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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.
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: ¹total_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: ¹total_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: ¹total_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>, …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.
Health stats visible at Monitor.