README.md
In hepplerj/superfundr: Superfund Sites in the United States

superfundr

The superfundr package contains data on U.S. Superfund sites established by the Environmental Protection Agency.

The data is processed with a combination of the tabulizer package and various tidyverse methods using the most recent available PDF and Excel data from the Environmental Protection Agency.

superfundr is a data package containing a dataset of Superfund sites in the United States. The best way to install it is through devtools. You can install superfundr from GitHub with:

library(devtools)
devtools::install_github("hepplerj/superfundr")

The package works best with the tidyverse libraries and the simple features package for mapping.

library(tidyverse)
#> ── Attaching packages ──────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.0     ✔ purrr   0.3.3
#> ✔ tibble  2.1.3     ✔ dplyr   0.8.1
#> ✔ tidyr   0.8.3     ✔ stringr 1.4.0
#> ✔ readr   1.3.1     ✔ forcats 0.4.0
#> ── Conflicts ─────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

Load the data:

library(superfundr)

Look at it:

superfunds
#> # A tibble: 66,386 x 20
#>    site_name epa_id city  county state zipcode region npl_status
#>    <chr>     <chr>  <chr> <chr>  <chr> <chr>    <dbl> <chr>     
#>  1 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  2 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  3 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  4 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  5 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  6 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  7 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  8 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#>  9 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#> 10 ATLAS TA… MAD00… FAIR… BRIST… MA    02719        1 Currently…
#> # … with 66,376 more rows, and 12 more variables:
#> #   superfund_agreement <chr>, federal_facility <chr>, op_unit_no <dbl>,
#> #   seq_id <dbl>, decision_type <chr>, completion_date <dttm>,
#> #   fiscal_year <dbl>, media <chr>, contaminant <chr>, address <fct>,
#> #   latitude <dbl>, longitude <dbl>

The data is structured just as it comes from the Environmental Protection Agency, which lists out each contaminant at each site. superfundr adds additional information from the EPA’s basic spreadsheet, including latitude and longitude coordinates and addresses, and converts data as necessary (title case for text, dates as date objects, etc).

The data can be used in a variety of ways. You can count the total number of contaminants across all sites.

superfunds %>% 
  group_by(contaminant) %>% 
  tally(sort = TRUE)
#> # A tibble: 663 x 2
#>    contaminant           n
#>    <chr>             <int>
#>  1 ARSENIC            2667
#>  2 LEAD               2531
#>  3 TRICHLOROETHENE    2049
#>  4 BENZENE            1659
#>  5 TETRACHLOROETHENE  1645
#>  6 CHROMIUM           1589
#>  7 CADMIUM            1538
#>  8 ZINC               1380
#>  9 MANGANESE          1288
#> 10 TOLUENE            1268
#> # … with 653 more rows

You can count the number of active, inactive, and deleted sites.

superfunds %>% 
  distinct(site_name, .keep_all = TRUE) %>% 
  group_by(npl_status) %>% 
  tally(sort = TRUE)
#> # A tibble: 7 x 2
#>   npl_status                     n
#>   <chr>                      <int>
#> 1 Currently on the Final NPL  1141
#> 2 Deleted from the Final NPL   362
#> 3 Not on the NPL                32
#> 4 Proposed for NPL               3
#> 5 Removed from Proposed NPL      2
#> 6 Site is Part of NPL Site       2
#> 7 <NA>                           1

You can also map the locations of sites using Leaflet, which may also lend itself to further spatial analysis using Census or demographic information.

library(leaflet)
library(superfundr)

leaflet(data = superfunds %>% distinct(site_name, .keep_all = T)) %>% 
  addProviderTiles("CartoDB.Positron") %>% 
  addCircleMarkers(radius = 3, stroke = FALSE, fillOpacity = 0.5)

This is an open source project and is open to contributions. There are several ways to get involved:

Open an issue. If you’re running into problems with executing code, consider using the reprex package to create a reproducible example.
Contribute your ideas, resources, and teaching material. Further examples on how to work with or use the dataset is always welcome.
Contributing documentation: Clarifying and expanding on the documentation is always appreciated. superfundr uses roxygen2, which provides documentation at the top of any function definition. Please submit improvements as a pull request.
Contributing code: If you see something that needs improving, data that seems incorrect, or want to include further data enhancements and you’d like to make the changes, contributed code is most welcome.

To get started, take a look at CONTRIBUTING.md.

superfundr is committed to creating and supporting an inclusive community of practice. Please see our Code of Conduct.