---
title: "Topic 8 - Flight Trajectory Analysis"
output:
  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)
}
```

# Abstract

This project implements an R-based application for the retrieval, processing, and statistical analysis of aircraft trajectories. Flight data is obtained from the OpenSky Network API, transformed into analyzable trajectory objects using the `trajr` package, and subsequently characterized using established movement ecology metrics. The methodology enables quantitative comparison of flight paths through parameters such as path length, straightness index, and fractal dimension.

# Introduction

## Background

The analysis of movement trajectories constitutes a fundamental aspect of spatial data science, with applications ranging from animal behavior studies to transportation network optimization. In the context of aviation, trajectory analysis provides insights into flight efficiency, airspace utilization, and routing patterns.

## Objectives

The primary objectives of this project are:

1. **Data Acquisition**: Implement robust methods for retrieving real-time flight trajectory data from the OpenSky Network
2. **Trajectory Characterization**: Apply established metrics from movement ecology to quantify flight path properties
3. **Statistical Analysis**: Perform comparative analysis across multiple flights to identify patterns and distributions

## Theoretical Framework

The `trajr` package, originally developed for animal movement analysis, provides a comprehensive toolkit for trajectory characterization. Key metrics employed in this analysis include:

- **Path Length**: Total distance traveled along the trajectory
- **Diffusion Distance**: Euclidean displacement from origin to destination
- **Straightness Index**: Ratio of diffusion distance to path length (range 0-1)
- **Fractal Dimension**: Measure of path complexity (1 = straight line, approaching 2 = space-filling curve)

# Methodology

## Data Source

Flight trajectory data is obtained from the OpenSky Network, a community-based receiver network providing open access to air traffic surveillance data. The API provides:

- Aircraft state vectors (position, velocity, heading)
- Historical flight tracks
- Airport departure and arrival information

## Data Processing Pipeline

The analysis workflow consists of the following stages:

1. **Authentication**: Establish connection to OpenSky API using credentials
2. **Data Acquisition**: Retrieve departure information for specified airport and time window
3. **Track Retrieval**: Obtain detailed waypoint data for individual flights
4. **Coordinate Transformation**: Convert geographic coordinates to metric distances
5. **Trajectory Construction**: Create `trajr` trajectory objects for analysis
6. **Statistical Computation**: Calculate trajectory metrics and aggregate statistics

## Libraries
The following libraries were included for convenience and backend handling. 
The packages more central to the task, such as `openSkies` and `trajr` are explicitly mentioned in the text.
```{r preamble, message=FALSE}
library(dplyr)
library(lubridate)
library(readr)
library(utils)
library(dotenv)
library(httr)
library(jsonlite)
library(shiny)
```

# Implementation

The following section will demonstrate the implementation of the methodology using R code snippets.

The full analysis is also available in the `shiny` web interface.

## API Authentication

Authentication with https://opensky-network.org/ was supposed to be provided by the `openSkies` R package.
The API provider however had deprecated username + password authentication for the API in 2025-03, leading us to first work with the
REST API manually.
While working on the manual API code, we however already forked the original package at [Rafael-Ayala/openSkies](https://github.com/Rafael-Ayala/openSkies)
to [eneller/openSkies](https://github.com/eneller/openSkies/) and made several
[changes](https://github.com/Rafael-Ayala/openSkies/issues/3) that were later included in a
[pull request](https://github.com/Rafael-Ayala/openSkies/pull/4).
Our contributions include refactoring and streamlining authenticated requests to use the `makeAuthenticatedRequest()` function used below
and store the token obtained by initial authentication in a `credentials` object obtained from the new `getCredentials()` function.

We further adjusted the front-facing functions to accept either _username + password_ or _client ID + secret_
where applicable using the new credentials object.

```{r auth, include=FALSE}
library(openSkies)
# Openskies API Functions 

creds <- getCredentials(
  client_id = Sys.getenv('OPENSKY_CLIENT_ID'),
  client_secret = Sys.getenv('OPENSKY_CLIENT_SECRET'))

```

## Data Acquisition
In the code below, the new `makeAuthenticatedRequests()` is also used to make requests to the `tracks/all` endpoint for which no
official function is provided by `openSkies` as that endpoint is still considered experimental at the time of writing.
We use this endpoint in `getAircraftTrack()` to obtain the track data that is used for all further trajectory calculations.

In order to request the track from the API, we first need to get a list of flights for an aircraft using 
`openSkies::getAircraftFlights()` in our convenience function `getFlights()`.

```{r get-data}
# Get flights for a specific aircraft from OpenSky API
getFlights <- function(icao, time, creds){
  flights <-getAircraftFlights(icao, startTime =  time - days(1), endTime = time, credentials = creds )
  return(flights)
}

# Get aircraft track from OpenSky API
getAircraftTrack <- function(icao, time, creds) {
  query <- list(icao24 = icao, time = as.numeric(time))
  response <- makeAuthenticatedRequest('tracks/all', query, creds)
  track_data <- fromJSON(content(response, as = "text", encoding = "UTF-8"))
  if (!is.null(track_data$path) && length(track_data$path) > 0) {
    route_df <- as.data.frame(track_data$path)
    colnames(route_df) <- c("time", "lat", "lon", "alt", "heading", "on_ground")
    return(route_df)
  }
  return(NULL)
}
```
## Parameter Calculation
We then calculate several basic parameters from the route, such as time and distance, that are then used to
construct an object in `getTrajFromRoute()` that can later be used with `trajr`.

```{r trajectory-functions}
library(trajr)

# Trajectory Conversion Functions

# Convert route to distance in meters
getRouteDistance <- function(route_df) {
  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
  return(list('x' = x_meters, 'y' = y_meters))
}

# Get time in seconds from start
getRouteTime <- function(route_df) {
  return(route_df$time - route_df$time[1])
}

# Create trajr object from route
getTrajFromRoute <- function(route_df) {
  meters <- getRouteDistance(route_df)
  time <- getRouteTime(route_df)
  trj <- TrajFromCoords(
    data.frame(x = meters$x, y = meters$y, time = time),
    xCol = "x", yCol = "y", timeCol = "time"
  )
  return(trj)
}

```

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, purl=FALSE, echo=FALSE}
time_now <- Sys.time()
departures <- getAirportDepartures(
  airport = "EDDF",
  startTime = time_now - hours(2),
  endTime = time_now - hours(1),
  credentials = creds
)
cat("Departures retrieved:", length(departures), "\n")
route_df <- NULL
icao <- "N/A"

if (length(departures) > 0) {
  for (i in seq_along(departures)) {
    icao <- departures[[i]][["ICAO24"]]
    dep_time <- departures[[i]][["departure_time"]]
    route_df <- getAircraftTrack(icao, dep_time, creds)
    if (!is.null(route_df) && nrow(route_df) >= 3) {
      cat("Aircraft ICAO24:", icao, "\n")
      cat("Track points acquired:", nrow(route_df), "\n")
      break
    }
    Sys.sleep(1)
  }
}

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,
       main = paste("Altitude Profile -", icao),
       xlab = "Elapsed Time (min)", ylab = "Barometric Altitude (m)")
  grid()
} else {
  cat("Insufficient data for altitude analysis\n")
}
```

# Results

## Trajectory Metrics
For each obtained trajectory, we first calculate the following metrics:
duration, length, diffusion distance, straightness index, mean velocity and fractal dimension.
```{r trajectory-metrics}
# 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
calculateTrajectoryStats <- function(input, icao = NULL, format = "row") {
  # Determine if input is route_df or trj
  if (inherits(input, "Trajectory")) {
    trj <- input
  } else {
    trj <- getTrajFromRoute(input)
  }
  
  # Calculate all metrics
  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 format based on use case
  if (format == "table") {
    # For single flight display (Parameter | Value)
    return(data.frame(
      Parameter = c(
        "Duration (s)", "Duration (min)",
        "Path Length (km)",
        "Diffusion Distance (m)",
        "Diffusion Distance (km)",
        "Straightness Index",
        "Mean Velocity (km/h)",
        "Fractal Dimension"
      ),
      Value = c(
        round(duration, 2),
        round(duration / 60, 2),
        round(path_length / 1000, 2),
        round(diffusion_distance, 2),
        round(diffusion_distance / 1000, 2),
        round(straightness, 4),
        round(mean_velocity * 3.6, 2),
        round(fractal_dim, 4)
      )
    ))
  } else {
    # For batch analysis (one row per flight)
    return(data.frame(
      icao24 = icao,
      diffusion_distance_km = diffusion_distance / 1000,
      path_length_km = path_length / 1000,
      straightness = straightness,
      duration_min = duration / 60,
      mean_velocity_kmh = mean_velocity * 3.6,
      fractal_dimension = fractal_dim
    ))
  }
}

# Calculate trajectory parameters for a single flight
calculate_trajectory_params <- function(icao, departure_time, creds) {
  tryCatch({
    route_df <- getAircraftTrack(icao, departure_time, creds)
    
    if (is.null(route_df) || nrow(route_df) < 3) return(NULL)
    
    return(calculateTrajectoryStats(route_df, icao = icao, format = "row"))
    
  }, error = function(e) {
    message("Error processing ", icao, ": ", e$message)
    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 demo-multiple-tracks, purl=FALSE, echo=FALSE, include=FALSE}
flight_data <- list()
successful_flights <- 0

if (length(departures) > 0) {
  max_attempts <- min(10, length(departures))
  
  for (i in seq_len(max_attempts)) {
    icao_temp <- departures[[i]][["ICAO24"]]
    dep_time_temp <- departures[[i]][["departure_time"]]
    
    route_df_temp <- getAircraftTrack(icao_temp, dep_time_temp, creds)
    
    if (!is.null(route_df_temp) && nrow(route_df_temp) >= 3) {
      stats <- calculateTrajectoryStats(route_df_temp, icao = icao_temp, format = "row")
      if (!is.null(stats)) {
        flight_data[[length(flight_data) + 1]] <- stats
        successful_flights <- successful_flights + 1
        cat("Flight", successful_flights, "| ICAO:", icao_temp, 
            "| Waypoints:", nrow(route_df_temp), "\n")
      }
    }
    
    if (successful_flights >= 5) break
  }
  
  if (length(flight_data) > 0) {
    all_flights_stats <- do.call(rbind, flight_data)
    cat("\nSample size (n):", nrow(all_flights_stats), "flights\n")
  } else {
    all_flights_stats <- NULL
    cat("No valid trajectories obtained\n")
  }
} else {
  all_flights_stats <- NULL
  cat("No departure data available\n")
}
```


The following table presents computed metrics for all successfully analyzed flights.

```{r demo-all-stats-table, purl=FALSE, echo=FALSE}
# FIXME we should display several flights from the same aircraft as stated in the requirements
# not different aircraft's flights
if (!is.null(all_flights_stats)) {
  display_stats <- all_flights_stats
  display_stats$diffusion_distance_km <- round(display_stats$diffusion_distance_km, 2)
  display_stats$path_length_km <- round(display_stats$path_length_km, 2)
  display_stats$straightness <- round(display_stats$straightness, 4)
  display_stats$duration_min <- round(display_stats$duration_min, 1)
  display_stats$mean_velocity_kmh <- round(display_stats$mean_velocity_kmh, 1)
  display_stats$fractal_dimension <- round(display_stats$fractal_dimension, 4)
  
  knitr::kable(display_stats, caption = "Computed Trajectory Metrics",
               col.names = c("ICAO24", "Displacement (km)", "Path Length (km)", 
                           "Straightness", "Duration (min)", "Velocity (km/h)", "Fractal Dim."))
} else {
  cat("No data available for tabulation\n")
}
```

## Trajectory Statistics
To enable statistical inference, trajectory data is collected for multiple flights.
We then calculate basic descriptive statistics for the metrics we obtained earlier,
such as
- mean
- median
- standard deviation
- interquartile range
```{r trajectory-summary}
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), 1),
      Variance = round(var(x), 1),
      Std_Dev = round(sd(x), 1),
      Q1 = round(quantile(x, 0.25), 1),
      Median = round(median(x), 1),
      Q3 = round(quantile(x, 0.75), 1)
    )
  })
  
  do.call(rbind, stats_list[!sapply(stats_list, is.null)])
}
```


The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter.

```{r demo-summary-stats, purl=FALSE, echo=FALSE}
# FIXME first table column broken
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")
} else {
  cat("Minimum sample size (n >= 2) not met\n")
}
```

## Visualisation
Boxplots provide a robust visualization of parameter distributions, displaying median, interquartile range, and potential outliers. The red diamond indicates the arithmetic mean.

```{r vis-boxplot}
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))
}
```

```{r demo-boxplot, fig.width=10, fig.height=8, purl=FALSE, echo=FALSE}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
  createBoxplots(all_flights_stats)
} else {
  cat("Minimum sample size (n >= 2) not met\n")
}
```


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 vis-density}
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))
}
```

```{r demo-density, fig.width=10, fig.height=8, purl=FALSE, echo=FALSE}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
  createDensityPlots(all_flights_stats)
} else {
  cat("Minimum sample size (n >= 3) not met for density estimation\n")
}
```

Histograms with overlaid density curves provide an alternative visualization of parameter distributions.

```{r vis-histogram}
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))
}
```

```{r demo-histogram, fig.width=10, fig.height=8, purl=FALSE, echo=FALSE}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 3) {
  createHistograms(all_flights_stats)
} else {
  cat("Minimum sample size (n >= 3) not met for histogram analysis\n")
}
```


The `generateInterpretation()` function provides contextual analysis of the computed trajectory metrics.

```{r vis-interpretation, include=FALSE}
generateInterpretation <- function(trajectory_stats_df) {
  df <- trajectory_stats_df
  
  text <- "========== INTERPRETATION OF TRAJECTORY PARAMETERS ==========\n\n"
  
  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")
  }
  
  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")
  }
  
  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")
  }
  
  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")
  }
  
  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
}
```

```{r demo-interpretation, purl=FALSE, echo=TRUE}
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
  interpretation <- generateInterpretation(all_flights_stats)
  cat(interpretation)
} else {
  cat("Minimum sample size (n >= 2) not met for interpretation\n")
}
```

We also include an interactive map using `leaflet` to provide an intuitive display for the route of the aircraft.
```{r vis-map}
library(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"
  )
}
```
![Departures View](../doc/web-departures.png)
![Single Flight View](../doc/web-single.png)
![Statistics View](../doc/web-stats.png)
![Interpretation View](../doc/web-interpretation.png)

# Discussion

## Key Findings

The trajectory analysis reveals several characteristics typical of commercial aviation:

1. **High Straightness Values**: Commercial flights generally exhibit straightness indices approaching 1.0, indicating efficient direct routing between waypoints.

2. **Low Fractal Dimension**: Values close to 1.0 confirm that flight paths approximate straight lines, consistent with fuel-efficient routing principles.

3. **Velocity Patterns**: Mean velocities below typical cruise speeds (800-900 km/h) reflect the inclusion of departure and arrival phases in the trajectory data.

## Limitations

- **Temporal Resolution**: Track data granularity varies based on ADS-B receiver coverage
- **Sample Size**: Statistical inference is limited by the number of available flights with complete track data

# Conclusion

This project demonstrates the successful application of movement ecology metrics to aviation trajectory analysis. The implemented R framework provides a reproducible methodology for flight path characterization and statistical comparison. The `trajr` package proves suitable for aircraft trajectory analysis, offering robust metrics originally developed for biological movement studies.

# References

1.  [The OpenSky Network. (2025). Internet archive of observed aircraft trajectories.](https://opensky-network.org/datasets/states/)
2.  [Schäfer, M, Strohmeier, M, Lenders, V, Martinovic, I, Wilhelm, M. (2014). Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research. In Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN), pages 83-94.](https://opensky-network.org/files/publications/ipsn2014.pdf)
3.  [Zheng, Y. (2015). Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology, 61(3), 1–41.](https://doi.org/10.1145/2743025)
4.  [Thulin, M. (2025). Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling. CRC Press. Boca Raton, Fl.](https://modernstatisticswithr.com/)
5.  [McLean, D J, and Skowron Volponi, M A. (2018). trajr: An R package for characterisation of animal trajectories. Ethology, 124, 440–448.](https://doi.org/10.1111/eth.12739)

