## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval = FALSE-------------------------------------------------------------
# # Load Brazilian municipalities dataset
# data <- datazoom.amazonia::municipalities
# 
# # Or after loading the package
# library(datazoom.amazonia)
# data <- municipalities
# 
# # View structure
# str(municipalities)
# head(municipalities)
# 
# # Filter for Legal Amazon municipalities only
# amazon_municipalities <- municipalities %>%
#   filter(legal_amazon == TRUE)

## ----eval=FALSE---------------------------------------------------------------
# library(dplyr)
# # Load any dataset with municipality information
# data <- load_prodes(
#   dataset = "deforestation",
#   raw_data = FALSE,
#   geo_level = "municipality",
#   language = "eng"
# )
# 
# # Filter to Legal Amazon using municipalities reference
# amazon_data <- data %>%
#   inner_join(
#     municipalities %>%
#       filter(legal_amazon == TRUE) %>%
#       select(code, name),
#     by = c("municipality_code" = "code")
#   )

## ----eval=FALSE---------------------------------------------------------------
# # Identify fully vs. partially included municipalities
# full_amazon <- municipalities %>%
#   filter(legal_amazon == TRUE & fully_included == TRUE)
# 
# partial_amazon <- municipalities %>%
#   filter(legal_amazon == TRUE & fully_included == FALSE)
# 
# print(paste("Fully in Amazon:", nrow(full_amazon)))
# print(paste("Partially in Amazon:", nrow(partial_amazon)))

## ----eval=FALSE---------------------------------------------------------------
# # Check for partial municipalities
# partial_check <- municipalities %>%
#   filter(legal_amazon == TRUE) %>%
#   filter(!is.na(amazon_percentage)) %>%
#   filter(amazon_percentage < 100)
# 
# if (nrow(partial_check) > 0) {
#   print("Municipalities partially in Legal Amazon:")
#   print(partial_check)
# }

## ----eval=FALSE---------------------------------------------------------------
# library(sf)
# library(ggplot2)
# 
# # Load municipalities with geometry
# municipalities_sf <- municipalities %>%
#   st_as_sf()  # If not already SF format
# 
# # Map Legal Amazon
# amazon_map <- municipalities_sf %>%
#   filter(legal_amazon == TRUE)
# 
# ggplot(amazon_map) +
#   geom_sf(fill = "lightgreen", color = "darkgreen") +
#   labs(title = "Legal Amazon Municipalities") +
#   theme_minimal()
# 
# # Spatial operations example: count municipalities by state
# munic_by_state <- municipalities_sf %>%
#   group_by(state) %>%
#   summarize(
#     num_municipalities = n(),
#     total_area_km2 = sum(st_area(.), na.rm = TRUE) / 1e6,
#     .groups = 'drop'
#   )