Introduction to the RAQSAPI package

Clinton Mccrowey, physical scientist - US EPA

• EPA Disclaimer
• Introduction
• About the timeliness of AQS Data
• Installing RAQSAPI
  • Option 1: Installing the stable version from CRAN
  • Option 2: Installing the development version of RAQSAPI
• Using The RAQSAPI library
  • Load RAQSAPI
  • Sign up and setting up user credentials with the RAQSAPI library
  • (suggested) Use the keyring package to manage credentials
  • Data Mart aggregate functions
    • See the RAQSAPI vignette for more details
• Acknowledgements
• References

EPA Disclaimer

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.

Introduction

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 Hypertext Transfer Protocol (HTTP) so there is no need to install external ODBC drivers, configure ODBC connections or deal with the security vulnerabilities associated with them. Most 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]

About the timeliness of AQS Data

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.

Installing RAQSAPI

Either install the stable version from CRAN or install the latest development version from GitHub.

Option 1: Installing the stable version from CRAN

install.packages(pkgs="RAQSAPI", dependencies = TRUE )

Option 2: Installing the development version of RAQSAPI

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 
                        )

Using The RAQSAPI library

Load RAQSAPI

after successfully installing the RAQSAPI package, load the RAQSAPI library:

library(RAQSAPI)

Sign up and setting up user credentials with the RAQSAPI library

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.

(suggested) Use the keyring package to manage 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]

Data Mart aggregate functions

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_dailydata_by_*)
• Annual Summary Data (aqs_annualdata_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_annualpeferomanceeval_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”.

See the RAQSAPI vignette for more details

(RAQSAPI must be installed and built with BUILD_MANUAL = TRUE enabled)

RShowDoc(what=“RAQSAPIvignette,” type=“html,” package=“RAQSAPI”)

Acknowledgements

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 badgecreator⁴.
• The R package usethis⁵ was used to generate GitHub actions for Continuous integration (CI).
• Code cleanup was assisted by the R package lintr⁶
• the function install.packages are provided by the R package utils⁷
• the function install_github are provided by the R package remotes⁸
• .gitignore file borrowed examples from https://github.com/github/gitignore/blob/master/R.gitignore
• The RAQSAPI hexsticker was created using the hexSticker⁹ package for R

References

(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) Hogervorst, R. M. Badgecreatr: Create Badges for ’Travis’, ’Repostatus’ ’Codecov.io’ Etc in Github Readme; 2019.

(5) Wickham, H.; Bryan, J.; Barrett, M. Usethis: Automate Package and Project Setup; 2021.

(6) Hester, J.; Angly, F.; Hyde, R. Lintr: A ’Linter’ for r Code; 2020.

(7) Team, R. C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2019.

(8) Csárdi, G.; Hester, J.; Wickham, H.; Chang, W.; Morgan, M.; Tenenbaum, D. Remotes: R Package Installation from Remote Repositories, Including ’GitHub’; 2021.

(9) Yu, G. hexSticker: Create Hexagon Sticker in r; 2020.

[1] 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.

[2] Use “?aqs_sign_up” after the RAQSAPI library has been loaded to see the full usage description of the aqs_sign_up function.

[3] Use “?aqs_credentials” after the RAQSAPI library has been loaded to see the full usage description of the aqs_credentials function.

[4] [R Keyring package]https://cran.r-project.org/package=keyring)

[5] See (https://aqs.epa.gov/aqsweb/documents/data_api.html) for the full details of the Data Mart API