Integrating time series graphs and leaflet maps using R shiny

ONLINECAST

2 min read

·

07-10-2024

5

0

Share

Bringing Data to Life: Integrating Time Series Graphs and Leaflet Maps in R Shiny

Data visualization is a powerful tool for understanding complex information, especially when dealing with time series data and geographical locations. Combining interactive time series graphs with Leaflet maps allows for dynamic and engaging exploration of trends across space and time. This article will guide you through the process of integrating these elements in your R Shiny applications.

The Challenge:

Let's imagine you have a dataset containing air quality measurements from various locations across a city over time. You want to visualize the temporal trends at each location on a map and enable users to interact with the data by selecting a location and viewing its corresponding time series graph.

Scenario:

We'll use a sample dataset of air quality measurements in different parts of New York City for this demonstration.

# Sample data: NYC air quality measurements
data <- data.frame(
  date = seq.Date(from = as.Date("2023-01-01"), to = as.Date("2023-01-10"), by = "day"),
  location = c("Central Park", "Times Square", "Brooklyn Bridge", "Queensboro Bridge"),
  ozone = runif(40, 0, 100),
  pm25 = runif(40, 0, 50)
)

# Location coordinates
locations <- data.frame(
  location = c("Central Park", "Times Square", "Brooklyn Bridge", "Queensboro Bridge"),
  lat = c(40.7829, 40.7580, 40.7029, 40.7505),
  lon = c(-73.9654, -73.9855, -73.9956, -73.9788)
)

Code Example (R Shiny):

library(shiny)
library(leaflet)
library(ggplot2)

# User interface
ui <- fluidPage(
  titlePanel("NYC Air Quality"),
  sidebarLayout(
    sidebarPanel(
      selectInput("location", "Select Location", choices = unique(data$location))
    ),
    mainPanel(
      leafletOutput("map"),
      plotOutput("timeSeries")
    )
  )
)

# Server logic
server <- function(input, output) {
  
  # Leaflet map output
  output$map <- renderLeaflet({
    leaflet() %>%
      addTiles() %>%
      addMarkers(data = locations, lng = ~lon, lat = ~lat, popup = ~location)
  })
  
  # Time series graph output
  output$timeSeries <- renderPlot({
    locationData <- data[data$location == input$location, ]
    ggplot(locationData, aes(x = date, y = ozone)) +
      geom_line() +
      labs(x = "Date", y = "Ozone Level")
  })
}

# Run the app
shinyApp(ui, server)

Explanation and Insights:

This R Shiny application dynamically visualizes air quality data across different NYC locations. The code leverages Leaflet for creating the interactive map and ggplot2 for generating the time series graphs.

1. Map Creation: The leafletOutput and renderLeaflet functions create the Leaflet map with tiles and markers representing each location. Clicking on a marker displays the location name in a popup.
2. Interactive Selection: The selectInput widget allows users to choose a specific location.
3. Dynamic Graph Update: The renderPlot function updates the time series graph based on the selected location. This graph displays the ozone levels over time for the chosen location.

Key Points for Effective Integration:

• Data Structure: Ensure your data is organized in a way that allows for easy filtering and aggregation by location and time.
• Map and Graph Synchronization: Use reactive programming in Shiny to update the graph whenever the user selects a different location on the map.
• Customization: Explore Leaflet and ggplot2 options to customize the map appearance, graph aesthetics, and add interactive elements like tooltips or zoom features.

Conclusion:

Combining time series graphs and Leaflet maps in R Shiny provides a powerful platform for visualizing dynamic data trends across spatial locations. This approach fosters deeper understanding and insightful exploration of your data, offering valuable insights for decision-making and analysis.