README.md
In rATTAINS: Access EPA 'ATTAINS' Data

rATTAINS

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')

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.

state_summary() provides summary information for assessed uses for organizations and by integrated reporting cycle.
huc_12_summary() provides summary information about impairments, actions, and documents for the specified 12-digit HUC (watershed).
actions() provides a summary of information for particular finalized actions (TMDLs and related).
assessments() provides summary data about the specified assessment decisions by waterbody.
plans() returns a summary of the plans (TMDLs and related) within a specified HUC.
domain_values() returns allowed values in ATTAINS. By default (no arguments) the function returns a list of allowed domain_names.
assessment_units() returns a summary of information about the specified assessment units.
surveys() returns results from state statistical survey results in ATTAINS.

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
)
#> $items
#> # A tibble: 31 × 18
#>    organizationIdentifier organizationName organizationTypeText reportingCycle
#>    <chr>                  <chr>            <chr>                <chr>         
#>  1 TCEQMAIN               Texas            State                2020          
#>  2 TCEQMAIN               Texas            State                2020          
#>  3 TCEQMAIN               Texas            State                2020          
#>  4 TCEQMAIN               Texas            State                2020          
#>  5 TCEQMAIN               Texas            State                2020          
#>  6 TCEQMAIN               Texas            State                2020          
#>  7 TCEQMAIN               Texas            State                2020          
#>  8 TCEQMAIN               Texas            State                2020          
#>  9 TCEQMAIN               Texas            State                2020          
#> 10 TCEQMAIN               Texas            State                2020          
#> # ℹ 21 more rows
#> # ℹ 14 more variables: cycleStatus <chr>, combinedCycles <list>,
#> #   waterTypeCode <chr>, unitsCode <chr>, useName <chr>,
#> #   `Fully Supporting` <dbl>, `Fully Supporting-count` <int>,
#> #   `Insufficient Information` <dbl>, `Insufficient Information-count` <int>,
#> #   `Not Assessed` <dbl>, `Not Assessed-count` <int>, `Not Supporting` <dbl>,
#> #   `Not Supporting-count` <int>, parameters <list>

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

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

tidyr::unnest(df, items) |>
  tidyr::unnest(summaryByUseGroup)
#> # A tibble: 4 × 24
#>   huc12        assessmentUnitCount totalCatchmentAreaSqMi totalHucAreaSqMi
#>   <chr>                      <int>                  <dbl>            <dbl>
#> 1 020700100204                  18                   46.1             46.2
#> 2 020700100204                  18                   46.1             46.2
#> 3 020700100204                  18                   46.1             46.2
#> 4 020700100204                  18                   46.1             46.2
#> # ℹ 20 more variables: assessedCatchmentAreaSqMi <dbl>,
#> #   assessedCatchmentAreaPercent <dbl>, assessedGoodCatchmentAreaSqMi <int>,
#> #   assessedGoodCatchmentAreaPercent <int>,
#> #   assessedUnknownCatchmentAreaSqMi <int>,
#> #   assessedUnknownCatchmentAreaPercent <int>,
#> #   containImpairedWatersCatchmentAreaSqMi <dbl>,
#> #   containImpairedWatersCatchmentAreaPercent <dbl>, …

tidyr::unnest(df, items) |>
  tidyr::unnest(summaryByParameterImpairments, names_repair = "minimal")
#> # A tibble: 16 × 26
#>    huc12        assessmentUnitCount totalCatchmentAreaSqMi totalHucAreaSqMi
#>    <chr>                      <int>                  <dbl>            <dbl>
#>  1 020700100204                  18                   46.1             46.2
#>  2 020700100204                  18                   46.1             46.2
#>  3 020700100204                  18                   46.1             46.2
#>  4 020700100204                  18                   46.1             46.2
#>  5 020700100204                  18                   46.1             46.2
#>  6 020700100204                  18                   46.1             46.2
#>  7 020700100204                  18                   46.1             46.2
#>  8 020700100204                  18                   46.1             46.2
#>  9 020700100204                  18                   46.1             46.2
#> 10 020700100204                  18                   46.1             46.2
#> 11 020700100204                  18                   46.1             46.2
#> 12 020700100204                  18                   46.1             46.2
#> 13 020700100204                  18                   46.1             46.2
#> 14 020700100204                  18                   46.1             46.2
#> 15 020700100204                  18                   46.1             46.2
#> 16 020700100204                  18                   46.1             46.2
#> # ℹ 22 more variables: assessedCatchmentAreaSqMi <dbl>,
#> #   assessedCatchmentAreaPercent <dbl>, assessedGoodCatchmentAreaSqMi <int>,
#> #   assessedGoodCatchmentAreaPercent <int>,
#> #   assessedUnknownCatchmentAreaSqMi <int>,
#> #   assessedUnknownCatchmentAreaPercent <int>,
#> #   containImpairedWatersCatchmentAreaSqMi <dbl>,
#> #   containImpairedWatersCatchmentAreaPercent <dbl>, …

tidyr::unnest(df, items) |>
  tidyr::unnest(summaryRestorationPlans, names_repair = "minimal")
#> # A tibble: 1 × 26
#>   huc12        assessmentUnitCount totalCatchmentAreaSqMi totalHucAreaSqMi
#>   <chr>                      <int>                  <dbl>            <dbl>
#> 1 020700100204                  18                   46.1             46.2
#> # ℹ 22 more variables: assessedCatchmentAreaSqMi <dbl>,
#> #   assessedCatchmentAreaPercent <dbl>, assessedGoodCatchmentAreaSqMi <int>,
#> #   assessedGoodCatchmentAreaPercent <int>,
#> #   assessedUnknownCatchmentAreaSqMi <int>,
#> #   assessedUnknownCatchmentAreaPercent <int>,
#> #   containImpairedWatersCatchmentAreaSqMi <dbl>,
#> #   containImpairedWatersCatchmentAreaPercent <dbl>, …

Find statistical surveys completed by an organization:

surveys(organization_id = "SDDENR", .unnest = TRUE)
#> Unable to further unnest data, check for nested dataframes.
#> $count
#> # A tibble: 1 × 1
#>   count
#>   <int>
#> 1     5
#> 
#> $items
#> # A tibble: 5 × 14
#>   organizationIdentifier organizationName organizationTypeText surveyStatusCode
#>   <chr>                  <chr>            <chr>                <chr>           
#> 1 SDDENR                 South Dakota     State                Final           
#> 2 SDDENR                 South Dakota     State                Final           
#> 3 SDDENR                 South Dakota     State                Final           
#> 4 SDDENR                 South Dakota     State                Final           
#> 5 SDDENR                 South Dakota     State                Final           
#> # ℹ 10 more variables: year <int>, surveyCommentText <lgl>, documents <list>,
#> #   waterTypeGroupCode <chr>, subPopulationCode <chr>, unitCode <chr>,
#> #   size <int>, siteNumber <int>, surveyWaterGroupCommentText <chr>,
#> #   surveyWaterGroupUseParameters <list>

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 1.0.1 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 = {2025},
#>     url = {https://CRAN.R-project.org/package=rATTAINS},
#>     doi = {10.5281/zenodo.5469911},
#>     note = {R package version 1.0.1},
#>   }