diff --git a/src/main.Rmd b/src/main.Rmd
index 0a58f22..63f1a44 100644
--- a/src/main.Rmd
+++ b/src/main.Rmd
@@ -9,6 +9,11 @@ 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}
@@ -383,7 +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")
@@ -394,7 +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", 
@@ -409,7 +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"
 
@@ -436,7 +441,7 @@ if (is.null(route_df)) {
 
 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)) {
   plot(route_df$lon, route_df$lat, type = "o", pch = 20, col = "blue",
        main = paste("Flight Trajectory -", icao),
@@ -454,7 +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,
@@ -470,7 +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))
@@ -484,7 +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))
@@ -497,7 +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
 
@@ -542,7 +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)
@@ -564,7 +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")
@@ -577,7 +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 {
@@ -589,7 +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 {
@@ -601,7 +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 {
@@ -613,7 +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)