Clinton Mccrowey, physical scientist - US EPA
- EPA Disclaimer
- Introduction
- About the timeliness of AQS Data
- Installing RAQSAPI
- Using The RAQSAPI library
- Acknowledgements
- References
This software/application was developed by the U.S. Environmental Protection Agency (USEPA). No warranty expressed or implied is made regarding the accuracy or utility of the system, nor shall the act of distribution constitute any such warranty. The USEPA has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality or availability of the information. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the USEPA. The USEPA seal and logo shall not be used in any manner to imply endorsement of any commercial product or activity by the USEPA or the United States Government.
| Warning: US EPA’s AQS Data Mart API V2 is
currently in beta phase of development, the API interface has not been finalized. This means that certain functionality of the API may change or be removed without notice. As a result, this package is also currently marked as beta and may also change to reflect any changes made to the Data Mart API or in respect to improvements in the design, functionality, quality and documentation of this package. The authors assume no liability for any problems that may occur as a result of using this package, the Data Mart service, any software, service, hardware, or user accounts that may utilize this package. |
|---|
The RAQSAPI package for the R programming environment allows a R
programming environment to connect to and retrieve data from the United
States Environmental Protection Agency’s (US EPA) Air Quality System
(AQS) Data Mart API v2 Air Quality System1 interface
directly. This package enables the data user to omit legacy challenges
including coercing data from a JSON object to a usable R object,
retrieving multiple years of data, formatting API requests, retrieving
results, handling credentials, requesting multiple pollutant data and
rate limiting data requests. All the basic functionality of the API have
been implemented that are available from the AQS API Data Mart server.
The library connects to AQS Data Mart API via Secure Hypertext Transfer
Protocol (HTTPS) so there is no need to install external ODBC drivers,
configure ODBC connections or deal with the security vulnerabilities
associated with them. Most API functions have a parameter,
return_header, which by default is set to FALSE. If the user decides to
set return_header to TRUE, then that function will return a R
AQS_DATAMART_APIv2 S3 object which is a two item named list.
The first item, ($Header) in the AQS_DATAMART_APIv2 object is a
tibble2 which contains the header information. The Header
contains status information regarding the request (success/fail), any
applicable error messages returned from the API, if any exist, the URL
used in the request, a date and time stamp noting when request was
received and other useful information. The second item of the
AQS_DATAMART_APIv2 object ($Data) is a tibble which contains the actual
data being requested. For functions with the return_header option set to
FALSE (default) a simple tibble is returned with just the $Data portion
of the request. After each call to the API a five second stall is
invoked to help prevent overloading the Data Mart API server and to
serve as a simple rate limit. 1
EPA’s AQS Datamart API, the service that RAQSAPI retrieves data from, does not host real time (collected now/today) data. If real time data is needed, please use the AirNow API and direct all questions toward real time data there. RAQSAPI does not work with AirNow and cannot retrieve real time data. For more details see section 7.1 of the About AQS Data page3.
Either install the stable version from CRAN or install the latest development version from GitHub.
install.packages(pkgs="RAQSAPI", dependencies = TRUE )To install the development version of RAQSAPI, first if not already
installed, install the remotes package and its dependencies. Then run
the following in a R environment.
remotes::install_github(repo = "USEPA/raqsapi",
dependencies = TRUE,
upgrade = "always",
build = TRUE,
#optional, set TRUE if the manual is desired,
#requires pandoc
build_manual = FALSE,
build_vignettes = TRUE
)after successfully installing the RAQSAPI package, load the RAQSAPI
library:
library(RAQSAPI)If you have not already done so you will need to sign up with AQS Data Mart using aqs_sign_up function, 2 this function takes one input, “email”, which is a R character object, that represents the email address that you want to use as a user credential to the AQS Data Mart service. After a successful call to aqs_sign_up an email message will be sent to the email address provided with a new Data Mart key which will be used as a credential key to access the Data Mart API. The aqs_sign_up function can also be used to regenerate a new key for an existing user, to generate a new key simply call the aqs_sign_up function with the parameter “email” set to an existing account. A new key will be e-mailed to the account given.
The credentials used to access the Data Mart API service are stored in a
R environment variable that needs to be set every time the RAQSAPI
library is attached or the key is changed. Without valid credentials,
the Data Mart server will reject any request sent to it. The key used
with Data Mart is a key and is not a password, so the RAQSAPI library
does not treat the key as a password; this means that the key is stored
in plain text and there are no attempts to encrypt Data Mart credentials
as would be done for a username and password combination. The key that
is supplied to use with Data Mart is not intended for authentication but
only account monitoring. Each time RAQSAPI is loaded and before using
any of it’s functions use the aqs_credentials 3 function to enter in
the user credentials so that RAQSAPI can access the AQS Data Mart
server.
| Note: The credentials used to access AQS
Data Mart API is not the same as the credentials used to access AQS. AQS users who do not have access to the AQS Data Mart will need to create new credentials. |
|---|
It is highly suggested that users use a keyring manager to store and
retrieve their credentials while using RAQSAPI. One such credential
manager is provided by the keyring package 4. The Keyring package
uses the credential manager available for most popular operating systems
to store and manage user credentials. This will help avoid hard coding
credential information into R scripts.
To use the keyring package with RAQSAPI first install keyring:
install.package("keyring")Ensure that your system is supported by the keyring package before
proceeding.
keyring::has_keyring_support()then set the keyring used to access AQS Data Mart (make sure to replace the text in the angled brackets with your specific user information):
library("keyring")
keyring::key_set(service = "AQSDatamart",
username = "\<user email account\>")a popup window will appear for the user to input their keyring
information. Enter the AQS Data mart credential key associated with the
AQS user name provided, then hit enter. Now the AQS Data Mart user
credential is set using keyring.
To retrieve the keyring to use with RAQSAPI load the keyring package
and use the function key_get to return the user credential to RAQSAPI:
library(RAQSAPI)
library(keyring)
datamartAPI_user <- \<user email account\>
server <- "AQSDatamart"then pass these variables to the aqs_credentials function when using RAQSAPI:
aqs_credentials(username = datamartAPI_user,
key = key_get(service = server,
username = datamartAPI_user
)
)To change the keyring stored with the keyring package repeat the steps
above to call the keyring::key_set function again with the new
credential information.
To retrieve a list of all keyrings managed with the keyring package
use the function: > keyring::key_list()
Refer the thekeyring package
documentation
for an in depth explanation on using the keyring package.
| Information: AQS Data Mart API restricts
the maximum amount of monitoring data to one full year of data per API call. |
|---|
RAQSAPI functions are named according to the service and filter variables that are available by the Data Mart API.5
These functions retrieve aggregated data from the Data Mart API and are grouped by how each function aggregates the data. There are 7 different families of related aggregate functions in which the AQS Data Mart API groups data.
These seven families are:
- _by_site
- _by_county
- _by_state
- _by_<latitude/longitude bounding box> (_by_box)
- _by_<monitoring agency> (_by_MA)
- _by_<Primary Quality Assurance Organization> (_by_pqao)
- _by_<core based statistical area (as defined by the
US census Bureau)> (_by_cbsa).
Within these families of aggregated data functions there are functions that call on the 13 different aggregate services that the Data Mart API provides. Note that not all aggregations are available for each service.
These fourteen services are:
- Monitors (aqs_monitors_by_*)
- Sample Data (aqs_sampledata_by_*)
- Daily Summary Data (aqs_dailysummary_by_*)
- Annual Summary Data (aqs_annualsummary_by_*)
- Quarterly Summary Data (aqs_quarterlysummary_by_*)
- Quality Assurance - Blanks Data (aqs_qa_blanks_by_*)
- Quality Assurance - Collocated Assessments (aqs_qa_collocated_assessments_by_*)
- Quality Assurance - Flow Rate Verifications (aqs_qa_flowrateverification_by_*)
- Quality Assurance - Flow Rate Audits (aqs_qa_flowrateaudit_by_*)
- Quality Assurance - One Point Quality Control Raw Data (aqs_qa_one_point_qc_by_*)
- Quality Assurance - PEP Audits (aqs_qa_pep_audit_by_*)
- Transaction Sample - AQS Submission data in transaction Format (RD) (aqs_transactionsample_by_*)
- Quality Assurance - Annual Performance Evaluations
(aqs_qa_annualperformanceeval_by_*) - Quality Assurance - Annual Performance Evaluations in the AQS
Submission transaction format (RD) (aqs_qa_annualperformanceevaltransaction_by_*)
| Information: AQS Data Mart API restricts
the maximum amount of monitoring data to one full year of data per API call. These functions are able to return multiple years of data by making repeated calls to the API. Each call to the Data Mart API will take time to complete. The more years of data being requested the longer RAQSAPI will take to return the results. |
|---|
Aggregate functions are named AQS_API<service>_<aggregation>() where <service> is one of the 13 services listed above and <aggregation> is either “_by_site”, “_by_county”, “_by_state”, “_by_box”, “_by_cbsa”, “_by_ma”, or “_by_pqao”.
(RAQSAPI must be installed and built with BUILD_MANUAL = TRUE enabled)
RShowDoc(what="RAQSAPIvignette", type="html", package="RAQSAPI")For users that feel more comfortable working within a python 3 environment, pyaqsapi4, a port of RAQSAPI to the python 3 language has been released. Both projects aim to maintain feature parity with the other and there are no inherent advantages to using either project over the other, except for the ability of working within the programming language environment of choice. The API of both packages are very structured similarly, both packages export the same data, use the same credentials and data source to retrieve data.
RAQSAPI was included in the Rblogger’s March 2021: “Top 40” New CRAN Packages.
The RAQSAPI package borrows upon functions and code provided by sources not mentioned in the DESCRIPTION file. Here we attempt to acknowledge those sources with them RAQSAPI would not be possible.
- README badges are provided by R package
badgecreator5. - The R package
usethis6 was used to generate GitHub actions for Continuous integration (CI). - Code cleanup was assisted by the R package
lintr7 - the function install.packages are provided by the R package
utils8 - the function install_github are provided by the R package
remotes9 - .gitignore file borrowed examples from https://github.com/github/gitignore/blob/master/R.gitignore
- . CITATION.cff file was generated by the R package
cffr10 - R package
urlchecker11 was used to check urls in RAQSAPI documentation
(1) AQS data mart welcome. https://aqs.epa.gov/aqsweb/documents/data_mart_welcome.html.
(2) Müller, K.; Wickham, H. Tibble: Simple Data Frames; 2021.
(3) About AQS data. https://aqs.epa.gov/aqsweb/documents/about_aqs_data.html.
(4) Mccrowey, C. A Python 3 Package to Retrieve Ambient Air Monitoring Data from the United States Environmental Protection Agency’s (US EPA) Air Quality System (AQS) Data Mart API V2 Interface, 2022. https://github.com/USEPA/pyaqsapi.
(5) Hogervorst, R. M. Badgecreatr: Create Badges for ’Travis’, ’Repostatus’ ’Codecov.io’ Etc in Github Readme; 2019.
(6) Wickham, H.; Bryan, J.; Barrett, M. Usethis: Automate Package and Project Setup; 2021.
(7) Hester, J.; Angly, F.; Hyde, R. Lintr: A ’Linter’ for r Code; 2020.
(8) Team, R. C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019.
(9) Csárdi, G.; Hester, J.; Wickham, H.; Chang, W.; Morgan, M.; Tenenbaum, D. Remotes: R Package Installation from Remote Repositories, Including ’GitHub’; 2021.
(10) Druskat, S.; Spaaks, J. H.; Chue Hong, N.; Haines, R.; Baker, J.; Bliven, S.; Willighagen, E.; Pérez-Suárez, D.; Konovalov, A. Citation File Format, 2021. https://doi.org/10.5281/zenodo.5171937.
(11) R-Lib/Urlchecker on Github. https://github.com/r-lib/urlchecker (accessed 2023-11-30).
Footnotes
-
RAQSAPI’s rate limit does not guarantee that the user will not go over the rate limit and does not guarantee that API calls do not overload the AQS Data Mart system, each user should monitor their requests independently. ↩
-
Use “?aqs_sign_up” after the RAQSAPI library has been loaded to see the full usage description of the aqs_sign_up function. ↩
-
Use “?aqs_credentials” after the RAQSAPI library has been loaded to see the full usage description of the aqs_credentials function. ↩
-
[R
Keyringpackage]https://cran.r-project.org/package=keyring) ↩ -
See (https://aqs.epa.gov/aqsweb/documents/data_api.html) for the full details of the Data Mart API ↩
