A dedicated Slack channel has been created for announcements, support and to help build a community of practice around this open source package. You may request an invitation to join from jonathan.callahan@dri.com.
Utilities for working with hourly air quality monitoring data
with a focus on small particulates (PM2.5). A compact data model is
structured as a list with two dataframes. A 'meta' dataframe contains
spatial and measuring device metadata associated with deployments at known
locations. A 'data' dataframe contains a 'datetime' column followed by
columns of measurements associated with each "device-deployment".The USFS AirFire group is focused on air quality measurements associated with wildfire smoke and maintains both historical and real-time databases of PM2.5 monitoring data obtained from stationary monitors. This data is used in operational displays and for retrospective analysis. Data ingest and management of air quality “stationary time series” are both important ongoing activities.
The AirMonitorIngest package is used to create data archives for the AirMonitor package and isolates the work of meticulously cleaning, validating and harmonizing data from various sources.
The AirMonitor package contains data access functions to easily download harmonized data files as well as data manipulation functions that make it easy to create “recipe style” analysis pipelines. The combination allows analysts to work efficiently with short, readable R scripts. Interactive and base R plotting functions allow for visual review of the data.
The AirMonitorPlots package contains ggplot2 based plotting functions for advanced plots.
NOTE: This package has not yet been uploaded to CRAN
Install the latest version from GitHub with:
devtools::install_github('mazamascience/AirMonitor')
The AirMonitor package uses the mts data model defined in MazamaTimeSeries.
In this data model, each unique time series is referred to as a “device-deployment” – a timeseries collected by a particular device at a specific location. Multiple device-deployments are stored in memory as a monitor object – an R list with two dataframes:
monitor$meta – rows = unique device-deployments; cols =
device/location metadata
monitor$data – rows = UTC times; cols =
device-deployments (plus an additional datetime column)
A key feature of this data model is the use of the
deviceDeploymentID as a “foreign key” that allows
data columns to be mapped onto the associated spatial and
device metadata in a meta row. The following will always be
true:
identical(names(monitor$data), c('datetime', monitor$meta$deviceDeploymentID))Each column of monitor$data represents a timeseries
associated with a particular device-deployment while each row represents
a synoptic snapshot of all measurements made at a particular
time.
In this manner, software can create both timeseries plots and maps
from a single monitor object in memory.
Note: The monitor object time
axis specified in data$datetime is guaranteed to be a
regular hourly axis with no gaps.
This project is supported by the USFS AirFire group.