Cholera Data Insights (R Shiny web application)

Web Application Link: Cholera Data Insights

Loading required libraries:

library(shinythemes)     # For applying themes to the Shiny app

library(leaflet)         # For creating interactive maps

library(ggplot2)         # For creating charts and plots

library(dplyr)           # For data manipulation

library(readxl)          # For reading Excel files

 library(DT)              # For creating interactive data tables


# Load the dataset
# The Excel file 'cholera - geoT.xlsx' is assumed to be in the app folder. The data is loaded into 'my_data' and converted to a data frame 'my_data_df'.
my_data <- read_excel("cholera - geoT.xlsx")
my_data_df <- as.data.frame(my_data)

# Summarizing the dataset by country
# This section groups the data by 'Countries', and for each country calculates:
# - TotalCases: Sum of cholera cases
# - TotalFatalities: Sum of fatalities
# - Average Longitude and Latitude (for mapping purposes)
 country_summary <- my_data_df %>%
 group_by(Countries) %>%
 summarise(
TotalCases = sum(Cases, na.rm = TRUE),
TotalFatalities = sum(Fatalities, na.rm = TRUE),
Longitude = mean(Longitude, na.rm = TRUE),
Latitude = mean(Latitude, na.rm = TRUE)
)

UI (User Interface) part of the app

ui <- fluidPage(
theme = shinytheme("cerulean"),   # Use the 'cerulean' theme for a clean blue look

# Header section with title, centered and styled with background color and padding
tags$div(
style = "text-align: center; background-color: lightblue; padding: 20px; border-radius: 10px;",
h2("Cholera Data Insights")     # Display app title
),

# Navigation bar with two main tabs: Home and Data
navbarPage(
"",

# 'Home' tab contains a sidebar to select a country and a map displaying data
tabPanel("Home",
         sidebarPanel(
           # Dropdown to select a country or view data for all countries
           selectInput("selectCountry", "Choose a country:", choices = c("All Countries", unique(my_data_df$Countries)))
         ),
         mainPanel(
           leafletOutput("world_map")   # Display a world map with outbreak data
         )
),

# 'Data' tab has sub-tabs for visualizing statistics, cases, fatalities, and fatality rate
tabPanel("Data",
         sidebarPanel(
           # Dropdown to select a country and a year range for data filtering
           selectInput("selectCountryData", "Choose a country:", choices = c("All Countries", unique(my_data_df$Countries))),
           tags$h3("Select Year Range:"),   # Year range selection
           selectInput("selectYearRange", "Choose a year range:", choices = c("All Years", "1949-1959", "1960-1969", "1970-1979", "1980-1989", "1990-1999", "2000-2009", "2010-2016"))
         ),
         mainPanel(
           # Sub-tabs for different data visualizations and a download report button
           tabsetPanel(
             tabPanel("Statistics", plotOutput("outbreak_plot", height = "250px", width = "90%")),    # Plot for top outbreaks
             tabPanel("Number of Cases", plotOutput("Number_of_Cases")),                              # Plot for number of cases
             tabPanel("Number of Fatalities", plotOutput("Number_of_Fatalities")),                    # Plot for number of fatalities
             tabPanel("Fatality Rate", plotOutput("Fatality_Rate")),                                  # Plot for fatality rate
             tabPanel("Download Report", downloadButton("download_report", "Download Country Data Report"))  # Button to download report
           )
         )
)
)
)

Server logic part of the app

server <- function(input, output, session) {

# Reactive expression to filter the data based on selected country and year range
filtered_data <- reactive({
 req(input$selectCountryData)    # Ensure the country selection is available
filtered <- my_data_df

# Filter by selected country
if (input$selectCountryData != "All Countries") {
  filtered <- filtered %>% filter(Countries == input$selectCountryData)
}

# Filter by selected year range
if (input$selectYearRange != "All Years") {
  year_range <- strsplit(input$selectYearRange, "-")[[1]]
  start_year <- as.numeric(year_range[1])
  end_year <- as.numeric(year_range[2])
  filtered <- filtered %>% filter(Year >= start_year & Year <= end_year)
}

return(filtered)   # Return filtered data
})

# Output for total cases
output$total_cases <- renderText({
total_cases <- sum(filtered_data()$Cases, na.rm = TRUE)
paste("Total Cases:", total_cases)
})

 # Output for total fatalities
 output$fatalities <- renderText({
fatalities <- sum(filtered_data()$Fatalities, na.rm = TRUE)
paste("Total Fatalities:", fatalities)
  })

# Plot for top outbreak years
output$outbreak_plot <- renderPlot({
avg_cases <- my_data_df %>%
summarise(Average = mean(Cases, na.rm = TRUE)) %>%
  pull(Average)

# Filter and summarize data for years with higher than average cases
outbreak_data <- filtered_data() %>%
  filter(Cases > avg_cases) %>%
  group_by(Year) %>%
  summarise(TotalCases = sum(Cases, na.rm = TRUE)) %>%
  arrange(desc(TotalCases)) %>%
  slice_head(n = 20) %>%
  ungroup()

# Plotting a line chart with outbreak data
ggplot(outbreak_data, aes(x = factor(Year), y = TotalCases)) +
  geom_line(group = 1, color = "blue", linewidth = 1) +
  geom_point(color = "red") +
  labs(title = "Top Outbreak Years", x = "Years", y = "Number of Cases") +
  theme_minimal()
 })

# Render the world map using Leaflet
output$world_map <- renderLeaflet({
map <- leaflet(data = country_summary) %>%
  addTiles() %>%
  # Add circle markers for each country, with color based on total cases
  addCircleMarkers(
    lng = ~Longitude,
    lat = ~Latitude,
    color = ~colorQuantile("YlOrRd", TotalCases)(TotalCases),
   popup = ~paste("Country:", Countries, "<br>Total Cases:", TotalCases, "<br>Total Fatalities:", TotalFatalities)
 )

# Zoom in on the selected country if a specific country is chosen
if (input$selectCountry != "All Countries") {
  selected_country <- country_summary %>%
    filter(Countries == input$selectCountry)
  
  map <- map %>% setView(lng = selected_country$Longitude, lat = selected_country$Latitude, zoom = 4)
}

map   # Return the map
})

# Plot for the number of cases
output$Number_of_Cases <- renderPlot({
req(filtered_data())   # Ensure filtered data is available

# Plotting a bar chart for the number of cases
ggplot(filtered_data(), aes(x = Year, y = Cases)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = paste("Number of Cases in", input$selectCountryData), x = "Years", y = "Number of Cases") +
  theme_minimal()
})

# Plot for the number of fatalities
output$Number_of_Fatalities <- renderPlot({
req(filtered_data())

# Plotting a scatter plot for fatalities
ggplot(filtered_data(), aes(x = Year, y = Fatalities)) +
  geom_point(size = 3, color = "red") +
  labs(title = paste("Number of Fatalities in", input$selectCountryData), x = "Years", y = "Number of Fatalities") +
  theme_minimal()
})

# Plot for the fatality rate
output$Fatality_Rate <- renderPlot({
req(filtered_data())

# Plotting a line chart for the fatality rate (Fatalities/Cases * 100)
ggplot(filtered_data(), aes(x = Year)) +
  geom_line(aes(y = (Fatalities / Cases) * 100, color = "Fatality Rate")) +
  labs(title = paste("Fatality Rate in", input$selectCountryData), x = "Years", y = "Fatality Rate (%)") +
  theme_minimal()
})

 # Download report feature
output$download_report <- downloadHandler(
filename = function() {
  paste(input$selectCountryData, "cholera_report.xlsx", sep = "_")   # Filename format
},
content = function(file) {
  write.csv(filtered_data(), file)   # Write the filtered data to a CSV file
}
)
}

Run the app

shinyApp(ui = ui, server = server)