Print Directory Trees for R Projects and Folders

Quickly visualize 'R' project directory structures with automatic project detection and clean tree output.

Robust Exponential Decreasing Index

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) < https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

Create Draggable Plots from Projected Geometry

Creates interactive draggable plots from grouped projected 'sf' geometry. The primary deliverable is a browser-based 'D3' helper where regions and labels can be moved freely; users drag, then copy or download the resulting offset tables. Labels can be derived automatically with make_region_labels(), supplied directly with as_drag_labels(), and their moved positions saved and restored with read_label_state() and apply_label_state(). Hierarchical spatial datasets are supported via hierarchy detection, upload profiling, make_hierarchy_key(), and inherit_layout(), which recommend parent-child groupings and propagate parent-level drag offsets to finer child groupings. Automatic starting layouts are provided by suggest_offsets() using radial, grid, or directional algorithms. Spatial file diagnostics are available through dragmapr_diagnostics(). When a reproducible static image is also needed, render_dragged_map() reconstructs the layout as a 'ggplot2' plot from the source geometry plus the exported offset tables. Project bundles can be written with write_dragmapr_project() and rendered with render_dragmapr_project(). The interactive layer is built on the 'D3' library: Bostock, Ogievetsky and Heer (2011) . Spatial data handling uses the 'sf' package: Pebesma (2018) .

Parse 'Tableau' Workbooks into Tidy Data and Dependency Graphs

High-performance parsing of 'Tableau' workbook files into tidy data frames and dependency graphs for other visualization tools like R 'Shiny' or 'Power BI' replication, plus an interactive 'Shiny' workbook inspector for uploaded .twb and .twbx files.

Load 'Overture' Datasets as 'dbplyr' and 'sf'-Ready Data Frames

An integrated R interface to the 'Overture' API (< https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 'dbplyr' data frames or materialized 'sf' spatial data frames.

Audit-Ready Geocoding and Local Geography for Messy Location Data

Cleans, geocodes, validates, reviews, and exports messy location address data supplied by the user. The package sits on top of 'tidygeocoder': it calls geocoding services, rejects implausible coordinates with configurable region guards, applies fallback name/address matching, joins points to optional local geography with 'sf', and records an audit trail showing how each coordinate was produced. Outputs are designed for manual review, dashboards, and reusable location crosswalks.

Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

Soundscape Background Noise, Power, and Saturation

Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) < https://eprints.qut.edu.au/110634/> and Burivalova (2017) .

Classify Occurrences by Confidence Levels in the Species ID

Classify occurrence records based on confidence levels of species identification. In addition, implement tools to filter occurrences inside grid cells and to manually check for possibles errors with an interactive shiny application.

Analysing 'SNP' and 'Silicodart' Data - Basic Functions

Facilitates the import and analysis of 'SNP' (single nucleotide 'polymorphism') and 'silicodart' (presence/absence) data. The main focus is on data generated by 'DarT' (Diversity Arrays Technology), however, data from other sequencing platforms can be used once 'SNP' or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived 'genlight' format (package 'adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of 'SNP' and 'silicodart' data, for reporting on and filtering on various criteria (e.g. 'callrate', 'heterozygosity', 'reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. 'dartR.base' is the 'base' package of the 'dartRverse' suits of packages. To install the other packages, we recommend to install the 'dartRverse' package, that supports the installation of all packages in the 'dartRverse'. If you want to cite 'dartR', you find the information by typing citation('dartR.base') in the console.