AirVizR

The goal of AirVizR is to act as a tool for extracting, wrangling, correcting, and visualizing spatio-temporal atmospheric data, with an emphasis on fine particulate matter (PM2.5, i.e. Particulate Matter with a diameter <2.5 µm) data from PurpleAir.
Correction factor equations from the Environmental Protection Agency (EPA) and Lane Regional Air Protection Agency (LRAPA) are implemented to allow for greatest data accuracy.
Visualization functions have been carefully selected to maximize accessibility and data transparency.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("gmcginnis/AirVizR", build_vignettes = TRUE)

# Load the library
library(AirVizR)

Wrangling options:

Many of the functions in this package are intended to work hand-in-hand with a series of provided inputs, hence why many of the defaults inputs for man class types are “input_*” or “raw_*”. For full lists of inputs, browse the vignettes.

One of three methods of data importation are documented in the vignettes, via either API or local import;
* API (PurpleAir only): Using a series of functions, some derived from the AirSensor package, archived PurpleAir data can be pulled if provided coordinates and date ranges of interest. See the api-data vignette for more information. * Local data

• PurpleAir: Many of the same arguments as the API setup can be used. You can download data from PurpleAir’s sensor download tool. It should be noted that this data does not include as much information as API data, including meta information regarding high values or A/B monitor sensor disparity. See the local-data vignette for more information.
• FRM: Although currently no functions are explicitly dedicated to loading FRM data, the visualization functions can be applied to them. The examples in this package use FRM data from Oregon’s DEQ. See the frm-data vignette for more information.

The data preparation functions are primarily configured for using PurpleAir data sets, however other types can be wrangled in similar methods. All spatio-temporal atmospheric data (STAD) sets can be visualized using the functions in this package, so long as the following structures are followed:
* A data frame of STAD with at least one column for a time stamp of any unit, one column for the atmospheric unit of interest, and one identifying column (site_id).
* A data frame of meta/location data for each site_id in the STAD, including latitudes, longitudes, and location (inside/outside/FRM).

Exaple Visualizations:

Map Spatio-Temporal Data

To represent both the spatial and temporal aspects of atmospheric data, maps can be used to visualize multiple monitors.
Visual options, such as point size and background graphics, can also be customized.

Map Temporal Data

Functions allow for the use of data from ObservAir®, which is an air monitor that records changes over time as well as location. Below is an example of temperature changes during a commute.
In RStudio and HTML, this map is interactive!

Heatmaps

Temporal heat maps use color to represent changes in a variable of interest over time. Typically, single “cells” (individual observations) of data are colored to the variable.
The heatmap options in this package are designed for continuous numeric variables.

Single monitors

To show hourly changes over time for a single monitor, data can be visualized with the date on the x-axis, and hour of the day on the y-axis. By default, cell data will also be supplemented with a text label (which is color-customizable).

Multiple monitors

To compare multiple monitors in a set over time (of any temporal resolution), data can be mapped to a heat map where the time is on the x-axis, and the y-axis is monitor labels. Monitors will be separated by location (i.e. inside/outside), and arranged north to south, allowing for a general representation of spatial differences.
This visualization option is ideal for larger data sets with many monitors. Optionally, monitors with incomplete data can be filtered out.
Below, an example is shown with the defaults, followed by one with only complete monitors included and a “color cap” applied to avoid a washed out color palette. This color “cap” can be applied to all visualization types with continuous colors in this package.

Timeseries line graphs

Time series visualizations are useful for representing changes over time.

Spatio-temporal

The following is a visualization option intended to spotlight specific monitors in a data set. Optional arguments allow for points to be added, color caps (as above) to be applied, and show/hide maximum & minimum values for each monitor. Additionally, data can be visualized in a single “column” of results if desired, allowing for easier temporal cross-comparisons.

Variation

A visualization option that builds upon the timeVariation() function from the OpenAir package is also available.
It can be modified to compare multiple groups (such as date ranges) or multiple pollutants.

ts_variation(july_api_hourly, "pm25_epa_2021", include = "Lighthouse", group = "date_tag",
    location_data = july_api_meta, subset = "hour")

Combine STADs

STADs can be combined and pivoted in order to visualize data from both FRMs and PurpleAir monitors simultaneously. The visualization functions above can be used, or custom ones can be applied: