# Load required libraries
library(bangladesh)
library(ggplot2)
library(leaflet)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# Load dengue dataset
dengue <- read.csv("E:/Dr.Nirob/meleria/dataset.csv", stringsAsFactors = FALSE)

# Load upazila shapefile from bangladesh package
data("map_upazila")
upazila <- map_upazila

# Filter for Dhaka district
dhaka_map <- upazila[upazila$District == "Dhaka", ]

# Aggregate dengue cases by area
dhaka_cases <- aggregate(Outcome ~ Area, data = dengue, sum)
colnames(dhaka_cases)[1] <- "Upazila"  # rename to match shapefile column

# Merge dengue case data with shapefile
dhaka_map$Cases <- dhaka_cases$Outcome[match(dhaka_map$Upazila, dhaka_cases$Upazila)]
dhaka_map$Cases[is.na(dhaka_map$Cases)] <- 0  # fill NAs with 0

# Plot static map using ggplot2
ggplot(dhaka_map) +
  geom_sf(aes(fill = Cases)) +
  scale_fill_gradient(low = "green", high = "red") +
  theme_minimal() +
  labs(title = "Dengue Cases in Dhaka District", fill = "Number of Cases")

# Interactive map using leaflet
pal <- colorNumeric(palette = "Reds", domain = dhaka_map$Cases)

leaflet(dhaka_map) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(
    fillColor = ~pal(Cases),
    color = "black", weight = 1, opacity = 1, fillOpacity = 0.7,
    highlightOptions = highlightOptions(
      weight = 3, color = "blue", fillOpacity = 0.9, bringToFront = TRUE),
    label = ~paste0(Upazila, ": ", Cases, " case(s)"),
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px", direction = "auto")
  ) %>%
  addLegend(pal = pal, values = ~Cases, title = "Number of Dengue Cases", position = "bottomright")

# Gender distribution pie chart
gender_count <- as.data.frame(table(dengue$Gender))
colnames(gender_count) <- c("Gender", "Count")

plot_ly(gender_count, labels = ~Gender, values = ~Count, type = 'pie') %>%
  layout(title = 'Gender Distribution of Dengue Patients',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

# Bar chart of dengue cases by area
area_cases <- aggregate(Outcome ~ Area, data = dengue, sum)

plot_ly(area_cases, x = ~Area, y = ~Outcome, type = "bar",
        text = ~Outcome, textposition = "auto") %>%
  layout(title = "Dengue Cases by Area (Dhaka)",
         xaxis = list(title = "Area"), yaxis = list(title = "Positive Cases"))

# Pie chart by house type
house_cases <- aggregate(Outcome ~ HouseType, data = dengue, sum)

plot_ly(house_cases, labels = ~HouseType, values = ~Outcome, type = 'pie') %>%
  layout(title = "Cases by House Type")

# Pie chart by area type
area_type_cases <- aggregate(Outcome ~ AreaType, data = dengue, sum)

plot_ly(area_type_cases, labels = ~AreaType, values = ~Outcome, type = 'pie') %>%
  layout(title = "Dengue Cases by Area Type")