# actel v1.1.0

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## Acoustic Telemetry Data Analysis

Designed for studies where fish tagged with acoustic tags are expected to move through receiver arrays. This package combines the advantages of automatic sorting and checking of fish movements with the possibility for user intervention on tags that deviate from expected behaviour. The three analysis functions (explore(), migration() and residency()) allow the users to analyse their data in a systematic way, making it easy to compare results from different studies. CJS calculations are based on Perry et al. (2012) <https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>.

# actel

of non-interactive code*

## Overview

If you are using acoustic telemetry to track animals as they move inside a study area or as they migrate somewhere, actel is the package for you. By bringing together the study area configuration and the recorded detections, actel provides a systematic way of analysing fish migration and residency data.

### Main functions:

1. explore()

explore() allows you to quickly get a summary of your data. You can use explore() to get a general feel for the study results, and check if the input files are behaving as expected. It is also a good candidate if you just want to validate your detections for later use in other analyses.

2. migration()

The migration() analysis runs the same initial checks as explore(), but on top of it, it analyses the fish behaviour. By selecting the arrays that lead to success, you can define whether or not your fish survived the migration. Additional plots help you find out if some fish has been acting odd. Multiple options allow you to tweak the analysis to fit your study perfectly.

3. residency()

The residency() analysis runs the same initial checks as explore(), but, similarly to migration, explores particular points of the fish behaviour. If you want to know where your fish were in each day of the study, how many fish were in each section each day, and other residency-focused variables, this is the analysis you are looking for!

## Unlock actel's full potential

To truly learn how to operate actel, you must read the package vignettes. These have been arranged so that you can prepare your analysis as you learn; quite soon you will get your first results!

Here are some examples:

Movement tables:

Array Detections First station Last station First time Last time Time travelling Time on array
River1 14 St.1 St.2 2019-05-15 10:30:00 2019-05-15 13:00:00 NA 3:30
River2 3 St.4 St.4 2019-05-15 13:50:00 2019-05-15 14:40:00 0:50 0:50
River3 8 St.5 St.6 2019-05-15 16:00:00 2019-05-15 16:20:00 1:20 0:20
Fjord2 21 St.10 St.11 2019-05-16 15:10:00 2019-05-16 18:00:00 22:50 2:50
Sea1 1 St.18 St.18 2019-05-18 09:45:00 2019-05-18 09:45:00 15:45 0:00

Detection graphics

Times of arrival and summary information

Array efficiency and fish progression

Individual residency

Global residency

## Installing actel

CRAN version: 1.0.0

actel is available on CRAN. To install the latest stable version, simply run:

install.packages("actel")


Development version

If you would like to install the latest updates (which have not been integrated to CRAN yet), you can run the line below. Note that you need to have the package remotes installed!

remotes::install_github("hugomflavio/actel", build_opts = c("--no-resave-data", "--no-manual"), build_vignettes = TRUE)


Have a look at the manual:

After installing, you should read the package vignettes (i.e. the manual), which can be found by running:

browseVignettes("actel")


Note:

1. If the vignettes are not showing up with the command above, you can download them directly here: actel_vignettes.zip
2. If you are getting "pandoc document conversion" errors during the package installation, try installing the newest version of pandoc, restarting R and trying again.

* interactive code (i.e. code that expects user input) cannot be tested automatically using codecov and, as such, was excluded from the codecov scope.

## Vignettes of actel

 Name LaTeX_example_survival.svg R64K-4521.png R64K-4526.png R64K-4526_overridden.png a-0_workspace_requirements.Rmd a-1_study_area.Rmd a-2_distances_matrix.Rmd a-3_preload.Rmd actel_logo.png arrays_bad.svg arrays_good.svg b-0_explore.Rmd b-1_explore_processes.Rmd b-2_explore_results.Rmd badmovements.Rdata c-0_migration.Rmd c-1_migration_processes.Rmd c-2_migration_results.Rmd c-3_migration_efficiency.Rmd d-0_residency.Rmd d-1_residency_processes.Rmd d-2_residency_results.Rmd d-3_residency_efficiency.Rmd distances_shape_1.png distances_shape_2.png e-0_manual_mode.Rmd efficiency_a.svg efficiency_b.svg event_order1.svg event_order2.svg event_order_a1.svg event_order_b1.svg example_progression.svg f-0_post_functions.Rmd if_last_skip_section_false.svg if_last_skip_section_true.svg impassables_a.svg impassables_b.svg jump_examples.svg maximum_time.svg mb_arrays.svg mb_efficiency.svg multi-branch.svg multi_way_efficiency.svg multi_way_efficiency_alt.svg one_way_efficiency_a.svg one_way_efficiency_b.svg one_way_efficiency_c.svg readme_global_residency.png readme_individual_residency.png replicates_A.svg replicates_B.svg replicates_C.svg replicates_D.svg spatialcsv_a1.svg spatialcsv_a2.svg spatialcsv_a3.svg spatialcsv_a4.svg spatialcsv_a5.svg spatialcsv_a6.svg spatialcsv_b.svg spatialcsv_b1.svg spatialcsv_b2.svg spatialcsv_b3.svg spatialtxt_a.svg spatialtxt_b.svg spatialtxt_c.svg spatialtxt_d.svg speed_method.svg study_area.svg times_River3.svg times_Sea1.svg No Results!