00000000 0.00000000
## PEA.Sierra 0.08652413 0.11372469cv3 <-cv(tabla3) #COEFICIENTE DE VARIACIÓN
cv3## NO PEA.Costa PEA.Costa NO PEA.Selva PEA.Selva NO PEA.Sierra
## NaN 0.03357048 NaN 0.08889250 NaN
## PEA.Sierra
## 0.06930424workbook3 <- createWorkbook()
addWorksheet(workbook3, sheetName = "Tabla 3")
addWorksheet(workbook3, sheetName = "IC 3")
addWorksheet(workbook3, sheetName = "CV 3")
writeData(workbook3, sheet = "Tabla 3", x = tabla3, colNames = TRUE)
writeData(workbook3, sheet = "IC 3", x = ic3, colNames = TRUE)
writeData(workbook3, sheet = "CV 3", x = cv3, colNames = TRUE)
saveWorkbook(workbook3, "datos3.xlsx")tabla4 <- svyby(~ocuformal, ~pea+regiones2, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla4ic4 <-as.table(confint(tabla4)) #INTERVALOS DE CONFIANZA
ic4## 2.5 % 97.5 %
## NO PEA.Amazonas 0.00000000 0.00000000
## PEA.Amazonas 0.02503783 0.06877547
## NO PEA.Ancash 0.00000000 0.00000000
## PEA.Ancash 0.08354910 0.14925548
## NO PEA.Apurimac 0.00000000 0.00000000
## PEA.Apurimac 0.02464545 0.10826618
## NO PEA.Arequipa 0.00000000 0.00000000
## PEA.Arequipa 0.21834322 0.30573371
## NO PEA.Ayacucho 0.00000000 0.00000000
## PEA.Ayacucho 0.01791013 0.06787981
## NO PEA.Cajamarca 0.00000000 0.00000000
## PEA.Cajamarca 0.04452036 0.11499756
## NO PEA.Callao 0.00000000 0.00000000
## PEA.Callao 0.29388138 0.41420317
## NO PEA.Cusco 0.00000000 0.00000000
## PEA.Cusco 0.04538679 0.11794425
## NO PEA.Huancavelica 0.00000000 0.00000000
## PEA.Huancavelica 0.02238264 0.07093504
## NO PEA.Huanuco 0.00000000 0.00000000
## PEA.Huanuco 0.03789212 0.10090086
## NO PEA.Ica 0.00000000 0.00000000
## PEA.Ica 0.25061326 0.32815212
## NO PEA.Junin 0.00000000 0.00000000
## PEA.Junin 0.07400599 0.13861233
## NO PEA.La Libertad 0.00000000 0.00000000
## PEA.La Libertad 0.20324622 0.28601140
## NO PEA.Lambayeque 0.00000000 0.00000000
## PEA.Lambayeque 0.14330469 0.20761910
## NO PEA.Lima Metropolitana 0.00000000 0.00000000
## PEA.Lima Metropolitana 0.28417673 0.34739766
## NO PEA.Lima Region 0.00000000 0.00000000
## PEA.Lima Region 0.11849091 0.19531406
## NO PEA.Loreto 0.00000000 0.00000000
## PEA.Loreto 0.06073816 0.12628864
## NO PEA.Madre de Dios 0.00000000 0.00000000
## PEA.Madre de Dios 0.05215094 0.17090157
## NO PEA.Moquegua 0.00000000 0.00000000
## PEA.Moquegua 0.13436795 0.24341178
## NO PEA.Pasco 0.00000000 0.00000000
## PEA.Pasco 0.11484848 0.22286317
## NO PEA.Piura 0.00000000 0.00000000
## PEA.Piura 0.13946031 0.20751589
## NO PEA.Puno 0.00000000 0.00000000
## PEA.Puno 0.03520992 0.12473184
## NO PEA.San Martín 0.00000000 0.00000000
## PEA.San Martín 0.04952275 0.10449950
## NO PEA.Tacna 0.00000000 0.00000000
## PEA.Tacna 0.12782452 0.24864441
## NO PEA.Tumbes 0.00000000 0.00000000
## PEA.Tumbes 0.16248891 0.27521538
## NO PEA.Ucayali 0.00000000 0.00000000
## PEA.Ucayali 0.05271800 0.11316667cv4 <-cv(tabla4) #COEFICIENTE DE VARIACIÓN
cv4## NO PEA.Amazonas PEA.Amazonas NO PEA.Ancash
## NaN 0.23787176 NaN
## PEA.Ancash NO PEA.Apurimac PEA.Apurimac
## 0.14400181 NaN 0.32099840
## NO PEA.Arequipa PEA.Arequipa NO PEA.Ayacucho
## NaN 0.08507874 NaN
## PEA.Ayacucho NO PEA.Cajamarca PEA.Cajamarca
## 0.29718169 NaN 0.22541929
## NO PEA.Callao PEA.Callao NO PEA.Cusco
## NaN 0.08669840 NaN
## PEA.Cusco NO PEA.Huancavelica PEA.Huancavelica
## 0.22665498 NaN 0.26545979
## NO PEA.Huanuco PEA.Huanuco NO PEA.Ica
## NaN 0.23162486 NaN
## PEA.Ica NO PEA.Junin PEA.Junin
## 0.06835476 NaN 0.15503380
## NO PEA.La Libertad PEA.La Libertad NO PEA.Lambayeque
## NaN 0.08631017 NaN
## PEA.Lambayeque NO PEA.Lima Metropolitana PEA.Lima Metropolitana
## 0.09350770 NaN 0.05107263
## NO PEA.Lima Region PEA.Lima Region NO PEA.Loreto
## NaN 0.12490626 NaN
## PEA.Loreto NO PEA.Madre de Dios PEA.Madre de Dios
## 0.17882322 NaN 0.27163185
## NO PEA.Moquegua PEA.Moquegua NO PEA.Pasco
## NaN 0.14727002 NaN
## PEA.Pasco NO PEA.Piura PEA.Piura
## 0.16318818 NaN 0.10007278
## NO PEA.Puno PEA.Puno NO PEA.San Martín
## NaN 0.28557451 NaN
## PEA.San Martín NO PEA.Tacna PEA.Tacna
## 0.18211576 NaN 0.16374242
## NO PEA.Tumbes PEA.Tumbes NO PEA.Ucayali
## NaN 0.13140050 NaN
## PEA.Ucayali
## 0.18592271workbook4 <- createWorkbook()
addWorksheet(workbook4, sheetName = "Tabla 4")
addWorksheet(workbook4, sheetName = "IC 4")
addWorksheet(workbook4, sheetName = "CV 4")
writeData(workbook4, sheet = "Tabla 4", x = tabla4, colNames = TRUE)
writeData(workbook4, sheet = "IC 4", x = ic4, colNames = TRUE)
writeData(workbook4, sheet = "CV 4", x = cv4, colNames = TRUE)
saveWorkbook(workbook4, "datos4.xlsx")tabla5 <- svyby(~ocuformal, ~pea+pobreza3, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla5ic5 <-as.table(confint(tabla5)) #INTERVALOS DE CONFIANZA
ic5## 2.5 % 97.5 %
## NO PEA.No pobre 0.000000000 0.000000000
## PEA.No pobre 0.210382755 0.237740686
## NO PEA.Pobre extremo 0.000000000 0.000000000
## PEA.Pobre extremo 0.009078157 0.073882126
## NO PEA.Pobre no extremo 0.000000000 0.000000000
## PEA.Pobre no extremo 0.083162447 0.124045009cv5 <-cv(tabla5) #COEFICIENTE DE VARIACIÓN
cv5## NO PEA.No pobre PEA.No pobre NO PEA.Pobre extremo
## NaN 0.03114853 NaN
## PEA.Pobre extremo NO PEA.Pobre no extremo PEA.Pobre no extremo
## 0.39855044 NaN 0.10066642workbook5 <- createWorkbook()
addWorksheet(workbook5, sheetName = "Tabla 5")
addWorksheet(workbook5, sheetName = "IC 5")
addWorksheet(workbook5, sheetName = "CV 5")
writeData(workbook5, sheet = "Tabla 5", x = tabla5, colNames = TRUE)
writeData(workbook5, sheet = "IC 5", x = ic5, colNames = TRUE)
writeData(workbook5, sheet = "CV 5", x = cv5, colNames = TRUE)
saveWorkbook(workbook5, "datos5.xlsx")tabla6 <- svyby(~ocuformal, ~pea+discapacidad, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla6ic6 <-as.table(confint(tabla6)) #INTERVALOS DE CONFIANZA
ic6## 2.5 % 97.5 %
## NO PEA.0 0.00000000 0.00000000
## PEA.0 0.18237292 0.20519494
## NO PEA.1 0.00000000 0.00000000
## PEA.1 0.02129504 0.17925656cv6<-cv(tabla6) #COEFICIENTE DE VARIACIÓN
cv6## NO PEA.0 PEA.0 NO PEA.1 PEA.1
## NaN 0.03004405 NaN 0.40186212workbook6 <- createWorkbook()
addWorksheet(workbook6, sheetName = "Tabla 6")
addWorksheet(workbook6, sheetName = "IC 6")
addWorksheet(workbook6, sheetName = "CV 6")
writeData(workbook6, sheet = "Tabla 6", x = tabla6, colNames = TRUE)
writeData(workbook6, sheet = "IC 6", x = ic6, colNames = TRUE)
writeData(workbook6, sheet = "CV 6", x = cv6, colNames = TRUE)
saveWorkbook(workbook6, "datos6.xlsx") tabla7 <- svyby(~ocuformal, ~pea+defiet2, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla7 ic7 <-as.table(confint(tabla7)) #INTERVALOS DE CONFIANZA
ic7 ## 2.5 %
## NO PEA.Aimara 0.00000000
## PEA.Aimara 0.02645257
## NO PEA.Blanco 0.00000000
## PEA.Blanco 0.12829054
## NO PEA.Mestizo 0.00000000
## PEA.Mestizo 0.22606636
## NO PEA.Nativo o indigena de la Amazonia 0.00000000
## PEA.Nativo o indigena de la Amazonia 0.02114774
## NO PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente 0.00000000
## PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente 0.14283458
## NO PEA.No sabe/No responde 0.00000000
## PEA.No sabe/No responde 0.14268243
## NO PEA.otro 0.00000000
## PEA.otro 0.14217887
## NO PEA.Quechua 0.00000000
## PEA.Quechua 0.08536508
## 97.5 %
## NO PEA.Aimara 0.00000000
## PEA.Aimara 0.07778123
## NO PEA.Blanco 0.00000000
## PEA.Blanco 0.22382648
## NO PEA.Mestizo 0.00000000
## PEA.Mestizo 0.25846592
## NO PEA.Nativo o indigena de la Amazonia 0.00000000
## PEA.Nativo o indigena de la Amazonia 0.08636131
## NO PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente 0.00000000
## PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente 0.21425817
## NO PEA.No sabe/No responde 0.00000000
## PEA.No sabe/No responde 0.27399386
## NO PEA.otro 0.00000000
## PEA.otro 0.24977301
## NO PEA.Quechua 0.00000000
## PEA.Quechua 0.12160816cv7 <-cv(tabla7) #COEFICIENTE DE VARIACIÓN
cv7 ## NO PEA.Aimara
## NaN
## PEA.Aimara
## 0.25124838
## NO PEA.Blanco
## NaN
## PEA.Blanco
## 0.13843047
## NO PEA.Mestizo
## NaN
## PEA.Mestizo
## 0.03411680
## NO PEA.Nativo o indigena de la Amazonia
## NaN
## PEA.Nativo o indigena de la Amazonia
## 0.30948877
## NO PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente
## NaN
## PEA.Negro/Moreno/Zambo/Mulato/Pueblo Afro peruano o Afrodescendiente
## 0.10204990
## NO PEA.No sabe/No responde
## NaN
## PEA.No sabe/No responde
## 0.16078874
## NO PEA.otro
## NaN
## PEA.otro
## 0.14005796
## NO PEA.Quechua
## NaN
## PEA.Quechua
## 0.08934347workbook7 <- createWorkbook()
addWorksheet(workbook7, sheetName = "Tabla 7")
addWorksheet(workbook7, sheetName = "IC 7")
addWorksheet(workbook7, sheetName = "CV 7")
writeData(workbook7, sheet = "Tabla 7", x = tabla7, colNames = TRUE)
writeData(workbook7, sheet = "IC 7", x = ic7, colNames = TRUE)
writeData(workbook7, sheet = "CV 7", x = cv7, colNames = TRUE)
saveWorkbook(workbook7, "datos7.xlsx")tabla8 <- svyby(~ocuformal, ~pea+lengua, encuesta, svymean, deff=F,na.rm=T) #PROMEDIO
tabla8 ic8 <-as.table(confint(tabla8)) #INTERVALOS DE CONFIANZA
ic8 ## 2.5 % 97.5 %
## NO PEA.Castellano 0.00000000 0.00000000
## PEA.Castellano 0.19860492 0.22369394
## NO PEA.Originaria 0.00000000 0.00000000
## PEA.Originaria 0.04364522 0.07544459cv8 <-cv(tabla8) #COEFICIENTE DE VARIACIÓN
cv8 ## NO PEA.Castellano PEA.Castellano NO PEA.Originaria PEA.Originaria
## NaN 0.03031207 NaN 0.13623721workbook8 <- createWorkbook()
addWorksheet(workbook8, sheetName = "Tabla 8")
addWorksheet(workbook8, sheetName = "IC 8")
addWorksheet(workbook8, sheetName = "CV 8")
writeData(workbook8, sheet = "Tabla 8", x = tabla8, colNames = TRUE)
writeData(workbook8, sheet = "IC 8", x = ic8, colNames = TRUE)
writeData(workbook8, sheet = "CV 8", x = cv8, colNames = TRUE)
saveWorkbook(workbook8, "datos8.xlsx")#save(enaho,file=paste(ruta,"BASEDEDATOSIndicador1ENDES.RData",sep = "/"))
#BORRAMOS TODO MENOS "RUTA"
#rm(list=setdiff(ls(), c("ruta")))
#VOLVEMOS A CARGAR NUESTRA BD LIMPIA
#load(paste(ruta,"BASEDEDATOSIndicador1ENDES.RData",sep="/"))