Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R...

Area of Science:

• Ecological research
• Earth System Sciences
• Data science

Background:

• Scientific research generates vast amounts of data, posing challenges for traditional analysis methods.
• Ensuring data quality, exploration, and reproducibility is crucial but often impractical at scale.
• Existing interactive tools may lack data manipulation capabilities or reproducible outputs.

Purpose of the Study:

• To introduce datacleanr, an open-source R package for interactive data exploration and quality control.
• To facilitate reproducible data processing for tabular, time series, and georeferenced data.
• To enhance researchers' workflows and the reliability of scientific findings.

Main Methods:

• Development of datacleanr, an interactive R package.
• Implementation of a "reproducible recipe" feature translating interactive actions into R code.
• Demonstration of utility in handling nested data and time series filtering.

Main Results:

• datacleanr enables interactive data exploration, outlier assessment, and flexible processing.
• The "reproducible recipe" integrates interactive strengths with script-based R workflows.
• The package effectively addresses challenges in nested data structures and time series data loss.

Conclusions:

• datacleanr enhances data analysis workflows by combining interactive processing with reproducibility.
• The tool improves confidence in research results for ecological and Earth System Sciences.
• Open-source availability promotes wider adoption and collaborative improvement.