Overview
Camera traps are an essential tool for wildlife monitoring and ecological research. They generate vast amounts of data that require careful processing, cleaning, and analysis to extract meaningful insights. Researchers use camera trap data for tasks such as species identification, biodiversity assessment, activity pattern analysis, and occupancy modeling.
Processing and analyzing camera trap data in R often requires multiple steps, from cleaning raw data to statistical modeling and visualization. The ct R package addresses these challenges by providing a modern, tidyverse-friendly workflow for camera trap data analysis. Using tidy evaluation principles, it enables users to efficiently manipulate and transform datasets. Additionally, it integrates seamlessly with ggplot2, allowing users to generate highly customizable visualizations.
Key Features
The ct package provides a comprehensive suite of 55+ functions covering the complete camera trap data analysis workflow. Population density estimation is supported through ct_fit_ds() for distance sampling with automated model selection, ct_fit_rem() for Random Encounter Models, and ct_traprate_estimate() for trap rate calculations. Data management capabilities include ct_independence() for filtering independent detections, ct_correct_datetime() for timestamp correction, and ct_check_location() for interactive spatial validation. Community ecology functions enable activity pattern analysis with ct_plot_density() and ct_overlap_estimates(), biodiversity assessment through ct_alpha_diversity() and ct_to_community(), and occupancy modeling via ct_to_occupancy(). Quality control tools include ct_find_break() for detecting temporal gaps, ct_plot_camtrap_activity() for monitoring deployment status, and ct_check_name() for taxonomic validation. The package also features Camera Trap Data Package integration, survey design tools with ct_survey_design(), and more.
For a full overview of all available functions, please visit the ct website
Installation:
You can install ct directly from GitHub:
# Install pak firstly if not installed
if (!requireNamespace("pak", quietly = TRUE)) {
install.packages("pak", dependencies = TRUE)
}
# Install maimer from GitHub
pak::pkg_install("stangandaho/ct")Code of conduct
Please note that this project is based on the Contributor Covenant v2.1. By participating in this project you agree to abide by its terms.
Getting help
If you encounter a clear bug, please file an issue with a minimal reproducible example. For questions and other discussion, please use relevant section.
Funding
The development of the ct package is supported by the R Consortium Infrastructure Steering Committee (ISC) under grant 25-ISC-1-04. This funding enables the creation of comprehensive statistical tools for camera trap data analysis, including population density estimation methods, and standardized data integration workflows.