Precision Agriculture Data Analysis

Precision agriculture spatial data depuration and homogeneous zones (management zone) delineation. The package includes functions that performs protocols for data cleaning management zone delineation and zone comparison; protocols are described in Paccioretti et al., (2020) .

Interface to the World Database on Protected Areas

Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet < https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the 'prepr' R package (available at < https://github.com/prioritizr/prepr>). For more information on this package, see Hanson (2022) .

Detect and Treat Outliers in Data Mining

Implements a suite of tools for outlier detection and treatment in data mining. It includes univariate methods (Z-score, Interquartile Range), multivariate detection using Mahalanobis distance, and density-based detection (Local Outlier Factor) via the 'dbscan' package. It also provides functions for visualization using 'ggplot2' and data cleaning via Winsorization.

Tidy Messy Data

Tools to help to create tidy data, where each column is a variable, each row is an observation, and each cell contains a single value. 'tidyr' contains tools for changing the shape (pivoting) and hierarchy (nesting and 'unnesting') of a dataset, turning deeply nested lists into rectangular data frames ('rectangling'), and extracting values out of string columns. It also includes tools for working with missing values (both implicit and explicit).

R Wrapper for the 'OneMap.Sg API'

An R wrapper for the 'OneMap.Sg' API < https://www.onemap.gov.sg/docs/>. Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.

Simple Data Frames

Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional data frame.

Text Analysis for All

An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. 'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.

Longitudinal Integration Site Analysis Toolkit

A comprehensive toolkit for the analysis of longitudinal integration site data, including data cleaning, quality control, statistical modeling, and visualization. It streamlines the entire workflow of integration site analysis, supports simple input formats, and provides user-friendly functions for researchers in virus integration site analysis. Ni et al. (2025) .

'Geocoordinate Validation Service'

The 'Geocoordinate Validation Service' (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning. Additionally, the package has functions related to attribution and metadata information. More information can be found at < https://github.com/ojalaquellueva/gvs/tree/master/api>.

Sites, Population, and Records Cleaning Skills

Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) .