build: exclude demo chunks from purl

src/app.Rmd

@@ -39,7 +39,7 @@ ui <- fluidPage(
       hr(),
       
       h4("Batch Analysis"),
-      numericInput("batch_size", "Number of flights to analyze:", value = 10, min = 2, max = 50),
+      numericInput("batch_size", "Days of flights to analyze:", value = 5, min = 1, max = 30),
       actionButton("batch_analyze", "Run Batch Analysis", class = "btn-warning"),
       
       hr(),
@@ -60,7 +60,7 @@ ui <- fluidPage(
         
         tabPanel("Single Flight Analysis",
                  fluidRow(
-                   column(6, leafletOutput("route_plot", height = "400px")),
+                   column(6, plotOutput("route_plot", height = "400px")),
                    column(6, plotOutput("altitude_plot", height = "400px"))
                  ),
                  fluidRow(
@@ -144,6 +144,7 @@ server <- function(input, output, session) {
           data.frame(
             Index = i,
             ICAO24 = dep[["ICAO24"]] %||% NA,
+            #FIXME Callsign, Origin, Destination
             Callsign = dep[["callsign"]] %||% NA,
             Origin = dep[["estDepartureAirport"]] %||% NA,
             Destination = dep[["estArrivalAirport"]] %||% NA,
@@ -214,9 +215,11 @@ server <- function(input, output, session) {
   })
   
   # Route plot
-  output$route_plot <- renderLeaflet({
+  output$route_plot <- renderPlot({
     req(rv$current_route)
-    createInteractiveMap(rv$current_route)
+    plot(rv$current_route$lon, rv$current_route$lat, type = "o", pch = 20, col = "blue",
+         main = paste("Geographic Route of", rv$current_icao),
+         xlab = "Longitude", ylab = "Latitude")
   })
   
   # Altitude plot
@@ -246,28 +249,9 @@ server <- function(input, output, session) {
     status("Running batch analysis...")
     
     tryCatch({
-      all_trajectories <- list()
-      n_departures <- min(length(rv$departures), input$batch_size)
       
       withProgress(message = 'Analyzing flights', value = 0, {
-        for (i in 1:n_departures) {
+        all_trajectories <- getAircraftTrajectories(rv$current_icao, time = Sys.time(), creds, days = input$batch_size)
-          dep <- rv$departures[[i]]
-          icao24 <- dep[["ICAO24"]]
-          dep_time <- dep[["departure_time"]]
-          
-          incProgress(1/n_departures, detail = paste("Processing", icao24))
-          
-          if (is.null(dep_time)) next
-          
-          # Use calculate_trajectory_params from main.Rmd
-          params <- calculate_trajectory_params(icao24, dep_time, rv$creds)
-          
-          if (!is.null(params)) {
-            all_trajectories[[length(all_trajectories) + 1]] <- params
-          }
-          
-          Sys.sleep(0.3)
-        }
       })
       
       if (length(all_trajectories) > 0) {

src/main.Rmd

@@ -2,13 +2,18 @@
 title: "Topic 8 - Flight Trajectory Analysis"
 subtitle: "Erik Neller, Patrik Mišura, Lukas Adrion"
 output:
-  html_document: default
   pdf_document: default
+  html_document: default
 date: "`r Sys.Date()`"
 ---
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+# include `eval=isArtifact()` to check if pdf/html is being produced
+isArtifact <- function(){
+  isOutput <-knitr::is_html_output() || knitr::is_latex_output()
+  return(isOutput)
+}
 ```
 
 ```{r preamble, message=FALSE, include=FALSE}
@@ -23,7 +28,6 @@ library(httr)
 library(jsonlite)
 library(trajr)
 library(shiny)
-library(leaflet)
 ```
 
 ```{r opensky, include=FALSE}
@@ -52,6 +56,8 @@ getAircraftTrack <- function(icao, time, creds) {
   }
   return(NULL)
 }
+
+
 ```
 
 ```{r trajectory-functions, include=FALSE}
@@ -87,7 +93,6 @@ getTrajFromRoute <- function(route_df) {
 # Calculate trajectory characteristics
 # Input: either route_df (data.frame with lat/lon) or trj (trajr object)
 # format: "row" for batch analysis (one row per flight), "table" for single flight display
-# FIXME for batch analysis: use the same aircraft
 calculateTrajectoryStats <- function(input, icao = NULL, format = "row") {
   # Determine if input is route_df or trj
   if (inherits(input, "Trajectory")) {
@@ -166,6 +171,21 @@ calculate_trajectory_params <- function(icao, departure_time, creds) {
     return(NULL)
   })
 }
+
+getAircraftTrajectories <- function(icao, time, creds, days = 5){
+  tracks <- list()
+  for (i in 0: (days-1)) {
+    flights <- getFlights(icao,time - days(i),creds)
+    for (f in flights){
+      track <- calculate_trajectory_params(icao, f[["departure_time"]], creds)
+      if (!is.null(track)){
+        tracks[[length(tracks)+1]] <- track
+      }
+      Sys.sleep(0.5) # API courtesy
+    }
+  }
+  return(tracks)
+}
 ```
 
 ```{r stat-functions, include=FALSE}
@@ -209,27 +229,6 @@ calculateStatsSummary <- function(trajectory_stats_df) {
 ```{r viz-functions, include=FALSE}
 # Visualization Functions
 
-# Create interactive map with leaflet
-createInteractiveMap <- function(route) {
-  leaflet(route) %>%
-    addTiles() %>%
-    addPolylines(lng=~lon, lat=~lat, color="blue", weight=3, opacity=0.8) %>%
-    addCircleMarkers(
-    lng = ~lon[1],
-    lat = ~lat[1],
-    color = "green",
-    radius = 6,
-    popup = "Origin"
-  ) %>%
-  addCircleMarkers(
-    lng = ~lon[nrow(route)],
-    lat = ~lat[nrow(route)],
-    color = "red",
-    radius = 6,
-    popup = "Destination"
-  )
-}
-
 # Create boxplots for trajectory statistics
 createBoxplots <- function(trajectory_stats_df) {
   p <- getTrajectoryParams()
@@ -389,7 +388,7 @@ The full analysis is also available in the GUI-based Shiny application.
 
 The `getCredentials()` function retrieves API credentials from environment variables, ensuring secure credential management.
 
-```r
+```{r, purl=FALSE}
 creds <- getCredentials(
   client_id = Sys.getenv("OPENSKY_CLIENT_ID"),
   client_secret = Sys.getenv("OPENSKY_CLIENT_SECRET")
@@ -400,7 +399,7 @@ creds <- getCredentials(
 
 Recent departures from Frankfurt Airport (ICAO: EDDF) are queried for a two-hour time window. This airport was selected due to its high traffic volume, ensuring sufficient data availability.
 
-```{r demo-departures}
+```{r demo-departures, purl=FALSE}
 time_now <- Sys.time()
 departures <- getAirportDepartures(
   airport = "EDDF", 
@@ -415,7 +414,7 @@ cat("Departures retrieved:", length(departures), "\n")
 
 The `getAircraftTrack()` function retrieves detailed waypoint data for individual aircraft. The function iterates through available departures until valid track data is obtained.
 
-```{r demo-track}
+```{r demo-track, purl=FALSE}
 route_df <- NULL
 icao <- "N/A"
 
@@ -429,6 +428,7 @@ if (length(departures) > 0) {
       cat("Track points acquired:", nrow(route_df), "\n")
       break
     }
+    Sys.sleep(1)
   }
 }
 
@@ -439,11 +439,17 @@ if (is.null(route_df)) {
 
 ## Step 4: Spatial Visualization
 
-The geographic trajectory is visualized on an interactive map with leaflet using the `createInteractiveMap()` function. Green and red markers indicate departure and current/final position, respectively.
+The geographic trajectory is visualized in a Cartesian coordinate system. Green and red markers indicate departure and current/final position, respectively.
 
-```{r demo-route-plot, fig.width=7, fig.height=5}
+```{r demo-route-plot, fig.width=7, fig.height=5, purl=FALSE}
 if (!is.null(route_df)) {
-  createInteractiveMap(route_df)
+  plot(route_df$lon, route_df$lat, type = "o", pch = 20, col = "blue",
+       main = paste("Flight Trajectory -", icao),
+       xlab = "Longitude (°)", ylab = "Latitude (°)")
+  points(route_df$lon[1], route_df$lat[1], pch = 17, col = "green", cex = 2)
+  points(route_df$lon[nrow(route_df)], route_df$lat[nrow(route_df)], pch = 15, col = "red", cex = 2)
+  legend("topright", legend = c("Origin", "Destination", "Trajectory"), 
+         pch = c(17, 15, 20), col = c("green", "red", "blue"))
 } else {
   cat("Insufficient data for visualization\n")
 }
@@ -453,7 +459,7 @@ if (!is.null(route_df)) {
 
 The altitude profile reveals distinct flight phases: climb, cruise, and descent. This temporal representation provides insight into vertical movement patterns.
 
-```{r demo-altitude-plot, fig.width=7, fig.height=4}
+```{r demo-altitude-plot, fig.width=7, fig.height=4, purl=FALSE}
 if (!is.null(route_df)) {
   time_minutes <- (route_df$time - route_df$time[1]) / 60
   plot(time_minutes, route_df$alt, type = "l", col = "red", lwd = 2,
@@ -469,7 +475,7 @@ if (!is.null(route_df)) {
 
 The `getTrajFromRoute()` function transforms geographic coordinates into a metric coordinate system and constructs a `trajr` trajectory object. This transformation is necessary for accurate distance calculations.
 
-```{r demo-trajectory-plot, fig.width=7, fig.height=5}
+```{r demo-trajectory-plot, fig.width=7, fig.height=5, purl=FALSE}
 if (!is.null(route_df)) {
   trj <- getTrajFromRoute(route_df)
   plot(trj, main = paste("Metric Trajectory -", icao))
@@ -483,7 +489,7 @@ if (!is.null(route_df)) {
 
 The `calculateTrajectoryStats()` function computes comprehensive trajectory metrics. The table format provides a clear overview of individual flight characteristics.
 
-```{r demo-stats-table}
+```{r demo-stats-table, purl=FALSE}
 if (!is.null(route_df)) {
   stats_table <- calculateTrajectoryStats(route_df, icao = icao, format = "table")
   knitr::kable(stats_table, caption = paste("Trajectory Metrics for Aircraft", icao))
@@ -496,7 +502,7 @@ if (!is.null(route_df)) {
 
 To enable statistical inference, trajectory data is collected for multiple flights. The algorithm attempts to retrieve valid track data for up to five departures in this example.
 
-```{r demo-multiple-tracks}
+```{r demo-multiple-tracks, purl=FALSE}
 flight_data <- list()
 successful_flights <- 0
 
@@ -541,7 +547,7 @@ if (length(departures) > 0) {
 
 The following table presents computed metrics for all successfully analyzed flights.
 
-```{r demo-all-stats-table}
+```{r demo-all-stats-table, purl=FALSE}
 if (!is.null(all_flights_stats)) {
   display_stats <- all_flights_stats
   display_stats$diffusion_distance_km <- round(display_stats$diffusion_distance_km, 2)
@@ -563,7 +569,7 @@ if (!is.null(all_flights_stats)) {
 
 The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter.
 
-```{r demo-summary-stats}
+```{r demo-summary-stats, purl=FALSE}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
   summary_stats <- calculateStatsSummary(all_flights_stats)
   knitr::kable(summary_stats, caption = "Descriptive Statistics Summary")
@@ -576,7 +582,7 @@ if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
 
 Boxplots provide a robust visualization of parameter distributions, displaying median, interquartile range, and potential outliers. The red diamond indicates the arithmetic mean.
 
-```{r demo-boxplots, fig.width=10, fig.height=8}
+```{r demo-boxplots, fig.width=10, fig.height=8, purl=FALSE}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
   createBoxplots(all_flights_stats)
 } else {
@@ -588,7 +594,7 @@ if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
 
 Density plots employ kernel density estimation to approximate the probability distribution of each parameter. Vertical lines indicate mean (red, dashed) and median (green, dotted).
 
-```{r demo-density, fig.width=10, fig.height=8}
+```{r demo-density, fig.width=10, fig.height=8, purl=FALSE}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
   createDensityPlots(all_flights_stats)
 } else {
@@ -600,7 +606,7 @@ if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
 
 Histograms with overlaid density curves provide an alternative visualization of parameter distributions.
 
-```{r demo-histograms, fig.width=10, fig.height=8}
+```{r demo-histograms, fig.width=10, fig.height=8, purl=FALSE}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
   createHistograms(all_flights_stats)
 } else {
@@ -612,7 +618,7 @@ if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
 
 The `generateInterpretation()` function provides contextual analysis of the computed trajectory metrics.
 
-```{r demo-interpretation}
+```{r demo-interpretation, purl=FALSE}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
   interpretation <- generateInterpretation(all_flights_stats)
   cat(interpretation)