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Resources","sub_category":"Air Quality","monthly_downloads":842,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/AirMonitor)](https://CRAN.R-project.org/package=AirMonitor)\n[![Downloads](https://cranlogs.r-pkg.org/badges/AirMonitor)](https://cran.r-project.org/package=AirMonitor)\n[![DOI](https://zenodo.org/badge/414793919.svg)](https://zenodo.org/badge/latestdoi/414793919)\n\u003c!-- badges: end --\u003e\n\nA 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.\n\n# AirMonitor\n\n```\nUtilities for working with hourly air quality monitoring data\nwith a focus on small particulates (PM2.5). A compact data model is \nstructured as a list with two dataframes. A 'meta' dataframe contains \nspatial and measuring device metadata associated with deployments at known \nlocations. A 'data' dataframe contains a 'datetime' column followed by \ncolumns of measurements associated with each \"device-deployment\".\n```\n\n## Background\n\nThe USFS AirFire group works with air quality measurements associated with \nwildfire smoke and maintains both historical and real-time databases of PM2.5 \nmonitoring data obtained from stationary monitors. This data is used in \noperational displays and for retrospective analysis. Data ingest and management \nof air quality “stationary time series” are important ongoing activities.\n\n## Related Packages\n\nThe **AirMonitor** package contains data access functions to easily download \nharmonized data files as well as data manipulation functions that\nmake it easy to create \"recipe style\" analysis pipelines. This combination allows\nanalysts to work efficiently with short, readable R scripts. Interactive and\nbase R plotting functions allow for visual review of the data.\n\nThe **[AirMonitorPlots](https://github.com/mazamascience/AirMonitorPlots)**\npackage contains **ggplot2** based plotting functions for advanced plots.\n\n## Installation\n\nInstall from CRAN with:\n\n`install.packages('AirMonitor')`\n\nInstall the latest version from GitHub with:\n\n`devtools::install_github('mazamascience/AirMonitor')`\n\n## Data Model\n\nThe **AirMonitor** package uses the _mts_ data model defined in \n**[MazamaTimeSeries](https://mazamascience.github.io/MazamaTimeSeries/)**.\n\nIn this data model, each unique time series is referred to as a \n_\"device-deployment\"_ -- a timeseries collected by a particular device at a \nspecific location. Multiple device-deployments are stored in memory as a\n_monitor_ object -- an R list with two dataframes:\n\n`monitor$meta` -- rows = unique device-deployments; cols = device/location metadata\n\n`monitor$data` -- rows = UTC times; cols = device-deployments (plus an additional `datetime` column)\n\nA key feature of this data model is the use of the `deviceDeploymentID` as a\n\"foreign key\" that allows `data` columns to be mapped onto the associated\nspatial and device metadata in a `meta` row. The following will always be true:\n\n```\nidentical(names(monitor$data), c('datetime', monitor$meta$deviceDeploymentID))\n```\n\nEach column of `monitor$data` represents a timeseries associated with a particular\nID while each row of `monitor$data` represents a _synoptic_ snapshot of all\nmeasurements made at a particular time. \n\nIn this manner, both timeseries plots and maps can be created from a single\n`monitor` object in memory.\n\n_**Note:**_ The `monitor` object time axis specified in `data$datetime` is \nguaranteed to be a regular hourly axis with no gaps.\n\n\n------------------------------------------------------------------------\n\nThis project is supported by the [USFS AirFire](https://www.airfire.org) team.\n\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/159948","html_url":"https://ost.ecosyste.ms/projects/159948"}