Nothing
## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(fig.width = 7, fig.height = 5)
## ----data_model_1-------------------------------------------------------------
library(AirMonitor)
# Recipe to select Washington state monitors in August of 2014:
monitor <-
# 1) start with NW Megafires
NW_Megafires %>%
# 2) filter to only include Washington state
monitor_filter(stateCode == "WA") %>%
# 3) filter to only include August
monitor_filterDate(20150801, 20150901) %>%
# 4) remove monitors with all missing values
monitor_dropEmpty()
# 'mts_monitor' objects can be identified by their class
class(monitor)
# They alwyas have two elements called 'meta' and 'data'
names(monitor)
# Examine the 'meta' dataframe
dim(monitor$meta)
names(monitor$meta)
# Examine the 'data' dataframe
dim(monitor$data)
# This should always be true
identical(names(monitor$data), c('datetime', monitor$meta$deviceDeploymentID))
## ----monitor_leaflet, results = "hold"----------------------------------------
# First, Obtain the monitor ids by clicking on dots in the interactive map:
NW_Megafires %>% monitor_leaflet()
## ----Methow_Valley, results = "hold"------------------------------------------
# Calculate daily means for the Methow Valley from monitors in Twisp and Winthrop
TwispID <- "99a6ee8e126ff8cf_530470009_04"
WinthropID <- "123035bbdc2bc702_530470010_04"
# Recipe to calculate Methow Valley August Means:
Methow_Valley_AugustMeans <-
# 1) start with NW Megafires
NW_Megafires %>%
# 2) select monitors from Twisp and Winthrop
monitor_select(c(TwispID, WinthropID)) %>%
# 3) average them together hour-by-hour
monitor_collapse(deviceID = 'MethowValley') %>%
# 4) restrict data to August
monitor_filterDate(20150801, 20150901) %>%
# 5) calculate daily mean
monitor_dailyStatistic(mean, minHours = 18) %>%
# 6) round data to one decimal place
monitor_mutate(round, 1)
# Look at the first week
Methow_Valley_AugustMeans$data[1:7,]
## ----custom_use1--------------------------------------------------------------
# Monitors within 100 km of Spokane, WA
Spokane <-
NW_Megafires %>%
monitor_filterByDistance(-117.42, 47.70, 100000) %>%
monitor_filterDate(20150801, 20150901) %>%
monitor_dropEmpty()
# Show the daily statistic for one week
Spokane %>%
monitor_filterDate(20150801, 20150808) %>%
monitor_dailyStatistic(mean) %>%
monitor_getData()
# Custom function to convert from metric ug/m3 to imperial grain/gallon
my_FUN <- function(x) { return( x * 15.43236 / 0.004546 ) }
Spokane %>%
monitor_filterDate(20150801, 20150808) %>%
monitor_mutate(my_FUN) %>%
monitor_dailyStatistic(mean) %>%
monitor_getData()
## ----custom_use2--------------------------------------------------------------
# Pull out the time series data to calculate correlations
Spokane_data <-
Spokane %>%
monitor_getData() %>%
dplyr::select(-1) # omit 'datetime' column
# Provide human readable names
names(Spokane_data) <- Spokane$meta$locationName
# Find correlation among monitors
cor(Spokane_data, use = "complete.obs")
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