Scripts and data to run and produce results from R-QWTREND models

This child page contains a zipped folder which contains all items necessary to run trend models and produce results published in U.S. Geological Scientific Investigations Report 2022–XXXX [Nustad, R.A., and Tatge, W.S., 2023, Comprehensive Water-Quality Trend Analysis for Selected Sites and Constituents in the International Souris River Basin, Saskatchewan and Manitoba, Canada and North Dakota, United States, 1970-2020: U.S. Geological Survey Scientific Investigations Report 2023-XXXX, XX p.]. To run the R-QWTREND program in R, 6 files are required and each is included in this child page: prepQWdataV4.txt, runQWmodelV4.txt, plotQWtrendV4.txt, qwtrend2018v4.exe, salflibc.dll, and StartQWTrendV4.R (Vecchia and Nustad, 2020). The folder contains: three items required to run the R–QWTREND trend analysis tool; a README.txt file; a folder called "dataout"; and a folder called "scripts". The "scripts" folder contains the scripts that can be used to reproduce the results found in the USGS Scientific Investigations Report referenced above. The "dataout" folder contains folders for each site that contain .RData files with the naming convention of site_flow for streamflow data and site_qw_XXX depending upon the group of constituents MI, NUT, or TM. R–QWTREND is a software package for analyzing trends in stream-water quality. The package is a collection of functions written in R (R Development Core Team, 2019), an open source language and a general environment for statistical computing and graphics. The following system requirements are necessary for using R–QWTREND: • Windows 10 operating system • R (version 3.4 or later; 64 bit recommended) • RStudio (version 1.1.456 or later). An accompanying report (Vecchia and Nustad, 2020) serves as the formal documentation for R–QWTREND. Vecchia, A.V., and Nustad, R.A., 2020, Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality: U.S. Geological Survey Open-File Report 2020–1014, 51 p., https://doi.org/10.3133/ofr20201014 R Development Core Team, 2019, R—A language and environment for statistical computing: Vienna, Austria, R Foundation for Statistical Computing, accessed December 7, 2020, at https://www.r-project.org.