Introduction to AirSensor2

Jonathan Callahan

2022-05-05

Source: vignettes/AirSensor2.Rmd

Installation

Install the latest version from GitHub with:

devtools::install_github('mazamascience/AirSensor2')

Data Models

Synoptic data (many measurements at a single point in time) are stored as a tibble (modern dataframe) for easy use with dplyr.

Time series data are stored using the MazamaTimeSeries SingleTimeSeries (sts) data model:

Time series data from a single environmental sensor typically consists of multiple parameters measured at successive times. This data is stored in an R list containing two dataframes.

sts$meta – 1 row = unique device-deployment; cols = device/location metadata

sts$data – rows = UTC times; cols = measured parameters (plus an additional datetime column)

API Keys

Many data providers require an API key before you can access their data. When this is the case, AirSensor2 data access functions will have an API key argument.

The package also allows users to specify API keys once per R session using the setAPIKey() function. Each data access function will require it’s own named API key. Specific names for each key are described in the function documentation.

Example usage:

library(AirSensor2)

setAPIKey("PurpleAir-read", "<MY_PurpleAir_READ_KEY>")

WA_pas <-
  pas_createNew(
    countryCodes = "US",
    stateCodes = "WA",
    lookbackDays = 1,
    outsideOnly = TRUE
  )

Function classes

The AirSensor2 packages handles variety of data sources within the world of low-cost sensors with classes of functions tailored to the specifics of each data source. Functions with names of the form <class>_<action>() are designed to perform a particular <action> on a particular <class> of data.

Currently supported classes of data inclucde:

  • pas – PurupleAirSynoptic. Data for many PurpleAir sensors at a specific point in time. Each pas object is a simple tibble.
  • pat – PurupleAirTimeseries. Time series data for a specific PurpleAir sensor. Each pat object is a list with two tibbles: meta and data.

Data pipelines

We encourage people to embrace “data pipeline” style coding as encouraged by dplyr and related packages. The special %>% operator uses the output of one function as the first argument of the next function, thus allowing for easy “chaining” of results. Many of the functions supporting a particular class take an object of that class as their first argument and return an object of that class. As a result, they can be chained together as in:

Colville_Tribes_pas <-
  example_pas %>%
  pas_filter(stateCode == "WA") %>%
  pas_filter(countyName %in% c("Okanogan", "Ferry"))

Best of luck analyzing your local air quality data!