Introduction

Geospatial data—data with location information—are widely used to inform environmental management and policy decisions, particularly through modeling and mapping exercises. For example, remote sensing data can be paired with ground‐based observations to create terrestrial species distribution maps (Elith and Leathwick 2009a, 2009b). Similarly, global sea surface temperature data and other environmental variables are used in fisheries and marine ecosystem models (Tittensor et al. 2021; Vaihola and Kininmonth 2023), and bathymetry data are used to map geomorphological features on the seafloor (Harris et al. 2014; Yesson et al. 2021). In addition, stakeholder mapping exercises can capture local knowledge of habitat status and ocean uses (Flower et al. 2020; Yates and Schoeman 2013), but require contextual geospatial data—such as bathymetry, seamount locations, and physical and legal boundaries—to act as references.

Before geospatial data can be used in analyses or models, these data must be acquired, processed and standardized (McCarthy et al. 2021). Finding data can be challenging because geospatial data are often scattered across many websites and databases. Acquiring data for a particular area of interest often requires technical skills, such as a knowledge of query languages (e.g., SQL) and database structures. Finally, geospatial data often need to be collated and harmonized to conform to the same spatial units (e.g., spatial grid, point localities of interest, or irregular spatial boundaries) for subsequent analysis, requiring additional geospatial data processing expertise. Although a diverse ecosystem of software has been developed to help with processing geospatial data (e.g., ArcGIS, QGIS, GDAL), the R statistical computing environment (R Core Team 2025) is widely used in the natural and environmental sciences (Lai et al. 2019) and is being rapidly expanded to increase its geospatial processing and visualization capabilities (Lovelace et al. 2025).

Over 20,000 packages are available on the Comprehensive R Archive Network (CRAN 2026) to extend the functionality of the R statistical computing environment. Many of these are designed specifically for geospatial data manipulation (Bivand et al. 2025), such as the sf (Pebesma 2018) and terra (Hijmans 2025) packages that are widely used for handling vector and raster data. In addition, there are packages that allow for acquisition of specific types of geospatial data, such as geoData, principally for terrestrial data (Hijmans et al. 2024), mregions2 for maritime boundaries (Fernandez‐Bejarano and Pohl 2023), and biooracler for marine biophysical data (Assis et al. 2024; Fernandez 2024). However, there are currently no R packages, to our knowledge, that provide access to a broad range of marine geospatial datasets and provide functions for processing and standardizing these data.

Here, we fill this critical gap, introducing the oceandatr R package for simplifying the process of acquiring, processing, standardizing, and analyzing marine data. The package provides functions for acquiring many widely used marine datasets, including bathymetry, seamounts, and biophysical data. Additionally, the package provides users with the flexibility to grid and standardize their own data, and classification methods to facilitate visualization and further analyses. Although oceandatr is marine focused, its functions to retrieve boundaries, create a custom spatial grid, and aggregate data at the grid level can be applied to terrestrial data. We discuss how the package could provide novel support for various research and management applications, and we offer guidance on how it can be used in conjunction with other R packages. Lastly, we provide case studies where oceandatr is used to acquire, grid, and classify data for subsequent use in spatial planning and in a spatial fisheries model.