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An R Shiny tool to analyze probability-based survey data

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  • Overview
Many organizations now use probability-based surveys for measuring the condition of aquatic resources. These include some states and tribes, as well as EPA’s National Aquatic Resource Surveys. The R package ‘spsurvey’ has long been used to analyze the data produced from such surveys. However, the analysis functions are somewhat complicated to use, particularly for someone not very familiar with R. Shiny is an R package that provides a GUI to run R code, such that the user does not need to know how to program in R. I built an R Shiny application with a web browser interface in order to provide more friendly access to the data analysis functions in the ‘spsurvey’ package. This tool is designed to allow the user to upload a file, select the appropriate variables from their dataset for analysis, select options for analysis, run the analysis, and save the results to an output file. For users that are unfamiliar with R, or just uncomfortable writing R code, this application provides a straightforward interface that makes it easy to run the code to analyze their probability-based survey data.

Impact/Purpose

EPA has used probability-based surveys to assess the nation’s waters as part of the National Aquatic Resource Surveys (NARS). Many states are now also using this approach to measure condition of their aquatic resources. An R package, ‘spsurvey’, was developed to analyze such probability-based survey data, but its functions are somewhat complicated to use and require programming in R code. An R Shiny application that uses a web browser interface was created to allow users to upload data, select variables from input dataset, run analyses, and save the output without having to know how to program in R. For users, that might be interested in using probability-based surveys but are not proficient in R, this tool provides them with a way to easily analyze the data themselves.

Citation

Blocksom, K. An R Shiny tool to analyze probability-based survey data. 12th National Monitoring Conference, Virtual, OR, April 19 - 24, 2021.
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Last updated on May 04, 2021
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