Nothing
## ---- echo=FALSE--------------------------------------------------------------
knitr::opts_chunk$set(fig.width = 7, fig.height = 5)
## -----------------------------------------------------------------------------
suppressPackageStartupMessages(library(PWFSLSmoke))
# Get some airnow data for Washington state in the summer of 2015
# NOTE: 'tlim' is interpreted as UTC unless we specify 'timezone'
N_M <- monitor_subset(Northwest_Megafires,
tlim = c(20150801, 20150831),
timezone = "America/Los_Angeles")
WA <- monitor_subset(N_M, stateCodes = 'WA')
# 'ws_monitor' objects can be identified by their class
class(WA)
# Examine the 'meta' dataframe
dim(WA$meta)
rownames(WA$meta)
colnames(WA$meta)
# Examine the 'data' dataframe
dim(WA$data)
colnames(WA$data)
# This should always be true
all(rownames(WA$meta) == colnames(WA$data[,-1]))
## ----magrittr-----------------------------------------------------------------
# Calculate daily means for the Methow Valley from monitors in Twisp and Winthrop
TwispID <- '530470009_01'
WinthropID <- '530470010_01'
Methow_Valley_JulyMeans <-
Northwest_Megafires %>%
monitor_subset(monitorIDs = c(TwispID,WinthropID)) %>%
monitor_collapse(monitorID = 'MethowValley') %>%
monitor_subset(tlim=c(20150701, 20150731), timezone = 'America/Los_Angeles') %>%
monitor_dailyStatistic(minHours = 18)
# Look at the first week
Methow_Valley_JulyMeans$data[1:7,]
## -----------------------------------------------------------------------------
# Use special knowledge of AirNow IDs to subset airnow data for Spokane county monitors
SpokaneCountyIDs <- N_M$meta$monitorID[stringr::str_detect(N_M$meta$monitorID, "^53063")]
Spokane <- monitor_subset(N_M, monitorIDs = SpokaneCountyIDs)
# Apply 3-hr rolling mean
Spokane_3hr <- monitor_rollingMean(Spokane, 3, align = "center")
# 1) Replace data columns with their squares (exponentiation is not supplied by the package)
Spokane_3hr_squared <- Spokane_3hr
Spokane_3hr_squared$data[,-1] <- (Spokane_3hr$data[,-1])^2 # exclude the 'datetime' column
# NOTE: Exponentiation is only used as an example. It does not generate a meaningful result.
# Create a daily averaged 'ws_monitor' object
Spokane_daily_3hr <- monitor_dailyStatistic(Spokane_3hr)
# 2) Check out the correlation between monitors (correlation is not supplied by the package)
data <- Spokane_daily_3hr$data[,-1] # exclude the 'datetime' column
cor(data, use = 'complete.obs')
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