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rATTAINS

CRAN status rATTAINS status badge Project Status: Active – The project has reached a stable, usable state and is being actively developed. R-CMD-check codecov DOI

rATTAINS provides functions for downloading tidy data from the United States (U.S.) Environmental Protection Agency (EPA) ATTAINS webservice. ATTAINS is the online system used to track and report Clean Water Act assessments and Total Maximum Daily Loads (TMDLs) in U.S. surface waters. rATTAINS facilitates access to the public information webservice made available through the EPA.

rATTAINS is on CRAN:

install.packages('rATTAINS')

Or install the development version from r-universe:

install.packages('rATTAINS',
                 repos = 'https://mps9506.r-universe.dev')

Functions and webservices

There are eight user available functions that correspond with the first eight web services detailed by EPA. All arguments are case sensitive. By default the functions attempt to provide flattened “tidy” data as a single or multiple dataframes. By using the tidy = FALSE argument in the function below, the raw JSON will be read into the session for the user to parse if desired. This can be useful since some webservices provide different results based on the query and the tidying process used in rATTAINS might make poor assumptions in the data flattening process. If the function returns unexpected results, try parsing the raw JSON string.

Examples:

Get a summary about assessed uses from the Texas Commission on Environmental Quality:

library(rATTAINS)
state_summary(organization_id = "TCEQMAIN", 
              reporting_cycle = "2020",
              .unnest = FALSE) |>
  tidyr::unnest(reporting_cycles) |> 
  tidyr::unnest(water_types) |> 
  tidyr::unnest(use_attainments)
#> # A tibble: 31 × 16
#>    organization_identifer organization_name organization_type_text
#>    <chr>                  <chr>             <chr>                 
#>  1 TCEQMAIN               Texas             State                 
#>  2 TCEQMAIN               Texas             State                 
#>  3 TCEQMAIN               Texas             State                 
#>  4 TCEQMAIN               Texas             State                 
#>  5 TCEQMAIN               Texas             State                 
#>  6 TCEQMAIN               Texas             State                 
#>  7 TCEQMAIN               Texas             State                 
#>  8 TCEQMAIN               Texas             State                 
#>  9 TCEQMAIN               Texas             State                 
#> 10 TCEQMAIN               Texas             State                 
#> # ℹ 21 more rows
#> # ℹ 13 more variables: reporting_cycle <chr>, water_type_code <chr>,
#> #   units_code <chr>, use_name <chr>, fully_supporting <dbl>,
#> #   fully_supporting_count <int>, use_insufficient_information <dbl>,
#> #   use_insufficient_information_count <int>, not_assessed <dbl>,
#> #   not_assessed_count <int>, not_supporting <dbl>, not_supporting_count <int>,
#> #   parameters <list<tibble[,9]>>

Get a summary about assessed uses, parameters and plans in a HUC12:

df <- huc12_summary(huc = "020700100204",
              .unnest = FALSE)

tidyr::unnest(df, summary_by_use)
#> # A tibble: 5 × 24
#>   huc12        assessment_unit_count total_catchment_area…¹ total_huc_area_sq_mi
#>   <chr>                        <int>                  <dbl>                <dbl>
#> 1 020700100204                    17                   46.1                 46.2
#> 2 020700100204                    17                   46.1                 46.2
#> 3 020700100204                    17                   46.1                 46.2
#> 4 020700100204                    17                   46.1                 46.2
#> 5 020700100204                    17                   46.1                 46.2
#> # ℹ abbreviated name: ¹​total_catchment_area_sq_mi
#> # ℹ 20 more variables: assessed_catchment_area_sq_mi <dbl>,
#> #   assessed_cathcment_area_percent <dbl>,
#> #   assessed_good_catchment_area_sq_mi <dbl>,
#> #   assessed_good_catchment_area_percent <dbl>,
#> #   assessed_unknown_catchment_area_sq_mi <dbl>,
#> #   assessed_unknown_catchment_area_percent <dbl>, …

tidyr::unnest(df, summary_by_parameter_impairments, names_repair = "minimal")
#> # A tibble: 16 × 25
#>    huc12       assessment_unit_count total_catchment_area…¹ total_huc_area_sq_mi
#>    <chr>                       <int>                  <dbl>                <dbl>
#>  1 0207001002…                    17                   46.1                 46.2
#>  2 0207001002…                    17                   46.1                 46.2
#>  3 0207001002…                    17                   46.1                 46.2
#>  4 0207001002…                    17                   46.1                 46.2
#>  5 0207001002…                    17                   46.1                 46.2
#>  6 0207001002…                    17                   46.1                 46.2
#>  7 0207001002…                    17                   46.1                 46.2
#>  8 0207001002…                    17                   46.1                 46.2
#>  9 0207001002…                    17                   46.1                 46.2
#> 10 0207001002…                    17                   46.1                 46.2
#> 11 0207001002…                    17                   46.1                 46.2
#> 12 0207001002…                    17                   46.1                 46.2
#> 13 0207001002…                    17                   46.1                 46.2
#> 14 0207001002…                    17                   46.1                 46.2
#> 15 0207001002…                    17                   46.1                 46.2
#> 16 0207001002…                    17                   46.1                 46.2
#> # ℹ abbreviated name: ¹​total_catchment_area_sq_mi
#> # ℹ 21 more variables: assessed_catchment_area_sq_mi <dbl>,
#> #   assessed_cathcment_area_percent <dbl>,
#> #   assessed_good_catchment_area_sq_mi <dbl>,
#> #   assessed_good_catchment_area_percent <dbl>,
#> #   assessed_unknown_catchment_area_sq_mi <dbl>,
#> #   assessed_unknown_catchment_area_percent <dbl>, …

tidyr::unnest(df, summary_restoration_plans, names_repair = "minimal")
#> # A tibble: 1 × 25
#>   huc12        assessment_unit_count total_catchment_area…¹ total_huc_area_sq_mi
#>   <chr>                        <int>                  <dbl>                <dbl>
#> 1 020700100204                    17                   46.1                 46.2
#> # ℹ abbreviated name: ¹​total_catchment_area_sq_mi
#> # ℹ 21 more variables: assessed_catchment_area_sq_mi <dbl>,
#> #   assessed_cathcment_area_percent <dbl>,
#> #   assessed_good_catchment_area_sq_mi <dbl>,
#> #   assessed_good_catchment_area_percent <dbl>,
#> #   assessed_unknown_catchment_area_sq_mi <dbl>,
#> #   assessed_unknown_catchment_area_percent <dbl>, …

Find statistical surveys completed by an organization:

surveys(organization_id="SDDENR",
        .unnest = FALSE) |> 
  tidyr::unnest(survey_water_groups) |> 
  tidyr::unnest(survey_water_group_use_parameters)
#> # A tibble: 104 × 21
#>    organization_identifier organization_name organization_type_text
#>    <chr>                   <chr>             <chr>                 
#>  1 SDDENR                  South Dakota      State                 
#>  2 SDDENR                  South Dakota      State                 
#>  3 SDDENR                  South Dakota      State                 
#>  4 SDDENR                  South Dakota      State                 
#>  5 SDDENR                  South Dakota      State                 
#>  6 SDDENR                  South Dakota      State                 
#>  7 SDDENR                  South Dakota      State                 
#>  8 SDDENR                  South Dakota      State                 
#>  9 SDDENR                  South Dakota      State                 
#> 10 SDDENR                  South Dakota      State                 
#> # ℹ 94 more rows
#> # ℹ 18 more variables: survey_status_code <chr>, year <int>,
#> #   survey_comment_text <chr>, documents <list<tibble[,8]>>,
#> #   water_type_group_code <chr>, sub_population_code <chr>, unit_code <chr>,
#> #   size <int>, site_number <int>, surey_water_group_comment_text <chr>,
#> #   stressor <chr>, survey_use_code <chr>, survey_category_code <chr>,
#> #   statistic <chr>, metric_value <dbl>, margin_of_error <dbl>, …

Citation

If you use this package in a publication, please cite as:

citation("rATTAINS")
#> 
#> To cite rATTAINS in publications use:
#> 
#>   Schramm, Michael (2021).  rATTAINS: Access EPA 'ATTAINS' Data.  R
#>   package version 1.0.0. doi:10.5281/zenodo.5469911
#>   https://CRAN.R-project.org/package=rATTAINS
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {{rATTAINS}: Access EPA 'ATTAINS' Data},
#>     author = {Michael Schramm},
#>     year = {2021},
#>     url = {https://CRAN.R-project.org/package=rATTAINS},
#>     doi = {10.5281/zenodo.5469911},
#>     note = {R package version 1.0.0},
#>   }

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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