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{"references": ["Chang W., Cheng J., Allaire J., Xie Y., McPherson J. (2020) Shiny: Web Application Framework for R. R package version 1.5.0. https://CRAN.R-project.org/package=shiny", "R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/", "Sievert C (2020) Interactive Web-Based Data Visualization with R, plotly, and shiny, Chapman and Hall/CRC, Florida. https://plotly-r.com", "Wickham H., Averic M., Bryan J., Chang W., McGowan L., Fran\u00e7ois R., Grolemund G., Hayes A., Henry L., Hester J., Kuhn M., Pedersen T., Miller E., Bache S., M\u00fcller K., Ooms J., Robinson D., Seidel D., Spinu V., Takahashi K., Vaughan D., Wilke C., Woo K., Yutani H. (2019) Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686"]}
Preprint release of the Groundwater level time series visual inspection web application. It is R Shiny application that assists visual analysis of groundwater level time series and ensures an approach to correct errors, while maintaining original data intact. Few simulated groundwater time series with errors are provided to demonstrate the application. The simulated data is stored in SQLite database.
nearby wells, outliers, interactive inspection, correction, record correction
nearby wells, outliers, interactive inspection, correction, record correction
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