---
title: "Introduction to PWFSLSmoke"
author: "Mazama Science"
date: "4/3/2019"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to PWFSLSmoke}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      fig.width=7,
                      fig.height=5)
```

## Background

The USFS Pacific Wildland Fire Sciences Lab [AirFire](https://www.airfire.org) 
team works to model wildland fire emissions and has created the BlueSky Modeling 
Framework. This system  integrates a wide collection of models along a smoke 
modeling pipeline (fire  information, fuel loadings, consumption modeling, 
emissions modeling, time rate of  emissions modeling, plume height estimations, 
and smoke trajectory and dispersion  modeling). The resulting model output has 
been integrated into many different smoke  prediction systems and scientific 
modeling efforts.

The **PWFSLSmoke** R package is being developed for PWFSL to help modelers,
scientists and air quality professionals more easily work with PM2.5 data from 
monitoring locations across North America.

The package makes it easier to obtain data, perform analyses and generate 
reports. It includes functionality to:

 * download and easily work with regulatory PM2.5 data from the EPA and AirNow
 * download and quality control raw monitoring data from AIRSIS and WRCC
 * convert between UTC and local timezones
 * apply various algorithms to the data 
 (nowcast, rolling means, aggregation, etc.)
 * provide interactive timeseries and maps through RStudio’s Viewer pane
 * create a variety of publication ready maps and timeseries plots

This introduction will help you get set up and walk you through a few simple
analyses.

## Installation

### Installing from CRAN

This package is designed to be used with [R](https://cran.r-project.org) 
(>= 3.3) and [RStudio](https://www.rstudio.com) so make sure you have those 
installed first.

The following packages should be installed by typing the following at the 
RStudio console:

```
install.packages('MazamaSpatialUtils')
install.packages('PWFSLSmoke')
```

### Installing from Github

Users will want to install the **devtools** package to have access to the latest 
version of the package from Github. Building vignettes will require **knitr**
and **rmarkdown**:

```
# Note that vignettes require knitr and rmarkdown
install.packages('knitr')
install.packages('rmarkdown')
install.packages('MazamaSpatialUtils')
devtools::install_github('MazamaScience/PWFSLSmoke', build_vignettes=TRUE)
```

### Installing spatial data

Any work with spatial data, *e.g.* assigning countries, states and timezones 
while ingesting raw data, will require installation of required spatial 
datasets. To get these datasets  you should type the following at the RStudio 
console:

```
library(MazamaSpatialUtils)
dir.create('~/Data/Spatial', recursive=TRUE)
setSpatialDataDir('~/Data/Spatial')
installSpatialData()
```

## Function names

The package has a lot of functions but most of them fall into a few specific
categories identified by the function name prefix:

* `airnow_` -- functions for processing AirNow data
* `airsis_` -- functions for processing raw AIRSIS data
* `epa_` -- functions for processing EPA data
* `monitor_` -- functions for working with *ws_monitor* objects
* `raw_` -- functions for processing raw, "engineering" data from AIRSIS and
WRCC
* `wrcc_` -- functions for processing raw WRCC data

For most people, only the `monitor_` category is important as it allows you to
work with pre-generated *ws_monitor* objects as described in the "Data Model"
vignette.

A *ws_monitor* object contains both data and metadata for a collection of 
monitors. It has a very compact data model and allows you to work
with data from many monitors at once.

## Available data

The USFS AirFire group regularly processes monitoring data in support of their
[Monitoring Website](https://tools.airfire.org/monitoring/v4). Pre-processed data
files can be loaded from their servers by the following functions:

* `~_load()` -- load data based on a start- and end-time
* `~loadAnnual()` -- load a year's worth of data
* `~loadDaily()` -- load the most recent 45 days of data (updated once per day)
* `~loadLatest()` -- load the most recent 10 days of data (updated every hour)

Data archives go back over 10 years depending on the source of data.

## Data pipelines

We encourage people to embrace "data pipeline" style coding as encouraged by
**dplyr** and related packages. The special `%>%` operator sends the output
of one function to the input of the next function, thus allowing for easy 
"chaining" of results.

With only a few exceptions, all the `monitor_` functions accept a *ws_monitor* 
object as their first argument and generate a *ws_monitor* object as a result so 
they can be chained together.

## A first example

```{r library, echo = FALSE}
suppressPackageStartupMessages({
  library(PWFSLSmoke)
})
```

Let's say we are interested in the impact of smoke from the 2018 
[Camp Fire](https://en.wikipedia.org/wiki/Camp_Fire_(2018)) on the Sacramento
area.

We would begin by creating a `camp_fire` *ws_monitor* object that has all the
monitors in California for the period of interest:

```{r Sacramento_1}
camp_fire <-
  monitor_loadAnnual(2018) %>%
  monitor_subset(stateCodes = 'CA') %>%
  monitor_subset(tlim = c(20181108,20181123))
```

We can display these monitors (colored by maximum PM2.5 value) in an interactive
map, zoom in and click on the monitor in downtown Sacramento to get it's 
`monitorID`:

```{r Sacramento_2}
monitor_leaflet(camp_fire)
```

We can use this `monitorID` to create a *ws_monitor* object for this single 
monitor and take a look at a timeseries plot:

```{r Sacramento_3}
Sacramento <-
  camp_fire %>%
  monitor_subset(monitorIDs = '060670010_01')

monitor_timeseriesPlot(
  Sacramento,
  style='aqidots',
  pch=16,
  xlab="2018"
)
addAQIStackedBar()
addAQILines()
addAQILegend(cex=0.8)
title("Sacramento Smoke")
```

Next, we can use this specific location to create a *ws_monitor* object
containing all monitors within 50km of Sacramento:

```{r Sacramento_4}
Sacramento_area <-
  camp_fire %>%
  monitor_subsetByDistance(
    longitude = Sacramento$meta$longitude,
    latitude = Sacramento$meta$latitude,
    radius = 50
  )

monitor_leaflet(Sacramento_area)
```

We can display the hourly data for all the monitors and add day/night shading:

```{r Sacramento_5}
monitor_timeseriesPlot(Sacramento_area, 
                       style='gnats',
                       shadedNight = TRUE)
```

We can average together all the monitors and create a local-time daily average
for the Sacramento area:

```{r Sacramento_6}
Sacramento_area %>%
  monitor_collapse() %>%
  monitor_dailyStatistic() %>%
  monitor_extractData()
```

Alternatively, we can plot the daily averages:

```{r Sacramento_7}
Sacramento_area %>%
  monitor_collapse() %>%
  monitor_dailyBarplot(labels_x_nudge = 0.5,
                       labels_y_nudge = 15)
```

Hopefully, this short introduction will encourage you to start using this 
package for your own analyses.

