♻️ refactor app.Rmd

src/app.Rmd

@@ -12,6 +12,7 @@ library(httr)
 library(jsonlite)
 library(trajr)
 
+
 # UI Definition
 ui <- fluidPage(
   titlePanel("Flight Trajectory Analysis - GUI"),
@@ -214,20 +215,7 @@ server <- function(input, output, session) {
       rv$current_route <- route_df
       
       # Create trajectory object
-      lat_ref <- route_df$lat[1]
+      rv$current_trj <- createTrajFromRoute(route_df)
-      lon_ref <- route_df$lon[1]
-      meters_per_deg_lat <- 111320
-      meters_per_deg_lon <- 111320 * cos(lat_ref * pi / 180)
-      
-      x_meters <- (route_df$lon - lon_ref) * meters_per_deg_lon
-      y_meters <- (route_df$lat - lat_ref) * meters_per_deg_lat
-      time_seconds <- route_df$time - route_df$time[1]
-      
-      rv$current_trj <- TrajFromCoords(
-        data.frame(x = x_meters, y = y_meters, time = time_seconds),
-        xCol = "x", yCol = "y", timeCol = "time"
-      )
-      
       status(paste("Successfully analyzed", icao24, "with", nrow(route_df), "points"))
       
       # Switch to analysis tab
@@ -266,41 +254,8 @@ server <- function(input, output, session) {
     
     trj <- rv$current_trj
     
-    duration <- TrajDuration(trj)
+    data.frame <- calculateRouteCharacteristics(trj)
-    path_length <- TrajLength(trj)
+    data.frame
-    diffusion_distance <- TrajDistance(trj)
-    straightness <- TrajStraightness(trj)
-    mean_velocity <- path_length / duration
-    
-    fractal_dim <- tryCatch({
-      min_step <- path_length / 100
-      max_step <- path_length / 2
-      if (min_step > 0 && max_step > min_step) {
-        step_sizes <- exp(seq(log(min_step), log(max_step), length.out = 10))
-        TrajFractalDimension(trj, stepSizes = step_sizes)
-      } else {
-        NA
-      }
-    }, error = function(e) NA)
-    
-    data.frame(
-      Parameter = c(
-        "Duration (s)", "Duration (min)",
-        "Path Length (m)", "Path Length (km)",
-        "Diffusion Distance (m)", "Diffusion Distance (km)",
-        "Straightness Index",
-        "Mean Velocity (m/s)", "Mean Velocity (km/h)",
-        "Fractal Dimension"
-      ),
-      Value = c(
-        round(duration, 2), round(duration / 60, 2),
-        round(path_length, 2), round(path_length / 1000, 2),
-        round(diffusion_distance, 2), round(diffusion_distance / 1000, 2),
-        round(straightness, 4),
-        round(mean_velocity, 2), round(mean_velocity * 3.6, 2),
-        round(fractal_dim, 4)
-      )
-    )
   })
   
   # Batch analysis
@@ -337,19 +292,7 @@ server <- function(input, output, session) {
             route_df <- as.data.frame(track_data$path)
             colnames(route_df) <- c("time", "lat", "lon", "alt", "heading", "on_ground")
             
-            lat_ref <- route_df$lat[1]
+            trj <- createTrajFromRoute(route_df)
-            lon_ref <- route_df$lon[1]
-            meters_per_deg_lat <- 111320
-            meters_per_deg_lon <- 111320 * cos(lat_ref * pi / 180)
-            
-            x_meters <- (route_df$lon - lon_ref) * meters_per_deg_lon
-            y_meters <- (route_df$lat - lat_ref) * meters_per_deg_lat
-            time_seconds <- route_df$time - route_df$time[1]
-            
-            trj <- TrajFromCoords(
-              data.frame(x = x_meters, y = y_meters, time = time_seconds),
-              xCol = "x", yCol = "y", timeCol = "time"
-            )
             
             duration <- TrajDuration(trj)
             path_length <- TrajLength(trj)
@@ -402,156 +345,239 @@ server <- function(input, output, session) {
   # Statistics summary table
   output$stats_summary_table <- renderTable({
     req(rv$trajectory_stats_df)
-    
+    calculateStatsSummary(rv$trajectory_stats_df)
-    params <- c("diffusion_distance_km", "straightness", "duration_min", 
-                "mean_velocity_kmh", "fractal_dimension")
-    labels <- c("Diffusion Distance (km)", "Straightness", "Duration (min)", 
-                "Mean Velocity (km/h)", "Fractal Dimension")
-    
-    stats_list <- lapply(seq_along(params), function(i) {
-      x <- rv$trajectory_stats_df[[params[i]]]
-      x <- x[!is.na(x)]
-      if (length(x) < 2) return(NULL)
-      
-      data.frame(
-        Parameter = labels[i],
-        N = length(x),
-        Mean = round(mean(x), 4),
-        Variance = round(var(x), 4),
-        Std_Dev = round(sd(x), 4),
-        Q1 = round(quantile(x, 0.25), 4),
-        Median = round(median(x), 4),
-        Q3 = round(quantile(x, 0.75), 4)
-      )
-    })
-    
-    do.call(rbind, stats_list[!sapply(stats_list, is.null)])
   })
   
   # Boxplots
   output$boxplots <- renderPlot({
     req(rv$trajectory_stats_df)
-    
+    createBoxplots(rv$trajectory_stats_df)
-    params <- c("diffusion_distance_km", "straightness", "duration_min", 
-                "mean_velocity_kmh", "fractal_dimension")
-    labels <- c("Diffusion Distance (km)", "Straightness", "Duration (min)", 
-                "Mean Velocity (km/h)", "Fractal Dimension")
-    
-    par(mfrow = c(2, 3))
-    for (i in seq_along(params)) {
-      data <- rv$trajectory_stats_df[[params[i]]][!is.na(rv$trajectory_stats_df[[params[i]]])]
-      if (length(data) >= 2) {
-        boxplot(data, main = labels[i], ylab = labels[i], col = "lightblue", border = "darkblue")
-        points(1, mean(data), pch = 18, col = "red", cex = 1.5)
-      }
-    }
-    par(mfrow = c(1, 1))
   })
   
   # Density plots
   output$density_plots <- renderPlot({
     req(rv$trajectory_stats_df)
-    
+    createDensityPlots(rv$trajectory_stats_df)
-    params <- c("diffusion_distance_km", "straightness", "duration_min", 
-                "mean_velocity_kmh", "fractal_dimension")
-    labels <- c("Diffusion Distance (km)", "Straightness", "Duration (min)", 
-                "Mean Velocity (km/h)", "Fractal Dimension")
-    
-    par(mfrow = c(2, 3))
-    for (i in seq_along(params)) {
-      data <- rv$trajectory_stats_df[[params[i]]][!is.na(rv$trajectory_stats_df[[params[i]]])]
-      if (length(data) >= 3) {
-        dens <- density(data)
-        plot(dens, main = paste("Density:", labels[i]), xlab = labels[i], col = "darkblue", lwd = 2)
-        polygon(dens, col = rgb(0, 0, 1, 0.3), border = "darkblue")
-        abline(v = mean(data), col = "red", lwd = 2, lty = 2)
-        abline(v = median(data), col = "green", lwd = 2, lty = 3)
-      }
-    }
-    par(mfrow = c(1, 1))
   })
   
   # Histograms
   output$histograms <- renderPlot({
     req(rv$trajectory_stats_df)
-    
+    createHistograms(rv$trajectory_stats_df)
-    params <- c("diffusion_distance_km", "straightness", "duration_min", 
-                "mean_velocity_kmh", "fractal_dimension")
-    labels <- c("Diffusion Distance (km)", "Straightness", "Duration (min)", 
-                "Mean Velocity (km/h)", "Fractal Dimension")
-    
-    par(mfrow = c(2, 3))
-    for (i in seq_along(params)) {
-      data <- rv$trajectory_stats_df[[params[i]]][!is.na(rv$trajectory_stats_df[[params[i]]])]
-      if (length(data) >= 3) {
-        hist(data, probability = TRUE, main = paste("Histogram:", labels[i]),
-             xlab = labels[i], col = "lightgray", border = "darkgray")
-        lines(density(data), col = "red", lwd = 2)
-      }
-    }
-    par(mfrow = c(1, 1))
   })
   
   # Interpretation text
   output$interpretation_text <- renderText({
     req(rv$trajectory_stats_df)
-    
+    generateInterpretation(rv$trajectory_stats_df)
-    df <- rv$trajectory_stats_df
-    
-    text <- "========== INTERPRETATION OF TRAJECTORY PARAMETERS ==========\n\n"
-    
-    # Diffusion Distance
-    dd <- df$diffusion_distance_km[!is.na(df$diffusion_distance_km)]
-    if (length(dd) >= 2) {
-      text <- paste0(text, "1. DIFFUSION DISTANCE (Net Displacement):\n")
-      text <- paste0(text, "   - Mean: ", round(mean(dd), 2), " km\n")
-      text <- paste0(text, "   - Represents straight-line distance from origin to destination.\n")
-      text <- paste0(text, "   - Variance: ", round(var(dd), 2), " (indicates diversity in flight distances)\n\n")
-    }
-    
-    # Straightness
-    st <- df$straightness[!is.na(df$straightness)]
-    if (length(st) >= 2) {
-      text <- paste0(text, "2. STRAIGHTNESS INDEX:\n")
-      text <- paste0(text, "   - Mean: ", round(mean(st), 4), " (range 0-1, where 1 = perfectly straight)\n")
-      text <- paste0(text, "   - Values close to 1 indicate efficient, direct flight paths.\n")
-      text <- paste0(text, "   - Lower values suggest deviations due to weather, airspace, or routing.\n\n")
-    }
-    
-    # Duration
-    dur <- df$duration_min[!is.na(df$duration_min)]
-    if (length(dur) >= 2) {
-      text <- paste0(text, "3. DURATION OF TRAVEL:\n")
-      text <- paste0(text, "   - Mean: ", round(mean(dur), 2), " minutes\n")
-      text <- paste0(text, "   - Range: ", round(min(dur), 2), " - ", round(max(dur), 2), " minutes\n")
-      text <- paste0(text, "   - IQR: ", round(IQR(dur), 2), " minutes (middle 50% of flights)\n\n")
-    }
-    
-    # Velocity
-    vel <- df$mean_velocity_kmh[!is.na(df$mean_velocity_kmh)]
-    if (length(vel) >= 2) {
-      text <- paste0(text, "4. MEAN TRAVEL VELOCITY:\n")
-      text <- paste0(text, "   - Mean: ", round(mean(vel), 2), " km/h\n")
-      text <- paste0(text, "   - Typical commercial aircraft cruise: 800-900 km/h\n")
-      text <- paste0(text, "   - Lower values may include taxi, takeoff, and landing phases.\n\n")
-    }
-    
-    # Fractal Dimension
-    fd <- df$fractal_dimension[!is.na(df$fractal_dimension)]
-    if (length(fd) >= 2) {
-      text <- paste0(text, "5. FRACTAL DIMENSION:\n")
-      text <- paste0(text, "   - Mean: ", round(mean(fd), 4), "\n")
-      text <- paste0(text, "   - Value of 1.0 = perfectly straight line\n")
-      text <- paste0(text, "   - Values closer to 2.0 = more complex, space-filling paths\n")
-      text <- paste0(text, "   - Aircraft typically show low fractal dimension (efficient paths).\n\n")
-    }
-    
-    text <- paste0(text, "========== END OF ANALYSIS ==========")
-    
-    text
   })
 }
 
+# Helper function to get parameter names and labels
+getTrajectoryParams <- function() {
+  list(
+    params = c("diffusion_distance_km", "straightness", "duration_min", 
+               "mean_velocity_kmh", "fractal_dimension"),
+    labels = c("Diffusion Distance (km)", "Straightness", "Duration (min)", 
+               "Mean Velocity (km/h)", "Fractal Dimension")
+  )
+}
+
+# Calculate statistics summary table
+calculateStatsSummary <- function(trajectory_stats_df) {
+  p <- getTrajectoryParams()
+  
+  stats_list <- lapply(seq_along(p$params), function(i) {
+    x <- trajectory_stats_df[[p$params[i]]]
+    x <- x[!is.na(x)]
+    if (length(x) < 2) return(NULL)
+    
+    data.frame(
+      Parameter = p$labels[i],
+      N = length(x),
+      Mean = round(mean(x), 4),
+      Variance = round(var(x), 4),
+      Std_Dev = round(sd(x), 4),
+      Q1 = round(quantile(x, 0.25), 4),
+      Median = round(median(x), 4),
+      Q3 = round(quantile(x, 0.75), 4)
+    )
+  })
+  
+  do.call(rbind, stats_list[!sapply(stats_list, is.null)])
+}
+
+# Create boxplots for trajectory statistics
+createBoxplots <- function(trajectory_stats_df) {
+  p <- getTrajectoryParams()
+  
+  par(mfrow = c(2, 3))
+  for (i in seq_along(p$params)) {
+    data <- trajectory_stats_df[[p$params[i]]][!is.na(trajectory_stats_df[[p$params[i]]])]
+    if (length(data) >= 2) {
+      boxplot(data, main = p$labels[i], ylab = p$labels[i], col = "lightblue", border = "darkblue")
+      points(1, mean(data), pch = 18, col = "red", cex = 1.5)
+    }
+  }
+  par(mfrow = c(1, 1))
+}
+
+# Create density plots for trajectory statistics
+createDensityPlots <- function(trajectory_stats_df) {
+  p <- getTrajectoryParams()
+  
+  par(mfrow = c(2, 3))
+  for (i in seq_along(p$params)) {
+    data <- trajectory_stats_df[[p$params[i]]][!is.na(trajectory_stats_df[[p$params[i]]])]
+    if (length(data) >= 3) {
+      dens <- density(data)
+      plot(dens, main = paste("Density:", p$labels[i]), xlab = p$labels[i], col = "darkblue", lwd = 2)
+      polygon(dens, col = rgb(0, 0, 1, 0.3), border = "darkblue")
+      abline(v = mean(data), col = "red", lwd = 2, lty = 2)
+      abline(v = median(data), col = "green", lwd = 2, lty = 3)
+    }
+  }
+  par(mfrow = c(1, 1))
+}
+
+# Create histograms for trajectory statistics
+createHistograms <- function(trajectory_stats_df) {
+  p <- getTrajectoryParams()
+  
+  par(mfrow = c(2, 3))
+  for (i in seq_along(p$params)) {
+    data <- trajectory_stats_df[[p$params[i]]][!is.na(trajectory_stats_df[[p$params[i]]])]
+    if (length(data) >= 3) {
+      hist(data, probability = TRUE, main = paste("Histogram:", p$labels[i]),
+           xlab = p$labels[i], col = "lightgray", border = "darkgray")
+      lines(density(data), col = "red", lwd = 2)
+    }
+  }
+  par(mfrow = c(1, 1))
+}
+
+# Generate interpretation text for trajectory statistics
+generateInterpretation <- function(trajectory_stats_df) {
+  df <- trajectory_stats_df
+  
+  text <- "========== INTERPRETATION OF TRAJECTORY PARAMETERS ==========\n\n"
+  
+  # Diffusion Distance
+  dd <- df$diffusion_distance_km[!is.na(df$diffusion_distance_km)]
+  if (length(dd) >= 2) {
+    text <- paste0(text, "1. DIFFUSION DISTANCE (Net Displacement):\n")
+    text <- paste0(text, "   - Mean: ", round(mean(dd), 2), " km\n")
+    text <- paste0(text, "   - Represents straight-line distance from origin to destination.\n")
+    text <- paste0(text, "   - Variance: ", round(var(dd), 2), " (indicates diversity in flight distances)\n\n")
+  }
+  
+  # Straightness
+  st <- df$straightness[!is.na(df$straightness)]
+  if (length(st) >= 2) {
+    text <- paste0(text, "2. STRAIGHTNESS INDEX:\n")
+    text <- paste0(text, "   - Mean: ", round(mean(st), 4), " (range 0-1, where 1 = perfectly straight)\n")
+    text <- paste0(text, "   - Values close to 1 indicate efficient, direct flight paths.\n")
+    text <- paste0(text, "   - Lower values suggest deviations due to weather, airspace, or routing.\n\n")
+  }
+  
+  # Duration
+  dur <- df$duration_min[!is.na(df$duration_min)]
+  if (length(dur) >= 2) {
+    text <- paste0(text, "3. DURATION OF TRAVEL:\n")
+    text <- paste0(text, "   - Mean: ", round(mean(dur), 2), " minutes\n")
+    text <- paste0(text, "   - Range: ", round(min(dur), 2), " - ", round(max(dur), 2), " minutes\n")
+    text <- paste0(text, "   - IQR: ", round(IQR(dur), 2), " minutes (middle 50% of flights)\n\n")
+  }
+  
+  # Velocity
+  vel <- df$mean_velocity_kmh[!is.na(df$mean_velocity_kmh)]
+  if (length(vel) >= 2) {
+    text <- paste0(text, "4. MEAN TRAVEL VELOCITY:\n")
+    text <- paste0(text, "   - Mean: ", round(mean(vel), 2), " km/h\n")
+    text <- paste0(text, "   - Typical commercial aircraft cruise: 800-900 km/h\n")
+    text <- paste0(text, "   - Lower values may include taxi, takeoff, and landing phases.\n\n")
+  }
+  
+  # Fractal Dimension
+  fd <- df$fractal_dimension[!is.na(df$fractal_dimension)]
+  if (length(fd) >= 2) {
+    text <- paste0(text, "5. FRACTAL DIMENSION:\n")
+    text <- paste0(text, "   - Mean: ", round(mean(fd), 4), "\n")
+    text <- paste0(text, "   - Value of 1.0 = perfectly straight line\n")
+    text <- paste0(text, "   - Values closer to 2.0 = more complex, space-filling paths\n")
+    text <- paste0(text, "   - Aircraft typically show low fractal dimension (efficient paths).\n\n")
+  }
+  
+  text <- paste0(text, "========== END OF ANALYSIS ==========")
+  
+  text
+}
+
+createTrajFromRoute <- function(route_df) {
+  tryCatch({
+    lat_ref <- route_df$lat[1]
+    lon_ref <- route_df$lon[1]
+    meters_per_deg_lat <- 111320
+    meters_per_deg_lon <- 111320 * cos(lat_ref * pi / 180)
+    
+    x_meters <- (route_df$lon - lon_ref) * meters_per_deg_lon
+    y_meters <- (route_df$lat - lat_ref) * meters_per_deg_lat
+    time_seconds <- route_df$time - route_df$time[1]
+    
+    trj <- TrajFromCoords(
+      data.frame(x = x_meters, y = y_meters, time = time_seconds),
+      xCol = "x", yCol = "y", timeCol = "time"
+    )
+
+    return(trj)
+
+  }, error = function(e) {
+    status(paste("Error creating trajectory object:", e$message))
+  })
+}
+
+calculateRouteCharacteristics <- function(trj) {
+  duration <- TrajDuration(trj)
+  path_length <- TrajLength(trj)
+  diffusion_distance <- TrajDistance(trj)
+  straightness <- TrajStraightness(trj)
+  mean_velocity <- path_length / duration
+  
+  fractal_dim <- tryCatch({
+    min_step <- path_length / 100
+    max_step <- path_length / 2
+    if (min_step > 0 && max_step > min_step) {
+      step_sizes <- exp(seq(log(min_step), log(max_step), length.out = 10))
+      TrajFractalDimension(trj, stepSizes = step_sizes)
+    } else {
+      NA
+    }
+  }, error = function(e) NA)
+  
+  return (data.frame(
+        Parameter = c(
+          "Duration (s)", "Duration (min)",
+          "Path Length (km)",
+          "Duffusion Distance (m)",
+          "Diffusion Distance (km)",
+          "Straightness Index",
+          "Mean Velocity (km/h)",
+          "Fractal Dimension"
+        ),
+        Value = c(
+        duration_s = round(duration, 2),
+        duration_min = round(duration / 60, 2),
+        path_length_km = round(path_length / 1000, 2),
+        diffusion_distance_m = round(diffusion_distance, 2),
+        diffusion_distance_km = round(diffusion_distance / 1000, 2),
+        straightness_index = round(straightness, 4),
+        mean_velocity_kmh = round(mean_velocity *3.6, 2),
+        fractal_dimension = round(fractal_dim, 4)
+      )
+    )
+  )
+}
+
 # Run the application
 shinyApp(ui = ui, server = server)
 ```