Package: FishDiveR 1.1.0
FishDiveR: Classify Aquatic Animal Behaviours from Vertical Movement Data
Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering. Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics. Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster. Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.
Authors:
FishDiveR_1.1.0.tar.gz
FishDiveR_1.1.0.zip(r-4.7)FishDiveR_1.1.0.zip(r-4.6)FishDiveR_1.1.0.zip(r-4.5)
FishDiveR_1.1.0.tgz(r-4.6-any)FishDiveR_1.1.0.tgz(r-4.5-any)
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FishDiveR_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
FishDiveR/json (API)
NEWS
| # Install 'FishDiveR' in R: |
| install.packages('FishDiveR', repos = c('https://calvinsbeale.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/calvinsbeale/fishdiver/issues
Last updated from:f2ccac2ca8. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 277 | ||
| source / vignettes | OK | 225 | ||
| linux-release-x86_64 | OK | 270 | ||
| macos-release-arm64 | OK | 259 | ||
| macos-oldrel-arm64 | OK | 340 | ||
| windows-devel | OK | 193 | ||
| windows-release | OK | 180 | ||
| windows-oldrel | OK | 166 | ||
| wasm-release | OK | 181 |
Exports:combine_datacreate_depth_statscreate_waveletcreate_wavelet_statsimport_tag_datak_clusteringpca_datapca_resultspca_scoresplot_cluster_TDRplot_clustersplot_TDRselect_k
Dependencies:abindbackportsbase64encbootbroombslibcachemcarcarDatacliclustercolorspacecowplotcpp11crosstalkdata.tableDerivdigestdoBydplyrDTellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustfontawesomeforecastFormulafracdifffsgenericsgeometryggplot2ggrepelgluegridExtragtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelinproglme4lmtestlpSolvelubridatemagicmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmomentsmultcompViewmvtnormnlmenloptrnnetnumDerivotelpatchworkpbkrtestpillarpkgconfigpromisespurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppProgressRdpackreformulasRfastrglrlangrmarkdownS7sassscalesscatterplot3dSparseMstringistringrsuncalcsurvivaltibbletidyrtidyselecttimechangetimeDatetinytexurcautf8vctrsviridisLiteWaveletCompwithrxfunyamlziggzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Import depth statistics and combine with PC scores | combine_data |
| Create depth statistics | create_depth_stats |
| Create and plot the wavelet power spectrum | create_wavelet |
| create_wavelet_stats | create_wavelet_stats |
| Load time-depth series data from csv file | import_tag_data |
| Perform k-means | k_clustering |
| Prepare all data for Principal Component Analysis | pca_data |
| Perform Principal Component Analysis | pca_results |
| Calculate Principal Component Analysis Scores not including depth statistics | pca_scores |
| Plot the time-series depth records of the selected tag. Colour days by cluster | plot_cluster_TDR |
| Plot the time-series depth records of the days closest to the centre of each cluster | plot_clusters |
| Plot the time-series depth dataset | plot_TDR |
| Perform k selection | select_k |
