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01_EDA.rmd
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---
title: "MOV 1"
author: "jose"
date: "18/6/2021"
output:
html_document:
code_folding: hide
fig_height: 6
fig_width: 9
toc: yes
toc_float: yes
pdf_document:
toc: yes
---
# Bases
```{r, echo=F, warning=F, message=F, error=F}
library(tidyverse)
library(gtsummary)
library(survey)
library(RColorBrewer)
library(sf)
library(ggupset)
library(questionr)
library(tmap)
library(GGally)
library(ggsci)
region_shape <- st_read("./ENDES/Regiones/DEPARTAMENTOS.shp")
```
# EDITADO
=======
# RESUMEN
V001 : CONGLOMERADO V022 : ESTRATO V005 : FACTOR DE PONDERACION MUJER
```{r,warning=F, message=F, error=F}
# Eliminando bases que no se usan
#rm(list=ls())
# Base de trabajo
MOV_GENERAL <- read.csv("./NewData/mov_general.csv")
df2<-MOV_GENERAL %>%
mutate(
tiposeguro = ifelse(CREDLUGAR == "H.MINSA/FFAA"|
CREDLUGAR == "H.ESSALUD"|
CREDLUGAR == "CS/POSTA MINSA"|
CREDLUGAR == "CS.ESSALUD", "Estado",
ifelse(CREDLUGAR == "OTRO/PRIVADO","Privado",NA)),
CREDLUGAR2 = ifelse(CREDLUGAR == "H.MINSA/FFAA"|CREDLUGAR == "H.ESSALUD","Hospital",
ifelse(CREDLUGAR == "CS/POSTA MINSA"|CREDLUGAR == "CS.ESSALUD","CentroSalud",CREDLUGAR))
) %>%
group_by(ANIO) %>%
nest() %>%
mutate(
df= map(.x = data,
.f = ~svydesign(id =~ V001, strata =~ V022, weights=~V005, data=.x)))
options(survey.lonely.psu="remove")
```
# DESCRIPCION DE VARIABLES
```{r}
df3<-df2 %>%
mutate(
tresidencia_m = map(.x = df,
.f = ~svymean(~as.factor(TIPORESIDENCIA), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
tresidencia_ci = map(.x = df,
.f = ~svymean(~as.factor(TIPORESIDENCIA), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
housingtype = map2(.x = tresidencia_m,
.y = tresidencia_ci,
.f = ~bind_cols(.x,.y)),
cred_m = map(.x = df,
.f = ~svymean(~as.factor(CREDLUGAR2), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
cred_ci = map(.x = df,
.f = ~svymean(~as.factor(CREDLUGAR2), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
credloc = map2(.x = cred_m,
.y = cred_ci,
.f = ~bind_cols(.x,.y)),
credtipo_m = map(.x = df,
.f = ~svymean(~as.factor(tiposeguro), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
credtipo_ci = map(.x = df,
.f = ~svymean(~as.factor(tiposeguro), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
credtype = map2(.x = credtipo_m,
.y = credtipo_ci,
.f = ~bind_cols(.x,.y)),
edu_m = map(.x = df,
.f = ~svymean(~as.factor(EDU_MADRE), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
edu_ci = map(.x = df,
.f = ~svymean(~as.factor(EDU_MADRE), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
edumadre = map2(.x = edu_m,
.y = edu_ci,
.f = ~bind_cols(.x,.y)),
etnia_m = map(.x = df,
.f = ~svymean(~as.factor(ETNIA_MADRE), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
etnia_ci = map(.x = df,
.f = ~svymean(~as.factor(ETNIA_MADRE), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
etnia = map2(.x = etnia_m,
.y = etnia_ci,
.f = ~bind_cols(.x,.y)),
income_m = map(.x = df,
.f = ~svymean(~as.factor(INDICERIQUEZA), design = .x, na.rm = T, vartype = c("se","ci")) %>%
as.vector() %>%
as.data.frame() %>%
mutate("prop" = as.numeric(.)) %>%
select(-.)),
income_ci = map(.x = df,
.f = ~svymean(~as.factor(INDICERIQUEZA), design = .x, na.rm = T, vartype = c("se","ci")) %>%
confint() %>%
as.data.frame() %>%
rownames_to_column(var = "var")),
income = map2(.x = income_m,
.y = income_ci,
.f = ~bind_cols(.x,.y))
)
```
```{r}
housing<-
df3 %>%
select(ANIO,housingtype) %>%
unnest(cols = c("housingtype")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable = "housing")
creloc<-
df3 %>%
select(ANIO,credloc) %>%
unnest(cols = c("credloc")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable = "credloc")
credtype<-
df3 %>%
select(ANIO,credtype) %>%
unnest(cols = c("credtype")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable = "credtype")
edumadre<-
df3 %>%
select(ANIO,edumadre) %>%
unnest(cols = c("edumadre")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable = "edumadre")
etnia<-
df3 %>%
select(ANIO,etnia) %>%
unnest(cols = c("etnia")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable="etnia")
income<-
df3 %>%
select(ANIO,income) %>%
unnest(cols = c("income")) %>% mutate(prop = round(prop*100),
`2.5 %`=round(`2.5 %`*100),
`97.5 %`=round(`97.5 %`*100),
variable = "income")
descriptivo<-
bind_rows(housing,credtype,creloc,edumadre,income,etnia) %>%
select(ANIO,prop,`2.5 %`,`97.5 %`,var,variable)
```
#DOSIS 11 ANIOS
Primeras Dosis de las 4 vacunas en los 11 anios de estudio.
```{r, warning=F, message=F, error=F}
ptv1<-
df2 %>%
mutate(
vacuna=map_dbl(.x = df,
.f =~svymean(~S45PV1_1, design =.x, na.rm = T)),
ptv1=map(.x = df,
.f =~svyciprop(~S45PV1_1, design =.x, na.rm = T)),
ptv1ci = map(.x = ptv1,
.f = ~confint(.x) %>%
as.data.frame() %>%
rownames_to_column("tar"))) %>%
unnest(cols = c(ptv1ci)) %>%
as.data.frame() %>%
mutate(ci_l = `2.5%`,
ci_h = `97.5%`,
vacunatipo = "ptv1") %>%
select(ANIO,ci_l,ci_h,vacunatipo,vacuna)
influ1<-
df2 %>%
mutate(
vacuna=map_dbl(.x = df,
.f =~svymean(~S45IF1_1, design =.x, na.rm = T)),
influ1=map(.x = df,
.f =~svyciprop(~S45IF1_1, design =.x, na.rm = T)),
influci = map(.x = influ1,
.f = ~confint(.x) %>%
as.data.frame() %>%
rownames_to_column("tar"))) %>%
unnest(cols = c(influci)) %>%
as.data.frame() %>%
mutate(ci_l = `2.5%`,
ci_h = `97.5%`,
vacunatipo = "influ1") %>%
select(ANIO,ci_l,ci_h,vacunatipo,vacuna)
neumo1<-
df2 %>%
mutate(
vacuna=map_dbl(.x = df,
.f =~svymean(~S45NM1_1, design =.x, na.rm = T)),
nuemo1=map(.x = df,
.f =~svyciprop(~S45NM1_1, design =.x, na.rm = T)),
neumoci = map(.x = nuemo1,
.f = ~confint(.x) %>%
as.data.frame() %>%
rownames_to_column("tar"))) %>%
unnest(cols = c(neumoci)) %>%
as.data.frame() %>%
mutate(ci_l = `2.5%`,
ci_h = `97.5%`,
vacunatipo = "neumo1") %>%
select(ANIO,ci_l,ci_h,vacunatipo,vacuna)
rota1<-
df2 %>%
mutate(
vacuna=map_dbl(.x = df,
.f =~svymean(~S45RT1_1, design =.x, na.rm = T)),
rota1=map(.x = df,
.f =~svyciprop(~S45RT1_1, design =.x, na.rm = T)),
rotaci = map(.x = rota1,
.f = ~confint(.x) %>%
as.data.frame() %>%
rownames_to_column("tar"))) %>%
unnest(cols = c(rotaci)) %>%
as.data.frame() %>%
mutate(ci_l = `2.5%`,
ci_h = `97.5%`,
vacunatipo = "rota1") %>%
select(ANIO,ci_l,ci_h,vacunatipo,vacuna)
rbind(ptv1,neumo1,influ1,rota1) %>%
as.data.frame() %>%
mutate(
vacunatipo = case_when(vacunatipo == "ptv1" ~ "PTV",
vacunatipo == "neumo1" ~ "NEUMO",
vacunatipo == "influ1" ~ "INFLU",
vacunatipo == "rota1" ~ "ROTA")
) %>%
ungroup() %>%
ggplot(aes(x = ANIO, y = vacuna*100,
ymin = ci_l*100, ymax = ci_h*100, group = vacunatipo))+
geom_line(aes(color = vacunatipo), size = 1.5) +
geom_ribbon(aes(fill = vacunatipo), alpha = 0.1)+
geom_vline(aes(xintercept = 2015), linetype = "dashed") +
scale_color_npg(alpha = 0.7)+
scale_fill_npg()+
xlab("Years") +
scale_x_continuous(expand = c(0, 0), breaks = c(2010:2020), limits = c(2010,2020.1))+
ylab("Prop. %")+
# geom_hline(aes(yintercept = 85), linetype = "dashed") +
theme_bw()+
theme(
axis.title = element_text(face ="bold", size = 11),
legend.text = element_text(size = 8, face = "bold"),
axis.text = element_text(face = "bold", family = "Arial"),
axis.text.x = element_text( size = 9),
legend.position = "top",
legend.title = element_blank(),
axis.line = element_line(colour = "black", size = 1),
panel.background = element_blank(),
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
panel.grid = element_blank()
)
ggsave("Fig1.png",width = 8, height = 6, dpi = 300)
coverage1 <- rbind(ptv1,neumo1,influ1,rota1)
coverage1 <-
coverage1 %>%
mutate(
across(.cols = c(ci_l,ci_h,vacuna), .fns = ~round(.x,3)*100),
estimate = paste0(vacuna," ","(",ci_l,"-",ci_h,")"),
vacunatipo = case_when(vacunatipo == "ptv1" ~ "Pentavalent",
vacunatipo == "rota1" ~ "Rotavirus",
vacunatipo == "neumo1" ~ "Pneumo",
vacunatipo == "influ1" ~ "Influenza")
)
```
# ggupset DE LAS 3 DOSIS DE VACUNACION
```{r}
#rm(list=ls())
# Base de trabajo
MOV_GENERAL <- read.csv("./NewData/mov_general.csv")
MOV_GENERAL<-MOV_GENERAL %>%
mutate(
PTVcompleta = ifelse(S45PV1_1 ==1 & S45PV2_1 == 1 & S45PV3_1 == 1,1,0),
NEUMOcompleta = ifelse(S45NM1_1==1 & S45NM2_1==1 & S45NM3_1==1,1,0),
INLFUcompelta = ifelse(S45IF1_1==1 & S45IF2_1==1,1,0),
ROTAcompleta = ifelse(S45RT1_1 ==1 & S45RT1_1==1,1,0),
vacuna5 = ifelse(PTVcompleta==1 & ROTAcompleta == 1 & INLFUcompelta==1 & NEUMOcompleta == 1, list(c("PTVc","ROTAc","INFLUc","NEUMOc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 0 & INLFUcompelta==0 & NEUMOcompleta == 0, list(c(NA)),
ifelse(PTVcompleta==1 & ROTAcompleta == 1 & INLFUcompelta==1 & NEUMOcompleta == 0, list(c("PTVc","ROTAc","INFLUc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 1 & INLFUcompelta==1 & NEUMOcompleta == 1, list(c("ROTAc","INFLUc","NEUMOc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 0 & INLFUcompelta==1 & NEUMOcompleta == 1, list(c("PTVc","INFLUc","NEUMOc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 1 & INLFUcompelta==0 & NEUMOcompleta == 1, list(c("PTVc","ROTAc","INFLUc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 1 & INLFUcompelta==0 & NEUMOcompleta == 0,list(c("PTVc","ROTAc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 0 & INLFUcompelta==1 & NEUMOcompleta == 1,list(c("INFLUc","NEUMOc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 0 & INLFUcompelta==0 & NEUMOcompleta == 1,list(c("PTVc","NEUMOc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 1 & INLFUcompelta==0 & NEUMOcompleta == 1,list(c("ROTAc","NEUMOc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 0 & INLFUcompelta==1 & NEUMOcompleta == 0,list(c("PTVc","INFLUc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 1 & INLFUcompelta==1 & NEUMOcompleta == 0,list(c("ROTAc","INFLUc")),
ifelse(PTVcompleta==1 & ROTAcompleta == 0 & INLFUcompelta==0 & NEUMOcompleta == 0,list(c("PTVc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 1 & INLFUcompelta==0 & NEUMOcompleta == 0,list(c("ROTAc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 0 & INLFUcompelta==1 & NEUMOcompleta == 0,list(c("INFLUc")),
ifelse(PTVcompleta==0 & ROTAcompleta == 0 & INLFUcompelta==0 & NEUMOcompleta == 1,list(c("NEUMOc")),NA)))))))))))))))),
year = ANIO)
```
```{r}
figura2a<-
ggplot(MOV_GENERAL) +
aes(x = vacuna5)+
geom_bar(fill = "#506D84")+
geom_text(stat='count', aes(label=after_stat(count)), vjust=-1, size = 3) +
theme_minimal()+
theme(
panel.grid = element_blank()
)+
labs(x = "Complete doses", y = "")+
scale_x_upset(n_intersections= 13) +
scale_y_continuous(breaks = NULL)
#ggsave("Fig2a.png", width = 10, height = 6)
figura2b<-
MOV_GENERAL %>%
select(DEPARTAMEN,PTVcompleta:ROTAcompleta) %>%
pivot_longer(cols = c(PTVcompleta:ROTAcompleta), names_to = "vacuna") %>%
filter(value == 0) %>%
group_by(DEPARTAMEN,vacuna) %>%
summarise(numero = n()) %>%
mutate(vacuna = recode(vacuna,"INLFUcompelta"="Influenza vaccine",
"NEUMOcompleta"="Pneumococcal vaccine",
"PTVcompleta"="Pentavalent vaccine",
"ROTAcompleta"="Rotavirus vaccine"),
percent = round((numero/sum(numero)*100)),
vacuna = factor(vacuna, levels = c("Influenza vaccine","Pneumococcal vaccine",
"Rotavirus vaccine","Pentavalent vaccine")))%>%
left_join(region_shape) %>%
st_as_sf() %>%
ggplot()+
geom_sf(aes(fill = percent), col = "#b6cfde") +
scale_fill_gradient(high ="#0C6291", low = "#e5e5e5")+
#scale_color_gradient(high ="#e5e5e5", low = "#e5e5e5") +
guides(fill = guide_colourbar(barheight = 0.5, barwidth = 25,title.position = "top", direction = "horizontal"))+
facet_wrap(~vacuna, nrow = 1)+
theme_minimal()+
labs(color = "", fill = "% of non-vaccinations by regions")+
theme(
legend.position = "bottom",
legend.title = element_text(size = 9, face = "bold"),
legend.text = element_text(size = 8),
panel.grid = element_blank(),
strip.text = element_text(size = 9, face = "bold"),
axis.text = element_text(size = 5))
#ggsave("figura2b.png", width = 8, height = 4)
figura2<-cowplot::plot_grid(figura2a,figura2b, ncol = 1, labels = c("a","b"), rel_heights = c(0.6,0.4))
ggsave("figura2.png", height = 9, width = 8.5, dpi = 300)
```
# PROPORCION POR CRED
Se incluyeron para el estudio los <5 anios que tenian CRED o SEGURO. En el grafico se muestra la evolucion de las OPV por vacuna en funcion de si tenian CRED o no (los que no tienen, es porque solo cuentan con seguro).
```{r, echo=F, warning=F, message=F, error=F}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~CRED, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTV") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~CRED, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~CRED, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~CRED, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
#full_join(region_shape, by="DEPARTAMEN") %>%
mutate(ANIO = as.character(ANIO))
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = CRED)) +
geom_col( position = position_dodge(width = 1), width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 1),
width = 0.25) +
scale_fill_manual(values = c("#9E5797","#6570C6")) +
facet_wrap(facets = ~ANIO, ncol = 4, scale = "free_x") +
theme_minimal() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
```
# PROPORCION POR LUGAR DONDE TIENE CRED
```{r, echo=F, warning=F, message=F, error=F, fig.height= 8, fig.width= 13}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~CREDLUGAR2, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTv") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~CREDLUGAR2, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~CREDLUGAR2, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~CREDLUGAR2, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
#full_join(region_shape, by="DEPARTAMEN") %>%
mutate(ANIO = as.character(ANIO))
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = CREDLUGAR2)) +
geom_col( position = position_dodge(width = 0.85), width = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 0.85),
width = 0.12) +
scale_fill_manual(values = c("#5D9C7C","#4F7D95","#9CB0A4","#4F97BF","#0090B7","#394B41")) +
facet_wrap(facets = ~ANIO, ncol = 3, scales = "free_x") +
theme_minimal() +
#coord_flip() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
#ggsave("cred.png",width = 18)
```
# CRED (ORIGEN)
```{r}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~tiposeguro, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTV") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~tiposeguro, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~tiposeguro, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~tiposeguro, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
#full_join(region_shape, by="DEPARTAMEN") %>%
mutate(ANIO = as.character(ANIO))
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = tiposeguro)) +
geom_col( position = position_dodge(width = 1), width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 1),
width = 0.25) +
scale_fill_manual(values = c("#9E5797","#6570C6")) +
facet_wrap(facets = ~ANIO, ncol = 4, scale = "free_x") +
theme_minimal() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
```
# TIPO DE RESIDENCIA
```{r, echo=F, warning=F, message=F, error=F}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~TIPORESIDENCIA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTV") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~TIPORESIDENCIA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~TIPORESIDENCIA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~TIPORESIDENCIA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
# full_join(region_shape, by="DEPARTAMEN") %>%
mutate(ANIO = as.character(ANIO))
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = TIPORESIDENCIA)) +
geom_col( position = position_dodge(width = 1), width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 1),
width = 0.25) +
scale_fill_manual(values = c("#9E5797","#6570C6")) +
facet_wrap(facets = ~ANIO, ncol = 4, scale = "free_x") +
theme_minimal() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
```
# EDUCACION DE LA MADRE
```{r, echo=F, warning=F, message=F, error=F}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~EDU_MADRE, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTV") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~EDU_MADRE, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~EDU_MADRE, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~EDU_MADRE, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
# full_join(region_shape, by="DEPARTAMEN") %>%
group_by(ANIO,vacunatipo) %>%
mutate(ANIO = as.character(ANIO),
EDUMADRE = case_when(EDU_MADRE == "sin educ" ~ 0,
EDU_MADRE == "primaria" ~ 1,
EDU_MADRE == "secundaria" ~ 2,
EDU_MADRE == "superior" ~ 3),
EDUMADRE = as.factor(EDUMADRE))
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = EDUMADRE)) +
geom_col( position = position_dodge(width = 0.85), width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 0.85),
width = 0.25) +
scale_fill_manual(values = c("#5D9C7C","#4F7D95","#9CB0A4","#4F97BF"),
labels = c("No education","Primary school","High school","Superiior education")) +
facet_wrap(facets = ~ANIO, ncol = 4, scale = "free_x") +
theme_minimal() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
```
# INDICE DE RIQUEZA
```{r, echo=F, warning=F, message=F, error=F, fig.width= 15, fig.height= 8}
df3<-df2 %>%
mutate(
ptv = map(.x = df ,
.f = ~svyby(~MOV_PTV_e1, by=~INDICERIQUEZA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_PTV_e1,vacunatipo = "PTV") %>%
select(-`se.as.numeric(MOV_PTV_e1)`,-MOV_PTV_e1) %>% data.frame),
neumo = map(.x = df ,
.f = ~svyby(~MOV_NEUMO_e1, by=~INDICERIQUEZA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_NEUMO_e1,vacunatipo = "NEUMO") %>%
select(-`se.as.numeric(MOV_NEUMO_e1)`,-MOV_NEUMO_e1) %>% data.frame),
influ = map(.x = df ,
.f = ~svyby(~MOV_INFLU_e1, by=~INDICERIQUEZA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_INFLU_e1,vacunatipo = "INFLU") %>%
select(-`se.as.numeric(MOV_INFLU_e1)`,-MOV_INFLU_e1) %>% data.frame),
rota = map(.x = df ,
.f = ~svyby(~MOV_ROTA_e1, by=~INDICERIQUEZA, design =.x, FUN=svyciprop, vartype=c('se','ci')) %>%
mutate(vacuna = MOV_ROTA_e1,vacunatipo = "ROTA") %>%
select(-`se.as.numeric(MOV_ROTA_e1)`,-MOV_ROTA_e1) %>% data.frame) ) %>%
select(-data) %>%
mutate(
pretotal1 = map2(.x = ptv, .y = neumo, .f = ~bind_rows(.x,.y)),
pretotal2 = map2(.x = influ, .y = rota, .f = ~bind_rows(.x,.y)),
total = map2(.x = pretotal1, .y = pretotal2, .f = ~bind_rows(.x,.y)),
) %>%
select(ANIO,total) %>%
ungroup() %>%
unnest(cols = c(total)) %>%
# full_join(region_shape, by="DEPARTAMEN") %>%
group_by(ANIO,vacunatipo) %>%
mutate(ANIO = as.character(ANIO))
#
#
ggplot(data = df3, aes(x = vacunatipo, y = vacuna, fill = INDICERIQUEZA)) +
geom_col( position = position_dodge(width = 0.85), width = 0.8, alpha = 0.8) +
geom_errorbar(aes(ymin = ci_l,
ymax = ci_u),
position = position_dodge(width = 0.85),
width = 0.25) +
scale_fill_manual(values = c("#5D9C7C","#4F7D95","#9CB0A4","#4F97BF","#394B41"),
labels = c("1st","2nd","3rd","4th","5th")) +
facet_wrap(facets = ~ANIO, ncol = 4, scale = "free_x") +
theme_minimal() +
theme(axis.text.x = element_text(size = 7),
strip.text = element_text(size = 9))
```