stagg:: A data pre-processing R package for climate impacts analysis

Published Article

Global

Publication date: September 11, 2024

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High‑resolution climate data are increasingly important for understanding climate impacts, yet processing these complex datasets remains a major barrier for many researchers. This study introduces stagg, an R package that streamlines nonlinear transformations, spatial and temporal aggregation, and weighting by social or economic variables. By consolidating climate‑data workflows into just a few lines of code, stagg expands access for social scientists and supports more robust, evidence‑based climate‑impact research.

Subject Tags

  • Climate impacts
  • Conservation Technology
  • Data Science and Artificial Intelligence

Abstract

The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator,” a new R package that streamlines three critical components of climate data processing for impacts analysis: nonlinear transformation, spatial and temporal aggregation, and spatial weighting by social or economic variables. The package consolidates the data processing pipeline into a few lines of code, lowering barriers to entry for researchers and facilitating a larger and more diverse research community. The paper provides an overview of stagg's functions, followed by an applied example demonstrating the package's utility in climate impacts research. stagg has the potential to be a valuable tool in generating evidence-based estimates of the likely impacts of future climate change.

Citation

Liddell, T., Boser, A. S., Orofino, S., Mangin, T., & Carleton, T. (2025). stagg:: A data pre-processing R package for climate impacts analysis. Environmental Modelling & Software, 183, 106202. https://doi.org/10.1016/j.envsoft.2024.106202

TNC Authors