Script para gerar gráfico com sazonalidade de doenças respiratórias

sazonalidade_doencas_respiratorias.rmd

---
title: "storytelling_sazonalidade_respiratoria"
author: "Fernando Almeida Barbalho"
date: "2026-04-24"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(tidyverse)
library(geobr)
library(colorspace)

participacao_mes <- readRDS("~/github/labs_microdatasus/participacao_mes.rds")

sedes<- geobr::read_municipal_seat()
municipios<- geobr::read_municipality(simplified = FALSE) 
regioes<- geobr::read_region()

theme_vpr<- function(paleta_cores){
  
  theme_light() +
    theme(
      panel.background = element_rect(fill= paleta_cores$cor_fundo_painel),
      panel.grid = element_blank(),
      legend.position = "right",
      legend.background = element_rect(fill= paleta_cores$cor_fundo, color = paleta_cores$cor_fundo),
      legend.title = element_blank(),
      legend.text = element_text(size = 8),
      plot.background = element_rect(fill =paleta_cores$cor_fundo ),
      plot.title = element_text(size = 12, face = "bold"),
      plot.subtitle = element_text(size = 10, face = "italic"),
      text = element_text( colour = paleta_cores$cor_texto),
      strip.background = element_rect(fill = paleta_cores$cor_fundo  ),
      strip.text = element_text(size= 8, face ="bold", color = paleta_cores$cor_texto)
    )  
  
  
}


paleta_cores<- list(  cor_fundo= "#D0D0D0", # "#E0E0E0", # # "#F8F8F8",
                      
                      cor_fundo_painel = "#F5F5F5",
                      
                      cor_fundo_estados = "#696969",
                      
                      cor_fundo_mapa_pontos = "#2B2B2B", #"#505050"
                      
                      cor_texto = "#324e5a"
)


```


```{r fig.dpi= 300}

sedes_convert<-
  extract(sedes, geom, into = c('Lon', 'Lat'), '\\((.*),(.*)\\)', conv = T) %>%
  janitor::clean_names()

base_dados_mapa_sazonalidade<-
  sedes_convert %>%
  mutate(munic_res = as.character(code_muni),
         munic_res = str_sub(code_muni,1,6)) %>%
  inner_join(participacao_mes) %>%
  mutate(
    mes_internacao = factor(
      mes_internacao,
      levels = 1:12,
      labels = c("Jan","Feb","Mar","Apr","May","Jun",
                 "Jul","Aug","Sep","Oct","Nov","Dec")
    ))

dados_mapa_sazonalidade<-
  base_dados_mapa_sazonalidade %>%
  bind_rows(
    base_dados_mapa_sazonalidade %>%
      mutate(name_region = "Brasil")  
  ) %>%
  inner_join(
    tibble(
      name_region = c("Norte", "Nordeste", "Centro Oeste", "Sul", "Sudeste", "Brasil"),
      name_region_en = c("North", "Northeast", "Central-West", "South", "Southeast", "Brazil")
    )) %>%
  
  mutate(name_region_en = factor(name_region_en, levels = c("North", "Northeast", "Central-West", "Southeast", "South", "Brazil")))


dados_resumo<-
  dados_mapa_sazonalidade %>%
  summarise(desvio_padrao_participacao = sd(participacao_internacao_mes),
            mediana_latitude = median(lat),
            media_latitude =mean(lat),
            .by = name_region_en)


dados_mapa_sazonalidade %>%
  ggplot(aes(x= mes_internacao, y=participacao_internacao_mes)) +
  geom_boxplot(aes(color = name_region_en), 
               fill=NA, 
               outliers = FALSE,
            show.legend = FALSE)  +
  geom_text(data= dados_resumo, 
            aes(x=1, y=0.28, label = paste0("Median latitude:  ", scales::number(abs(mediana_latitude), accuracy = 0.0001, decimal.mark = ","),"ºS"  ), colour = name_region_en),
            size = 3,
            hjust =0,
            fontface = "bold",
            show.legend = FALSE) +
  scale_color_discrete_qualitative(palette = "Dark 2") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1.0), expand = expansion(mult = c(0.01,0.1))) +
  theme_vpr(paleta_cores = paleta_cores) +
  theme(axis.text = element_text(size = 6 ),
        axis.title = element_text(size = 8))+
  labs(
    title = "More defined seasons, clearer seasonality in Brazil",
    subtitle = "The South and Southeast regions exhibit more pronounced cycles of respiratory hospitalizations in 2024,\n whereas lower-latitude regions show more stable levels with only isolated fluctuations.",
    caption = "Data refer to ICD-10 respiratory diseases (Chapter J). Source: DATASUS (2024). Prepared by Fernando Barbalho.",
    x= "Month of hospitalization",
    y= "Distribution (%) of respiratory hospitalizations") +
  facet_wrap(name_region_en~.)



  


```