doc: show screenshots in readme

doc: minor tweaks
doc: include screenshots
2026-01-22 00:37:47 +01:00 · 2026-01-21 22:42:00 +01:00 · 2026-01-21 22:31:12 +01:00 · 2026-01-21 22:21:24 +01:00 · 2026-01-21 16:02:00 +01:00 · 2026-01-21 15:57:57 +01:00
10 changed files with 1027 additions and 353 deletions
--- a/README.md
+++ b/README.md
@@ -14,6 +14,12 @@ Develop an R-based software, which will perform the following tasks:
 6.  In the final project report, describe the problem, describe the method and the developed software support in the R environment, present and interpret the results, and form a conclusion.
 ## Demo
 ![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)
 ## Resources
 1.  [The OpenSky Network. (2025). Internet archive of observed aircraft trajectories.](https://opensky-network.org/datasets/states/)
--- a/doc/slides.Rmd
+++ b/doc/slides.Rmd
@@ -23,11 +23,22 @@ Develop an R-based software, which will perform the following tasks:
 ## Methodology
-1. acquire data using the OpenSky API
+1. acquire data using the OpenSky API bindings in the `openSkies` R package
-2. use 
+2. use `tcltk` and `shiny` for GUI and Web Interface
 3. show an interactive map using leaflet
 4. calculate required parameters using `trajr`
 5. calculate descriptive statistics
 ## Contribution
- extended functionality of the `openSkies` R package and created a merge request
+- extended functionality of the `openSkies` R package and created a merge request in the original repository
 - created a web app to display stats, density functions and boxplots
 ---
 ![web interface](web-single.png)
--- a/doc/web-departures.png
+++ b/doc/web-departures.png
--- a/doc/web-interpretation.png
+++ b/doc/web-interpretation.png
--- a/doc/web-single.png
+++ b/doc/web-single.png
--- a/doc/web-stats.png
+++ b/doc/web-stats.png
--- a/doc/web-stats_full.png
+++ b/doc/web-stats_full.png
--- a/renv.lock
+++ b/renv.lock
@@ -58,6 +58,31 @@
      "Maintainer": "Kirill Müller <kirill@cynkra.com>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "KernSmooth": {
      "Package": "KernSmooth",
      "Version": "2.23-26",
      "Source": "Repository",
      "Priority": "recommended",
      "Date": "2024-12-10",
      "Title": "Functions for Kernel Smoothing Supporting Wand & Jones (1995)",
      "Authors@R": "c(person(\"Matt\", \"Wand\", role = \"aut\", email = \"Matt.Wand@uts.edu.au\"), person(\"Cleve\", \"Moler\", role = \"ctb\", comment = \"LINPACK routines in src/d*\"), person(\"Brian\", \"Ripley\", role = c(\"trl\", \"cre\", \"ctb\"), email = \"Brian.Ripley@R-project.org\", comment = \"R port and updates\"))",
      "Note": "Maintainers are not available to give advice on using a package they did not author.",
      "Depends": [
        "R (>= 2.5.0)",
        "stats"
      ],
      "Suggests": [
        "MASS",
        "carData"
      ],
      "Description": "Functions for kernel smoothing (and density estimation) corresponding to the book:  Wand, M.P. and Jones, M.C. (1995) \"Kernel Smoothing\".",
      "License": "Unlimited",
      "ByteCompile": "yes",
      "NeedsCompilation": "yes",
      "Author": "Matt Wand [aut], Cleve Moler [ctb] (LINPACK routines in src/d*), Brian Ripley [trl, cre, ctb] (R port and updates)",
      "Maintainer": "Brian Ripley <Brian.Ripley@R-project.org>",
      "Repository": "CRAN"
    },
    "MASS": {
      "Package": "MASS",
      "Version": "7.3-65",
@@ -503,6 +528,68 @@
      "Maintainer": "Winston Chang <winston@posit.co>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "class": {
      "Package": "class",
      "Version": "7.3-23",
      "Source": "Repository",
      "Priority": "recommended",
      "Date": "2025-01-01",
      "Depends": [
        "R (>= 3.0.0)",
        "stats",
        "utils"
      ],
      "Imports": [
        "MASS"
      ],
      "Authors@R": "c(person(\"Brian\", \"Ripley\", role = c(\"aut\", \"cre\", \"cph\"), email = \"Brian.Ripley@R-project.org\"), person(\"William\", \"Venables\", role = \"cph\"))",
      "Description": "Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.",
      "Title": "Functions for Classification",
      "ByteCompile": "yes",
      "License": "GPL-2 | GPL-3",
      "URL": "http://www.stats.ox.ac.uk/pub/MASS4/",
      "NeedsCompilation": "yes",
      "Author": "Brian Ripley [aut, cre, cph], William Venables [cph]",
      "Maintainer": "Brian Ripley <Brian.Ripley@R-project.org>",
      "Repository": "CRAN"
    },
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      "Version": "0.4-11",
      "Source": "Repository",
      "Date": "2025-01-06",
      "Title": "Choose Univariate Class Intervals",
      "Authors@R": "c( person(\"Roger\", \"Bivand\", role=c(\"aut\", \"cre\"), email=\"Roger.Bivand@nhh.no\", comment=c(ORCID=\"0000-0003-2392-6140\")), person(\"Bill\", \"Denney\", role=\"ctb\", comment=c(ORCID=\"0000-0002-5759-428X\")), person(\"Richard\", \"Dunlap\", role=\"ctb\"), person(\"Diego\", \"Hernangómez\", role=\"ctb\", comment=c(ORCID=\"0000-0001-8457-4658\")), person(\"Hisaji\", \"Ono\", role=\"ctb\"), person(\"Josiah\", \"Parry\", role = \"ctb\", comment = c(ORCID = \"0000-0001-9910-865X\")), person(\"Matthieu\", \"Stigler\", role=\"ctb\", comment =c(ORCID=\"0000-0002-6802-4290\")))",
      "Depends": [
        "R (>= 2.2)"
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        "e1071",
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        "KernSmooth"
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      "Suggests": [
        "spData (>= 0.2.6.2)",
        "units",
        "knitr",
        "rmarkdown",
        "tinytest"
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      "NeedsCompilation": "yes",
      "Description": "Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.",
      "License": "GPL (>= 2)",
      "URL": "https://r-spatial.github.io/classInt/, https://github.com/r-spatial/classInt/",
      "BugReports": "https://github.com/r-spatial/classInt/issues/",
      "RoxygenNote": "6.1.1",
      "Encoding": "UTF-8",
      "VignetteBuilder": "knitr",
      "Author": "Roger Bivand [aut, cre] (<https://orcid.org/0000-0003-2392-6140>), Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>), Richard Dunlap [ctb], Diego Hernangómez [ctb] (<https://orcid.org/0000-0001-8457-4658>), Hisaji Ono [ctb], Josiah Parry [ctb] (<https://orcid.org/0000-0001-9910-865X>), Matthieu Stigler [ctb] (<https://orcid.org/0000-0002-6802-4290>)",
      "Maintainer": "Roger Bivand <Roger.Bivand@nhh.no>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
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    "cli": {
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      "Version": "3.6.5",
@@ -757,6 +844,38 @@
      "Maintainer": "Jeroen Ooms <jeroenooms@gmail.com>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
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      "Description": "Provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e. static .html files). Currently supports linked brushing and filtering.",
      "License": "MIT + file LICENSE",
      "URL": "https://rstudio.github.io/crosstalk/, https://github.com/rstudio/crosstalk",
      "BugReports": "https://github.com/rstudio/crosstalk/issues",
      "Imports": [
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      "Config/Needs/website": "jcheng5/d3scatter, DT, leaflet, rmarkdown",
      "Encoding": "UTF-8",
      "RoxygenNote": "7.3.2",
      "NeedsCompilation": "no",
      "Author": "Joe Cheng [aut], Carson Sievert [aut, cre] (ORCID: <https://orcid.org/0000-0002-4958-2844>), Posit Software, PBC [cph, fnd], jQuery Foundation [cph] (jQuery library and jQuery UI library), jQuery contributors [ctb, cph] (jQuery library; authors listed in inst/www/shared/jquery-AUTHORS.txt), Mark Otto [ctb] (Bootstrap library), Jacob Thornton [ctb] (Bootstrap library), Bootstrap contributors [ctb] (Bootstrap library), Twitter, Inc [cph] (Bootstrap library), Brian Reavis [ctb, cph] (selectize.js library), Kristopher Michael Kowal [ctb, cph] (es5-shim library), es5-shim contributors [ctb, cph] (es5-shim library), Denis Ineshin [ctb, cph] (ion.rangeSlider library), Sami Samhuri [ctb, cph] (Javascript strftime library)",
      "Maintainer": "Carson Sievert <carson@posit.co>",
      "Repository": "RSPM"
    },
    "curl": {
      "Package": "curl",
      "Version": "7.0.0",
@@ -1004,6 +1123,42 @@
      "Maintainer": "Hadley Wickham <hadley@posit.co>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "e1071": {
      "Package": "e1071",
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      "Source": "Repository",
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      "Imports": [
        "graphics",
        "grDevices",
        "class",
        "stats",
        "methods",
        "utils",
        "proxy"
      ],
      "Suggests": [
        "cluster",
        "mlbench",
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        "randomForest",
        "rpart",
        "SparseM",
        "xtable",
        "Matrix",
        "MASS",
        "slam"
      ],
      "Authors@R": "c(person(given = \"David\", family = \"Meyer\", role = c(\"aut\", \"cre\"), email = \"David.Meyer@R-project.org\", comment = c(ORCID = \"0000-0002-5196-3048\")),     person(given = \"Evgenia\", family = \"Dimitriadou\", role = c(\"aut\",\"cph\")), person(given = \"Kurt\", family = \"Hornik\", role = \"aut\", email = \"Kurt.Hornik@R-project.org\", comment = c(ORCID = \"0000-0003-4198-9911\")), person(given = \"Andreas\", family = \"Weingessel\", role = \"aut\"), person(given = \"Friedrich\", family = \"Leisch\", role = \"aut\"), person(given = \"Chih-Chung\", family = \"Chang\", role = c(\"ctb\",\"cph\"), comment = \"libsvm C++-code\"), person(given = \"Chih-Chen\", family = \"Lin\", role = c(\"ctb\",\"cph\"), comment = \"libsvm C++-code\"))",
      "Description": "Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour ...",
      "License": "GPL-2 | GPL-3",
      "LazyLoad": "yes",
      "NeedsCompilation": "yes",
      "Author": "David Meyer [aut, cre] (ORCID: <https://orcid.org/0000-0002-5196-3048>), Evgenia Dimitriadou [aut, cph], Kurt Hornik [aut] (ORCID: <https://orcid.org/0000-0003-4198-9911>), Andreas Weingessel [aut], Friedrich Leisch [aut], Chih-Chung Chang [ctb, cph] (libsvm C++-code), Chih-Chen Lin [ctb, cph] (libsvm C++-code)",
      "Maintainer": "David Meyer <David.Meyer@R-project.org>",
      "Repository": "https://packagemanager.posit.co/cran/latest",
      "Encoding": "UTF-8"
    },
    "evaluate": {
      "Package": "evaluate",
      "Version": "1.0.5",
@@ -1499,6 +1654,39 @@
      "Maintainer": "Carson Sievert <carson@posit.co>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "htmlwidgets": {
      "Package": "htmlwidgets",
      "Version": "1.6.4",
      "Source": "Repository",
      "Type": "Package",
      "Title": "HTML Widgets for R",
      "Authors@R": "c( person(\"Ramnath\", \"Vaidyanathan\", role = c(\"aut\", \"cph\")), person(\"Yihui\", \"Xie\", role = \"aut\"), person(\"JJ\", \"Allaire\", role = \"aut\"), person(\"Joe\", \"Cheng\", , \"joe@posit.co\", role = \"aut\"), person(\"Carson\", \"Sievert\", , \"carson@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4958-2844\")), person(\"Kenton\", \"Russell\", role = c(\"aut\", \"cph\")), person(\"Ellis\", \"Hughes\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
      "Description": "A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications.",
      "License": "MIT + file LICENSE",
      "URL": "https://github.com/ramnathv/htmlwidgets",
      "BugReports": "https://github.com/ramnathv/htmlwidgets/issues",
      "Imports": [
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      "Suggests": [
        "testthat"
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      "Enhances": [
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      "Author": "Ramnath Vaidyanathan [aut, cph], Yihui Xie [aut], JJ Allaire [aut], Joe Cheng [aut], Carson Sievert [aut, cre] (<https://orcid.org/0000-0002-4958-2844>), Kenton Russell [aut, cph], Ellis Hughes [ctb], Posit Software, PBC [cph, fnd]",
      "Maintainer": "Carson Sievert <carson@posit.co>",
      "Repository": "RSPM"
    },
    "httpuv": {
      "Package": "httpuv",
      "Version": "1.6.16",
@@ -1819,6 +2007,159 @@
      "Maintainer": "Charlie Gao <charlie.gao@posit.co>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
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      "Source": "Repository",
      "Date": "2025-03-31",
      "Priority": "recommended",
      "Title": "Trellis Graphics for R",
      "Authors@R": "c(person(\"Deepayan\", \"Sarkar\", role = c(\"aut\", \"cre\"), email = \"deepayan.sarkar@r-project.org\", comment = c(ORCID = \"0000-0003-4107-1553\")), person(\"Felix\", \"Andrews\", role = \"ctb\"), person(\"Kevin\", \"Wright\", role = \"ctb\", comment = \"documentation\"), person(\"Neil\", \"Klepeis\", role = \"ctb\"), person(\"Johan\", \"Larsson\", role = \"ctb\", comment = \"miscellaneous improvements\"), person(\"Zhijian (Jason)\", \"Wen\", role = \"cph\", comment = \"filled contour code\"), person(\"Paul\", \"Murrell\", role = \"ctb\", email = \"paul@stat.auckland.ac.nz\"), person(\"Stefan\", \"Eng\", role = \"ctb\", comment = \"violin plot improvements\"), person(\"Achim\", \"Zeileis\", role = \"ctb\", comment = \"modern colors\"), person(\"Alexandre\", \"Courtiol\", role = \"ctb\", comment = \"generics for larrows, lpolygon, lrect and lsegments\") )",
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      "Title": "Create Interactive Web Maps with the JavaScript 'Leaflet' Library",
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      "Description": "Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.",
      "License": "MIT + file LICENSE",
      "URL": "https://rstudio.github.io/leaflet/, https://github.com/rstudio/leaflet",
      "BugReports": "https://github.com/rstudio/leaflet/issues",
      "Depends": [
        "R (>= 3.5)"
      ],
      "Imports": [
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        "RColorBrewer",
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      "Suggests": [
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        "sp",
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        "testthat (>= 3.0.0)"
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      "Config/Needs/website": "dplyr, ncdf4, rnaturalearth, tidyverse/tidytemplate",
      "Config/testthat/edition": "3",
      "Encoding": "UTF-8",
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      "URL": "https://rstudio.github.io/leaflet.providers/, https://github.com/rstudio/leaflet.providers",
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      "Author": "Leslie Huang [aut], Barret Schloerke [ctb, cre] (<https://orcid.org/0000-0001-9986-114X>), Leaflet Providers contributors [ctb, cph] (Leaflet Providers plugin), Posit Software, PBC [cph, fnd]",
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@@ -2420,6 +2761,32 @@
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@@ -2498,6 +2865,49 @@
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      "License": "GPL (>= 3)",
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@@ -2715,6 +3125,42 @@
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      "Description": "Provides R bindings for Google's s2 library for geometric calculations on the sphere. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries.",
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      "Encoding": "UTF-8",
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      "URL": "https://r-spatial.github.io/s2/, https://github.com/r-spatial/s2, http://s2geometry.io/",
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      "Config/testthat/edition": "3",
      "NeedsCompilation": "yes",
      "Author": "Dewey Dunnington [aut] (ORCID: <https://orcid.org/0000-0002-9415-4582>), Edzer Pebesma [aut, cre] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Ege Rubak [aut], Jeroen Ooms [ctb] (configure script), Google, Inc. [cph] (Original s2geometry.io source code)",
      "Maintainer": "Edzer Pebesma <edzer.pebesma@uni-muenster.de>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
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@@ -2795,6 +3241,86 @@
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      "Repository": "https://packagemanager.posit.co/cran/latest"
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      "Description": "Support for simple feature access, a standardized way to encode and analyze spatial vector data. Binds to 'GDAL'  <doi:10.5281/zenodo.5884351> for reading and writing data, to 'GEOS' <doi:10.5281/zenodo.11396894> for geometrical operations, and to 'PROJ' <doi:10.5281/zenodo.5884394> for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates.",
      "License": "GPL-2 | MIT + file LICENSE",
      "URL": "https://r-spatial.github.io/sf/, https://github.com/r-spatial/sf",
      "BugReports": "https://github.com/r-spatial/sf/issues",
      "Depends": [
        "methods",
        "R (>= 3.3.0)"
      ],
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      "NeedsCompilation": "yes",
      "Author": "Edzer Pebesma [aut, cre] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Roger Bivand [ctb] (ORCID: <https://orcid.org/0000-0003-2392-6140>), Etienne Racine [ctb], Michael Sumner [ctb], Ian Cook [ctb], Tim Keitt [ctb], Robin Lovelace [ctb], Hadley Wickham [ctb], Jeroen Ooms [ctb] (ORCID: <https://orcid.org/0000-0002-4035-0289>), Kirill Müller [ctb], Thomas Lin Pedersen [ctb], Dan Baston [ctb], Dewey Dunnington [ctb] (ORCID: <https://orcid.org/0000-0002-9415-4582>)",
      "Maintainer": "Edzer Pebesma <edzer.pebesma@uni-muenster.de>",
      "Repository": "CRAN"
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    "shiny": {
      "Package": "shiny",
      "Version": "1.12.1",
@@ -2920,6 +3446,48 @@
      "NeedsCompilation": "yes",
      "Repository": "https://packagemanager.posit.co/cran/latest"
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      "Title": "Classes and Methods for Spatial Data",
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        "raster"
      ],
      "Description": "Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details.",
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      "Collate": "bpy.colors.R AAA.R Class-CRS.R CRS-methods.R Class-Spatial.R Spatial-methods.R projected.R Class-SpatialPoints.R SpatialPoints-methods.R Class-SpatialPointsDataFrame.R SpatialPointsDataFrame-methods.R Class-SpatialMultiPoints.R SpatialMultiPoints-methods.R Class-SpatialMultiPointsDataFrame.R SpatialMultiPointsDataFrame-methods.R Class-GridTopology.R Class-SpatialGrid.R Class-SpatialGridDataFrame.R Class-SpatialLines.R SpatialLines-methods.R Class-SpatialLinesDataFrame.R SpatialLinesDataFrame-methods.R Class-SpatialPolygons.R Class-SpatialPolygonsDataFrame.R SpatialPolygons-methods.R SpatialPolygonsDataFrame-methods.R GridTopology-methods.R SpatialGrid-methods.R SpatialGridDataFrame-methods.R SpatialPolygons-internals.R point.in.polygon.R SpatialPolygons-displayMethods.R zerodist.R image.R stack.R bubble.R mapasp.R select.spatial.R gridded.R asciigrid.R spplot.R over.R spsample.R recenter.R dms.R gridlines.R spdists.R rbind.R flipSGDF.R chfids.R loadmeuse.R compassRose.R surfaceArea.R spOptions.R subset.R disaggregate.R sp_spat1.R merge.R aggregate.R elide.R sp2Mondrian.R",
      "VignetteBuilder": "knitr",
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@@ -3080,6 +3648,47 @@
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      "Package": "terra",
      "Version": "1.8-93",
      "Source": "Repository",
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      "Title": "Spatial Data Analysis",
      "Date": "2026-01-11",
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      "Encoding": "UTF-8",
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      "Maintainer": "Robert J. Hijmans <r.hijmans@gmail.com>",
      "Description": "Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started.",
      "License": "GPL (>= 3)",
      "URL": "https://rspatial.org/, https://rspatial.github.io/terra/",
      "BugReports": "https://github.com/rspatial/terra/issues",
      "LazyLoad": "yes",
      "Authors@R": "c( person(\"Robert J.\", \"Hijmans\", role=c(\"cre\", \"aut\"),   email=\"r.hijmans@gmail.com\", comment=c(ORCID=\"0000-0001-5872-2872\")),\t\t\t person(\"Márcia\", \"Barbosa\", role=\"ctb\", comment=c(ORCID=\"0000-0001-8972-7713\")), person(\"Roger\", \"Bivand\", role=\"ctb\", comment=c(ORCID=\"0000-0003-2392-6140\")), person(\"Andrew\", \"Brown\", role=\"ctb\", comment=c(ORCID=\"0000-0002-4565-533X\")), person(\"Michael\", \"Chirico\", role=\"ctb\", comment=c(ORCID=\"0000-0003-0787-087X\")), person(\"Emanuele\", \"Cordano\", role=\"ctb\",comment=c(ORCID=\"0000-0002-3508-5898\")), person(\"Krzysztof\", \"Dyba\", role=\"ctb\", comment=c(ORCID=\"0000-0002-8614-3816\")), person(\"Edzer\", \"Pebesma\", role=\"ctb\", comment=c(ORCID=\"0000-0001-8049-7069\")), person(\"Barry\", \"Rowlingson\", role=\"ctb\", comment=c(ORCID=\"0000-0002-8586-6625\")), person(\"Michael D.\", \"Sumner\", role=\"ctb\", comment=c(ORCID=\"0000-0002-2471-7511\")))",
      "NeedsCompilation": "yes",
      "Author": "Robert J. Hijmans [cre, aut] (ORCID: <https://orcid.org/0000-0001-5872-2872>), Márcia Barbosa [ctb] (ORCID: <https://orcid.org/0000-0001-8972-7713>), Roger Bivand [ctb] (ORCID: <https://orcid.org/0000-0003-2392-6140>), Andrew Brown [ctb] (ORCID: <https://orcid.org/0000-0002-4565-533X>), Michael Chirico [ctb] (ORCID: <https://orcid.org/0000-0003-0787-087X>), Emanuele Cordano [ctb] (ORCID: <https://orcid.org/0000-0002-3508-5898>), Krzysztof Dyba [ctb] (ORCID: <https://orcid.org/0000-0002-8614-3816>), Edzer Pebesma [ctb] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Barry Rowlingson [ctb] (ORCID: <https://orcid.org/0000-0002-8586-6625>), Michael D. Sumner [ctb] (ORCID: <https://orcid.org/0000-0002-2471-7511>)",
      "Repository": "CRAN"
    },
    "tibble": {
      "Package": "tibble",
      "Version": "3.3.0",
@@ -3363,6 +3972,50 @@
      "Maintainer": "Davis Vaughan <davis@posit.co>",
      "Repository": "RSPM"
    },
    "units": {
      "Package": "units",
      "Version": "1.0-0",
      "Source": "Repository",
      "Title": "Measurement Units for R Vectors",
      "Authors@R": "c(person(\"Edzer\", \"Pebesma\", role = c(\"aut\", \"cre\"), email = \"edzer.pebesma@uni-muenster.de\", comment = c(ORCID = \"0000-0001-8049-7069\")), person(\"Thomas\", \"Mailund\", role = \"aut\", email = \"mailund@birc.au.dk\"), person(\"Tomasz\", \"Kalinowski\", role = \"aut\"), person(\"James\", \"Hiebert\", role = \"ctb\"), person(\"Iñaki\", \"Ucar\", role = \"aut\", email = \"iucar@fedoraproject.org\", comment = c(ORCID = \"0000-0001-6403-5550\")), person(\"Thomas Lin\", \"Pedersen\", role = \"ctb\") )",
      "Depends": [
        "R (>= 3.5.0)"
      ],
      "Imports": [
        "Rcpp"
      ],
      "LinkingTo": [
        "Rcpp (>= 0.12.10)"
      ],
      "Suggests": [
        "NISTunits",
        "measurements",
        "xml2",
        "magrittr",
        "pillar (>= 1.3.0)",
        "dplyr (>= 1.0.0)",
        "vctrs (>= 0.3.1)",
        "ggplot2 (> 3.2.1)",
        "testthat (>= 3.0.0)",
        "vdiffr",
        "knitr",
        "rvest",
        "rmarkdown"
      ],
      "VignetteBuilder": "knitr",
      "Description": "Support for measurement units in R vectors, matrices and arrays: automatic propagation, conversion, derivation and simplification of units; raising errors in case of unit incompatibility. Compatible with the POSIXct, Date and difftime  classes. Uses the UNIDATA udunits library and unit database for  unit compatibility checking and conversion. Documentation about 'units' is provided in the paper by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a vignette; see 'citation(\"units\")' for details.",
      "SystemRequirements": "udunits-2",
      "License": "GPL-2",
      "URL": "https://r-quantities.github.io/units/, https://github.com/r-quantities/units",
      "BugReports": "https://github.com/r-quantities/units/issues",
      "RoxygenNote": "7.3.3",
      "Encoding": "UTF-8",
      "Config/testthat/edition": "3",
      "NeedsCompilation": "yes",
      "Author": "Edzer Pebesma [aut, cre] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Thomas Mailund [aut], Tomasz Kalinowski [aut], James Hiebert [ctb], Iñaki Ucar [aut] (ORCID: <https://orcid.org/0000-0001-6403-5550>), Thomas Lin Pedersen [ctb]",
      "Maintainer": "Edzer Pebesma <edzer.pebesma@uni-muenster.de>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "utf8": {
      "Package": "utf8",
      "Version": "1.2.6",
@@ -3578,6 +4231,35 @@
      "Maintainer": "Lionel Henry <lionel@posit.co>",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "wk": {
      "Package": "wk",
      "Version": "0.9.5",
      "Source": "Repository",
      "Title": "Lightweight Well-Known Geometry Parsing",
      "Authors@R": "c( person(given = \"Dewey\", family = \"Dunnington\", role = c(\"aut\", \"cre\"), email = \"dewey@fishandwhistle.net\", comment = c(ORCID = \"0000-0002-9415-4582\")), person(given = \"Edzer\", family = \"Pebesma\", role = c(\"aut\"), email = \"edzer.pebesma@uni-muenster.de\", comment = c(ORCID = \"0000-0001-8049-7069\")), person(given = \"Anthony\", family = \"North\", email = \"anthony.jl.north@gmail.com\", role = c(\"ctb\")) )",
      "Maintainer": "Dewey Dunnington <dewey@fishandwhistle.net>",
      "Description": "Provides a minimal R and C++ API for parsing well-known binary and well-known text representation of geometries to and from R-native formats. Well-known binary is compact and fast to parse; well-known text is human-readable and is useful for writing tests. These formats are useful in R only if the information they contain can be accessed in R, for which high-performance functions are provided here.",
      "License": "MIT + file LICENSE",
      "Encoding": "UTF-8",
      "RoxygenNote": "7.3.2",
      "Suggests": [
        "testthat (>= 3.0.0)",
        "vctrs (>= 0.3.0)",
        "sf",
        "tibble",
        "readr"
      ],
      "URL": "https://paleolimbot.github.io/wk/, https://github.com/paleolimbot/wk",
      "BugReports": "https://github.com/paleolimbot/wk/issues",
      "Config/testthat/edition": "3",
      "Depends": [
        "R (>= 2.10)"
      ],
      "LazyData": "true",
      "NeedsCompilation": "yes",
      "Author": "Dewey Dunnington [aut, cre] (ORCID: <https://orcid.org/0000-0002-9415-4582>), Edzer Pebesma [aut] (ORCID: <https://orcid.org/0000-0001-8049-7069>), Anthony North [ctb]",
      "Repository": "https://packagemanager.posit.co/cran/latest"
    },
    "xfun": {
      "Package": "xfun",
      "Version": "0.55",
--- a/src/app.Rmd
+++ b/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(),
@@ -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,
@@ -246,28 +247,9 @@ 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) {
--- a/src/main.Rmd
+++ b/src/main.Rmd
@@ -1,39 +1,122 @@
 ---
 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}
+# Abstract
-# Load Libraries
+
 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(openSkies)
 library(dotenv)
 library(httr)
 library(jsonlite)
 library(trajr)
 library(shiny)
 library(leaflet)
 ```
-```{r opensky, include=FALSE}
+# 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 
 time_now <- Sys.time()
 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 )
@@ -53,8 +136,13 @@ getAircraftTrack <- function(icao, time, creds) {
  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)
 ```{r trajectory-functions, include=FALSE}
 # Trajectory Conversion Functions
 # Convert route to distance in meters
@@ -84,10 +172,56 @@ getTrajFromRoute <- function(route_df) {
  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
 # 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")) {
@@ -166,12 +300,94 @@ 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}
+```{r demo-multiple-tracks, purl=FALSE, echo=FALSE, include=FALSE}
-# Statistical Helper Functions
+flight_data <- list()
 successful_flights <- 0
-# Get parameter names and labels for trajectory statistics
+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", 
@@ -193,12 +409,12 @@ calculateStatsSummary <- function(trajectory_stats_df) {
    data.frame(
      Parameter = p$labels[i],
      N = length(x),
-      Mean = round(mean(x), 4),
+      Mean = round(mean(x), 1),
-      Variance = round(var(x), 4),
+      Variance = round(var(x), 1),
-      Std_Dev = round(sd(x), 4),
+      Std_Dev = round(sd(x), 1),
-      Q1 = round(quantile(x, 0.25), 4),
+      Q1 = round(quantile(x, 0.25), 1),
-      Median = round(median(x), 4),
+      Median = round(median(x), 1),
-      Q3 = round(quantile(x, 0.75), 4)
+      Q3 = round(quantile(x, 0.75), 1)
    )
  })
@@ -206,31 +422,23 @@ calculateStatsSummary <- function(trajectory_stats_df) {
 }
 ```
 ```{r viz-functions, include=FALSE}
 # Visualization Functions
-# Create interactive map with leaflet
+The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter.
-createInteractiveMap <- function(route) {
+
-  leaflet(route) %>%
+```{r demo-summary-stats, purl=FALSE, echo=FALSE}
-    addTiles() %>%
+# FIXME first table column broken
-    addPolylines(lng=~lon, lat=~lat, color="blue", weight=3, opacity=0.8) %>%
+if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
-    addCircleMarkers(
+  summary_stats <- calculateStatsSummary(all_flights_stats)
-    lng = ~lon[1],
+  knitr::kable(summary_stats, caption = "Descriptive Statistics Summary")
-    lat = ~lat[1],
+} else {
-    color = "green",
+  cat("Minimum sample size (n >= 2) not met\n")
    radius = 6,
    popup = "Origin"
  ) %>%
  addCircleMarkers(
    lng = ~lon[nrow(route)],
    lat = ~lat[nrow(route)],
    color = "red",
    radius = 6,
    popup = "Destination"
  )
 }
 ```
-# Create boxplots for trajectory statistics
+## 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()
@@ -244,8 +452,20 @@ createBoxplots <- function(trajectory_stats_df) {
  }
  par(mfrow = c(1, 1))
 }
 ```
-# Create density plots for trajectory statistics
+```{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()
@@ -262,8 +482,19 @@ createDensityPlots <- function(trajectory_stats_df) {
  }
  par(mfrow = c(1, 1))
 }
 ```
-# Create histograms for trajectory statistics
+```{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()
@@ -278,8 +509,20 @@ createHistograms <- function(trajectory_stats_df) {
  }
  par(mfrow = c(1, 1))
 }
 ```
-# Generate interpretation text for trajectory statistics
+```{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
@@ -331,288 +574,7 @@ generateInterpretation <- function(trajectory_stats_df) {
 }
 ```
-
+```{r demo-interpretation, purl=FALSE, echo=TRUE}
 # 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. **Query Execution**: 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
 # Implementation
 The following section will demonstrate the implementation of the methodology using R code snippets. 
 The full analysis is also available in the GUI-based Shiny application.
 ## Step 1: API Authentication
 The `getCredentials()` function retrieves API credentials from environment variables, ensuring secure credential management.
 ```r
 creds <- getCredentials(
  client_id = Sys.getenv("OPENSKY_CLIENT_ID"),
  client_secret = Sys.getenv("OPENSKY_CLIENT_SECRET")
 )
 ```
 ## Step 2: Data Acquisition
 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}
 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")
 ```
 ## Step 3: Track Data Retrieval
 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}
 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
    }
  }
 }
 if (is.null(route_df)) {
  cat("No valid track data available\n")
 }
 ```
 ## 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.
 ```{r demo-route-plot, fig.width=7, fig.height=5}
 if (!is.null(route_df)) {
  createInteractiveMap(route_df)
 } else {
  cat("Insufficient data for visualization\n")
 }
 ```
 ## Step 5: Vertical Profile Analysis
 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}
 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")
 }
 ```
 ## Step 6: Trajectory Object Construction
 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}
 if (!is.null(route_df)) {
  trj <- getTrajFromRoute(route_df)
  plot(trj, main = paste("Metric Trajectory -", icao))
  cat("Trajectory object created with", nrow(trj), "waypoints\n")
 } else {
  cat("Insufficient data for trajectory construction\n")
 }
 ```
 ## Step 7: Single Flight Characterization
 The `calculateTrajectoryStats()` function computes comprehensive trajectory metrics. The table format provides a clear overview of individual flight characteristics.
 ```{r demo-stats-table}
 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))
 } else {
  cat("Insufficient data for statistical analysis\n")
 }
 ```
 ## Step 8: Multi-Flight Data Collection
 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}
 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")
 }
 ```
 # Results
 ## Individual Flight Metrics
 The following table presents computed metrics for all successfully analyzed flights.
 ```{r demo-all-stats-table}
 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")
 }
 ```
 ## Descriptive Statistics
 The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter.
 ```{r demo-summary-stats}
 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")
 }
 ```
 ## Distribution Analysis: Boxplots
 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}
 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")
 }
 ```
 ## Distribution Analysis: Kernel Density Estimation
 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}
 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")
 }
 ```
 ## Distribution Analysis: Histograms
 Histograms with overlaid density curves provide an alternative visualization of parameter distributions.
 ```{r demo-histograms, fig.width=10, fig.height=8}
 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")
 }
 ```
 ## Parameter Interpretation
 The `generateInterpretation()` function provides contextual analysis of the computed trajectory metrics.
 ```{r demo-interpretation}
 if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
  interpretation <- generateInterpretation(all_flights_stats)
  cat(interpretation)
@@ -621,6 +583,34 @@ if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
 }
 ```
 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
@@ -644,6 +634,9 @@ This project demonstrates the successful application of movement ecology metrics
 # References
- OpenSky Network: https://opensky-network.org/
+1.  [The OpenSky Network. (2025). Internet archive of observed aircraft trajectories.](https://opensky-network.org/datasets/states/)
- McLean, D.J. & Skowron Volponi, M.A. (2018). trajr: An R package for characterisation of animal trajectories. Ethology, 124(6), 440-448: https://CRAN.R-project.org/package=trajr
+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)
Author	SHA1	Message	Date
eneller	c1541c693a	doc: show screenshots in readme	2026-01-22 00:37:47 +01:00
eneller	1ab649f25f	doc: minor tweaks	2026-01-21 22:42:00 +01:00
eneller	137ddb197b	doc: include screenshots	2026-01-21 22:31:12 +01:00
eneller	5d129f1498	doc: finalize report	2026-01-21 22:21:24 +01:00
eneller	010345ae03	move doc	2026-01-21 16:02:00 +01:00
eneller	595388e413	build: add webshot for pdf screenshots	2026-01-21 15:57:57 +01:00
eneller	20270f5816	Create Web UI using shiny commit `3de1345642` Merge: `9106db3` `f4955af` Author: eneller <erikneller@gmx.de> Date: Wed Jan 21 15:14:32 2026 +0100 Merge branch 'feat/trajectories-and-alternative-gui' into feat/add-osm-for-trajectory commit `9106db35a9` Author: eneller <erikneller@gmx.de> Date: Wed Jan 21 15:13:28 2026 +0100 build: lock renv commit `f4955af7f4` Author: eneller <erikneller@gmx.de> Date: Wed Jan 21 13:13:23 2026 +0100 build: exclude demo chunks from purl commit `2757a86383` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Wed Jan 21 13:12:34 2026 +0100 🐛 add getAircraftTrajectories function commit `133827c2bd` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Wed Jan 21 12:54:03 2026 +0100 add interactive map with leaflet commit `a7aa5025ea` Merge: `74292bd` `93f4e3e` Author: eneller <erikneller@gmx.de> Date: Wed Jan 21 12:22:42 2026 +0100 Merge remote-tracking branch 'refs/remotes/origin/feat/trajectories-and-alternative-gui' into feat/trajectories-and-alternative-gui commit `93f4e3e81d` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Wed Jan 21 00:21:50 2026 +0100 📝 add more documentation commit `74292bd0ec` Merge: `ec51069` `86a9e41` Author: eneller <erikneller@gmx.de> Date: Wed Jan 21 00:09:16 2026 +0100 Merge remote-tracking branch 'origin/feat/trajectories-and-alternative-gui' into feat/trajectories-and-alternative-gui commit `ec51069f1d` Author: eneller <erikneller@gmx.de> Date: Tue Jan 20 23:49:51 2026 +0100 wip: fix: trajectory stats for single aircraft commit `86a9e4163f` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Tue Jan 20 23:36:07 2026 +0100 🐛 fixed the credentials bug for documentation commit `387e1caa7f` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Tue Jan 20 23:10:08 2026 +0100 📝 add documentation to main commit `696f52eda3` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Tue Jan 20 16:53:19 2026 +0100 ♻️ refactor all logic to main.rmd commit `eb49746268` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Tue Jan 20 16:13:41 2026 +0100 🩹 fix small issues in main commit `f491345ea0` Author: eneller <erikneller@gmx.de> Date: Tue Jan 20 15:46:26 2026 +0100 refactor: consolidate main.Rmd statistics commit `aacdc12638` Author: lukasadrion <lukas.adrion@uni-ulm.de> Date: Tue Jan 20 15:31:21 2026 +0100 ♻️ refactor app.Rmd commit `e4c7ce4977` Author: eneller <erikneller@gmx.de> Date: Tue Jan 20 13:48:49 2026 +0100 chore: move app from R to Rmd commit `d9a33a5d2b` Author: eneller <erikneller@gmx.de> Date: Mon Jan 19 17:45:35 2026 +0100 build: add dependencies commit `d8dd920d6b` Author: Patrik M <57401452+Patrik157@users.noreply.github.com> Date: Mon Jan 19 17:25:18 2026 +0100 Added trajectories and alternative GUI	2026-01-21 15:38:17 +01:00
eneller	e2699640ca	doc: slides	2026-01-20 18:10:55 +01:00