Awesome

AirMonitorIngest

A suite of utility functions for ingesting hourly air quality monitoring data. 
Data from various sources are harmonized to use standardized variable names and 
a compact data model with spatial metadata stored in a 'meta' dataframe while 
hourly data are stored in a separate 'data' dataframe.

Background

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. In our experience, every new data source or data format requires dedicated code to bring it into a standardized system. Because new data sources appear every few months, putting all of the data ingest code into a separate package allows downstream packages that work with harmonized data to be much more stable.

Installation

Install from CRAN with:

install.packages('AirMonitorIngest')

Install the latest version from GitHub with:

devtools::install_github('mazamascience/AirMonitorIngest')

Features

Although each source of data is unique, a similar set of functions is used to obtain, parse and harmonize the data. The basic suite of functions associated with any source of data will include:

• <source>_openWebPages() -- Opens web pages relevant to the source data.
• <source>_get~() -- Gets tables of non-data information.
• <source>_downloadData() -- Download ASCII data.
• <source>_parseData() -- Parse ASCII data.
• <source>_createData() -- Create the data dataframe.
• <source>_createMeta() -- Create the meta dataframe.

This project is supported by the USFS AirFire group.