#### Aula - Elaborando mapas no R

#coloque o comando setwd() abaixo


#############################################################################
###### INSTALANDO E CARREGANDO OS PACOTES QUE SERÃO UTILIZADOS
#############################################################################

#Tidyverse: Conjunto de pacotes para facilitar a manipulação e visualização de dados no R.
# Verifica e instala o pacote tidyverse se necessário
if (!require(tidyverse)) {
  install.packages("tidyverse")
  library(tidyverse)
}

#Geobr: Fornece acesso a dados geoespaciais do Brasil, como mapas e informações administrativas.
# Verifica e instala o pacote geobr se necessário
if (!require(geobr)) {
  install.packages("geobr")
  library(geobr)
}

#Magick: Interface para manipulação avançada de imagens, permitindo editar e processar imagens no R
# Verifica e instala o pacote magick se necessário
if (!require(magick)) {
  install.packages("magick")
  library(magick)
}

#viridis: Oferece esquemas de cores perceptivamente uniformes e adequados para representação de dados quantitativos

# Verifica e instala o pacote viridis se necessário
if (!require(viridis)) {
  install.packages("viridis")
  library(viridis)
}

#ggspatial usado para integrar dados espaciais (geoespaciais) em gráficos feitos com ggplot2, permitindo a visualização de informações geográficas em mapas e outras representações visuais.

# Verifica e instala o pacote ggspatial se necessário
if (!require(ggspatial)) {
  install.packages("ggspatial")
  library(ggspatial)
}


#gridExtra utilizado para organizar e combinar múltiplos gráficos feitos com o ggplot2 em uma única visualização, permitindo criar layouts personalizados com diferentes plots, facilitando a comparação e análise conjunta de múltiplos gráficos.

# Verifica e instala o pacote gridExtra se necessário
if (!require(gridExtra)) {
  install.packages("gridExtra")
  library(gridExtra)
}

# opção para colcoar escala e símbolo do norte
#https://github.com/oswaldosantos/ggsn

#############################################################################
###### VERIFICANDO SE VOCÊ JÁ INFORMOU AO R STUDIO QUAL É O DIRETÓRIO DE TRABALHO QUE ELE IRÁ CONSIDERAR PARA ACESSAR E SALVAR OS SEUS ARQUIVOS
#############################################################################

# Ainda na aba “Files”, clique em “ More” (com ícone de engrenagem em azul) e depois em “Set As Working Directory”

# Repare: no console, aparecerá o comando “setwd()”. Dentro dos parênteses deverá conter o caminho até a pasta que você escolheu

# Para verificar qual o diretório de trabalho está sendo trabalhado, utilize a função:

getwd()

#############################################################################
###### LIMPANDO OS OBJETOS DE OUTROS PROJETOS
#############################################################################


ls() #Verificando se há objetos usando a função

rm(list = ls()) # removendo os objetos

#############################################################################
###### LENDO E PLOTANDO O SHAPE FILE  
#############################################################################

# Pacote geobr
#https://cran.r-project.org/web/packages/geobr/vignettes/intro_to_geobr.html

# datasets disponíveis
datasets <- list_geobr()

str(datasets) #estrutura

names(datasets) #nomes das colunas

unique(datasets$`function`) #nomes das funções

unique(datasets$`geography`) # shps disponíveis

#Anos disponíveis para os estados
datasets %>%
  filter(geography == "States") %>%
  select(years)

#datasets[, c("geography", "years")]

#############################################################################
#### Treino: Anos disponíveis para as regiões




##########################

# Lendo o ano de 2020, para os estados

states_shp <- read_state(
  year = 2020,
  showProgress = FALSE
)


#############################################################################
#### Treino: Ler as regiões para o ano 2018





##########################
# Plotando todas as estados brasileiros
ggplot() +
  geom_sf(data = states_shp)


#############################################################################
#### Treino: Plotando todas as regiões brasileiros




##########################

#############################################################################
###### CRIANDO BANCO DE DADOS
#############################################################################

## fake dataset para estados
states_df <- data.frame(code_state = c(11, 12, 13, 14, 15, 16, 17, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 35, 41, 42, 43, 50, 51, 52, 53),
                        season  = c("Summer", "Autumn", "Winter", "Spring"),
                        cod_season  = 1:4,
                        year =  rep(c(2019, 2020), each = 27 * 4),
                        rate = sample(10:200,
                                      size = 216,
                                      replace = TRUE))

table(states_df$code_state, states_df$year, states_df$cod_season)

#  fake dataset para regiões
regions_df <- data.frame(
  code_region = 1:5,
  name_region = c("Norte", "Nordeste", "Sudeste", "Sul", "Centro Oeste"),
  season = c("Summer", "Autumn", "Winter", "Spring"),
  cod_season = 1:4,
  year = rep(c(2018, 2019), each = 5 * 4),
  rate = sample(10:200, size = 5 * 4 * 2,
                replace = TRUE)
)

table(regions_df$code_region, regions_df$year, regions_df$cod_season)

#############################################################################
###### UNINDO O MAPA COM A TABELA
#############################################################################

states_unido <- left_join(states_shp, states_df, by = "code_state")


#############################################################################
#### Treino: Unir as regiões



##########################


# conferindo
states_unido %>%
  filter(code_state == 12 & season == "Autumn" &
           year == 2019) %>%
  select(rate)


states_df %>%
  filter(code_state == 12 & season == "Autumn" &
           year == 2019) %>%
  select(rate)


# conferindo
states_unido %>%
  filter(code_state == 35 & season == "Winter" &
           year == 2020) %>%
  select(rate)


states_df %>%
  filter(code_state == 35 & season == "Winter" &
           year == 2020) %>%
  select(rate)


#############################################################################
###### ELABORANDO MAPAS
#############################################################################

#### plotando somente São Paulo
states_unido %>%
  filter(code_state == 35) %>%
  ggplot() +
  geom_sf()


#############################################################################
### Variáveis contínuas

# Taxa pelo ano de 2020, na estação "verão"

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate))

# Borda dos limites das áreas: vermelho
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), colour = "red")

# Borda dos limites das áreas: Sem cor
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), colour = NA)

# Fundo
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), colour = NA) +
  theme_classic()



# Cores de preenchimento

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_viridis_c() +  # Aplicando cores espectrais
  theme_classic()


states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_viridis_c(option = "magma") +  # Aplicando cores espectrais, magma
  theme_classic()


states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_gradientn(colors = c("red", "yellow", "green", "blue")) +  # Especificando as cores
  theme_classic()

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_gradientn(colors = c("blue", "green")) +  # Especificando as cores
  theme_classic()

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Spectral") +  # Especificando as cores
  theme_classic()

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Reds") +  # Especificando as cores
  theme_classic()


states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller() +  # Especificando as cores
  theme_classic()

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic()


# Remover os eixos

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(axis.line = element_blank(),  # Remove as linhas dos eixos
        axis.text = element_blank(),  # Remove os rótulos dos eixos
        #axis.title = element_blank(), # Remove os títulos dos eixos
        axis.ticks = element_blank()) # Remove as marcações nos eixos


# Inclue a escala

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(axis.line = element_blank(),  # Remove as linhas dos eixos
        axis.text = element_blank(),  # Remove os rótulos dos eixos
        axis.ticks = element_blank()) + # Remove as marcações nos eixos
  annotation_scale(location = "br")


# Inclue o simbolo do norte
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(axis.line = element_blank(),  # Remove as linhas dos eixos
        axis.text = element_blank(),  # Remove os rótulos dos eixos
        axis.ticks = element_blank()) + # Remove as marcações nos eixos
  annotation_scale(location = "br" #,
                   #bar_cols = c("grey", "white"),
                   #height = unit(0.5, "cm")
                   ) +
  annotation_north_arrow(location = "br",
                         which_north = "true",
                         pad_x = unit(.9, "cm"),
                         pad_y = unit(.9, "cm"),
                         style = north_arrow_fancy_orienteering)


states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(axis.line = element_blank(),  # Remove as linhas dos eixos
        axis.text = element_blank(),  # Remove os rótulos dos eixos
        axis.ticks = element_blank()) + # Remove as marcações nos eixos
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "br",
                         which_north = "true",
                         pad_x = unit(.9, "cm"),
                         pad_y = unit(.9, "cm"),
                         style = north_arrow_minimal)

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(axis.line = element_blank(),  # Remove as linhas dos eixos
        axis.text = element_blank(),  # Remove os rótulos dos eixos
        axis.ticks = element_blank()) + # Remove as marcações nos eixos
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "br",
                         which_north = "true",
                         pad_x = unit(.9, "cm"),
                         pad_y = unit(.9, "cm"),
                         style = north_arrow_nautical)

# Mais opções:
#https://search.r-project.org/CRAN/refmans/ggspatial/html/north_arrow_orienteering.html


### Título da legenda
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm")  # Largura dos elementos da legenda
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )



# Legenda do preenchimento: opções
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.position = "bottom"  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )

states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.title = element_blank(), # Remove os títulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.position = "top"  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )


states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.title = element_blank(), # Remove os títulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.position = "left"  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )


### JUstificado
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.justification = c(0, 0)  # Justificação da legenda (x, y)
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )

# Posição da legenda
states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.position = c(0.8, 0.2),  # Posição da legenda
    legend.justification = c(0, 0)  # Justificação da legenda (x, y)
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )



states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA) +
  scale_fill_distiller(name = "Taxa", palette = "Greys") + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 18, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    legend.position = c(0.8, 0.2)  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )



#############################################################################
###### MÚLTIPLOS MAPAS
#############################################################################


range(states_unido$rate) # amplitude da taxa

plot_2019 <- states_unido %>%
  filter(year == 2019 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA, show.legend = F) + ### tirar a legenda
  scale_fill_distiller(name = "Taxa", palette = "Greys", limits = c(10, 200)) + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    # legend.title = element_text(size = 12, face = "bold"),  # Título da legenda
    # legend.text = element_text(size = 10),  # Tamanho do texto da legenda
    # legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    # legend.position = c(0.8, 0.2)  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  labs(title = "2019", size = 30) + ## Texto
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )

plot_2020 <- states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = rate), color = NA, show.legend = T) +
  scale_fill_distiller(name = "Taxa", palette = "Greys", limits = c(10, 200)) + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 20, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 14),  # Tamanho do texto da legenda
    legend.key.width = unit(0.5, "cm"),  # Largura dos elementos da legenda
    legend.position = c(0.8, 0.2)  # Posição da legenda
  ) +
  #annotation_scale(location = "br") +
  labs(title = "2020", size = 30) # +
# annotation_north_arrow(
#   location = "br",
#   which_north = "true",
#   pad_x = unit(.9, "cm"),
#   pad_y = unit(.9, "cm"),
#   style = north_arrow_nautical
#)



# Abre o dispositivo de saída TIFF
tiff("mapa_por_ano.tiff", width = 35, height = 17, units = "cm", res = 300, 
     # compression = "lzw"
)

grid.arrange( plot_2019, plot_2020, ncol = 2)
dev.off()

## outros formatos

jpeg("mapa_por_ano.jpeg", width = 35, height = 17, units = "cm", res = 300, 
    # compression = "lzw"
     )

grid.arrange( plot_2019, plot_2020, ncol = 2)
dev.off()




# Loop para criar os mapas para cada estação e atribuir a objetos separados
for (season in c("Summer", "Spring", "Winter", "Autumn")) {
  # Filtra os dados para a estação atual no loop
  current_plot <- states_unido %>%
    filter(year == 2020 & season == season) %>%
    ggplot() +
    geom_sf(aes(fill = rate), color = NA) +
    scale_fill_distiller(name = "Taxa", palette = "Spectral", limits = c(10, 200)) + # Especificando as cores
    theme_classic() +
    theme(
      axis.line = element_blank(),  # Remove as linhas dos eixos
      axis.text = element_blank(),  # Remove os rótulos dos eixos
      axis.title = element_blank(), # Remove os títulos dos eixos
      axis.ticks = element_blank(), # Remove as marcações nos eixos
      legend.title = element_text(size = 9, face = "bold"),  # Título da legenda
      legend.text = element_text(size = 6),  # Tamanho do texto da legenda
      legend.key.width = unit(0.2, "cm"),  # Largura dos elementos da legenda
      legend.position = c(0.9, 0.4)  # Posição da legenda
    ) +
    labs(title = season) +
    annotation_scale(location = "br") +
    annotation_north_arrow(
      location = "br",
      which_north = "true",
      pad_x = unit(1.2, "cm"),
      pad_y = unit(.8, "cm"),
      style = north_arrow_nautical
    )
  
  # Atribui o gráfico atual a um objeto separado com o nome da estação
  assign(paste0(season, "_plot"), current_plot)
}


grid.arrange( Summer_plot, Spring_plot, Winter_plot, Autumn_plot, ncol = 2)

#############################################################################
###### MAPA CATEGÓRICO
#############################################################################


################### CRIANDO CTEGORIAS SEGUNDO QUANTIL

quantile(states_unido$rate) # quantil em 4 categorias

quantile(states_unido$rate, prob = seq(0, 1, 1/4)) # quantil em 4 categorias

quantile(states_unido$rate, prob = seq(0, 1, 1/5)) # quantil em 5 categorias

states_unido$quantil <- NA

states_unido$quantil[states_unido$rate < 49] <- "0-48"
states_unido$quantil[states_unido$rate >= 49 & states_unido$rate < 99] <- "49-98"
states_unido$quantil[states_unido$rate >= 99 & states_unido$rate < 148] <- "99-147"
states_unido$quantil[states_unido$rate >= 148] <- "148 ou mais"

table(states_unido$quantil, useNA = "always") # conferência, faz uma tabela de valores por categoria e mostra os valores NA

# Transformando em factor e reordenando os níveis
states_unido$quantil <- factor(states_unido$quantil, levels =  c("0-48", "49-98", "99-147", "148 ou mais"))

# Verifique os níveis atuais
levels(states_unido$quantil)



###### MAPAS

# table(states_unido$quantil, states_unido$season,  states_unido$year) # conferência, faz uma tabela de valores por categoria e mostra os valores NA


# Suponha que você tem uma paleta de cores para cada nível
cores <- c("0-48" = "#fee5d9",
           "49-98" = "#fcae91",
           "99-147" = "#fb6a4a",
           "148 ou mais" = "#cb181d")


plot_2019_cat <- states_unido %>%
  filter(year == 2019 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = quantil), color = NA, show.legend = F) +
  scale_fill_manual(values = cores) + # Especificando as cores
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.title = element_blank(), # Remove os títulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    # legend.title = element_text(size = 12, face = "bold"),  # Título da legenda
    # legend.text = element_text(size = 10),  # Tamanho do texto da legenda
    # legend.key.width = unit(1, "cm"),  # Largura dos elementos da legenda
    # legend.position = c(0.8, 0.2)  # Posição da legenda
  ) +
  annotation_scale(location = "br") +
  labs(title = "2019") +
  annotation_north_arrow(
    location = "br",
    which_north = "true",
    pad_x = unit(.9, "cm"),
    pad_y = unit(.9, "cm"),
    style = north_arrow_nautical
  )

plot_2020_cat <- states_unido %>%
  filter(year == 2020 & season == "Summer") %>%
  ggplot() +
  geom_sf(aes(fill = quantil), color = NA, show.legend = T) +
  scale_fill_manual(name = "Taxa", values = cores) + # Especificando as cores  
  theme_classic() +
  theme(
    axis.line = element_blank(),  # Remove as linhas dos eixos
    axis.text = element_blank(),  # Remove os rótulos dos eixos
    axis.title = element_blank(), # Remove os títulos dos eixos
    axis.ticks = element_blank(), # Remove as marcações nos eixos
    legend.title = element_text(size = 14, face = "bold"),  # Título da legenda
    legend.text = element_text(size = 12),  # Tamanho do texto da legenda
    legend.key.width = unit(0.5, "cm"),  # Largura dos elementos da legenda
    legend.position = c(0.82, 0.2)  # Posição da legenda
  ) +
  #annotation_scale(location = "br") +
  labs(title = "2020") # +
# annotation_north_arrow(
#   location = "br",
#   which_north = "true",
#   pad_x = unit(.9, "cm"),
#   pad_y = unit(.9, "cm"),
#   style = north_arrow_nautical
#)

getwd()
# Abre o dispositivo de saída TIFF
tiff("mapa_por_ano_categorias.tiff", width = 35, height = 17, units = "cm", res = 300, compression = "lzw")


grid.arrange( plot_2019_cat, plot_2020_cat, ncol = 2)


dev.off()



#############################################################################
#### Treino: Elaboro um múltiplos mapas segundo regiões em escala de cores laranjas




##########################