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Radarplot_departamentos.R
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Radarplot_departamentos.R
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library(ggradar)
library(tidyverse)
library(scales)
library(survey)
library(fmsb)
df_gestantes<- read.csv("./data/datatest.csv")
###################
df_gestantes2<-
df_gestantes %>%
mutate(
CHECKUP_RULE_OUT_HIV = ifelse(is.na(CHECKUP_RULE_OUT_HIV),"NO",CHECKUP_RULE_OUT_HIV)
)
##################
df<-
df_gestantes2 %>%
mutate(
CHECKUP_RULE_OUT_HIV2 = ifelse(is.na(CHECKUP_RULE_OUT_HIV),"Missing",
ifelse(CHECKUP_RULE_OUT_HIV == 1,"Yes",
ifelse(CHECKUP_RULE_OUT_HIV == 0 | CHECKUP_RULE_OUT_HIV == 8 , "No",CHECKUP_RULE_OUT_HIV))),
CHECKUP_RULE_OUT_HIV2 = as.factor(CHECKUP_RULE_OUT_HIV2)
) %>%
group_by(year) %>%
nest() %>%
mutate(
datasvy = map(.x = data,
.f = ~svydesign(id =~ V001, strata =~ V022, weights=~V005, data=.x))
)
options(survey.lonely.psu="remove")
#################
df2<-
df %>%
mutate(
vih = map(.x = datasvy,
.f = ~svyby(~as.factor(CHECKUP_RULE_OUT_HIV), by = ~as.factor(DEPARTAMEN), design = .x, FUN =svyciprop, na.rm.all = T))
) %>%
unnest(vih) %>%
mutate(
across(`as.factor(CHECKUP_RULE_OUT_HIV)`:`se.as.numeric(as.factor(CHECKUP_RULE_OUT_HIV))`,.fns = ~round(.,2)*100)
) %>%
rename(
DEPARTAMEN = `as.factor(DEPARTAMEN)`,
`HIV Screening` = `as.factor(CHECKUP_RULE_OUT_HIV)`
)
##### Grafico ###########
df2 %>%
select(year,`HIV Screening`,DEPARTAMEN) %>%
group_by(DEPARTAMEN,year) %>%
arrange(.by_group = T) %>%
pivot_wider(names_from = "year", values_from = `HIV Screening`) %>%
ggradar(grid.max = 100)
##########################
df3<-
df2 %>%
select(year,`HIV Screening`,DEPARTAMEN) %>%
group_by(DEPARTAMEN,year) %>%
arrange(.by_group = T) %>%
pivot_wider(names_from = "year", values_from = `HIV Screening`) %>%
ungroup() %>%
mutate(
macroreg = ifelse(DEPARTAMEN == "TUMBES"|DEPARTAMEN == "PIURA"|
DEPARTAMEN == "LAMBAYEQUE"|DEPARTAMEN == "LA LIBERTAD"|
DEPARTAMEN == "CAJAMARCA"|DEPARTAMEN == "AMAZONAS"|
DEPARTAMEN == "SAN MARTIN","Macro NO",
ifelse(DEPARTAMEN == "ANCASH"|DEPARTAMEN == "UCAYALI"|
DEPARTAMEN == "JUNIN"|DEPARTAMEN == "HUANUCO"|
DEPARTAMEN == "PASCO"|DEPARTAMEN == "HUANCAVELICA","MACRO C",
ifelse(DEPARTAMEN == "ICA"|DEPARTAMEN == "AREQUIPA"|
DEPARTAMEN == "MOQUEGUA"|DEPARTAMEN == "TACNA","Macro SO",
ifelse(DEPARTAMEN == "CUSCO"|DEPARTAMEN == "PUNO"|
DEPARTAMEN == "AYACUCHO"|DEPARTAMEN == "MADRE DE DIOS"|
DEPARTAMEN == "APURIMAC","Macro SE",
ifelse(DEPARTAMEN == "LORETO", "macro NE",
ifelse(DEPARTAMEN == "LIMA"|
DEPARTAMEN == "CALLAO","Macro Lima",DEPARTAMEN))))))
)
color <- c("#5F4690","#1D6996","#38A6A5","#0F8554",
"#73AF48","#EDAD08","#E17C05","#CC503E",
"#94346E","#6F4070","#994E95","#666666")
blue_fall <- c("#eaac8b","#e56b6f","#b56576","#6d597a", "#355070","red"
)
a<-
df3 %>%
#select(-DEPARTAMEN) %>%
mutate(
macroreg = as.character(macroreg),
DEPARTAMEN = as.character(DEPARTAMEN)
) %>%
group_by(macroreg) %>%
nest() %>%
mutate(
grafico = map(.x = data,
.f = ~ggradar(.x, grid.max = 100,
legend.text.size = 8,
legend.position = "bottom", group.colours = color))
)
cowplot::plot_grid(plotlist = a$grafico, ncol = 3, labels = "auto")
ggsave("test.jpg",width = 22, height = 11, dpi = 300)
a$grafico[[1]]