# gather and clean data
weather1 <- read.csv("https://www.wunderground.com/history/airport/KISP/2012/2/16/CustomHistory.html?dayend=8&monthend=7&yearend=2012&req_city=NA&req_state=NA&req_statename=NA&format=1")
weather2 <- read.csv("https://www.wunderground.com/history/airport/KNHK/2012/7/11/CustomHistory.html?dayend=29&monthend=7&yearend=2013&req_city=NA&req_state=NA&req_statename=NA&format=1")
weather3 <- read.csv("https://www.wunderground.com/history/airport/KISP/2013/7/30/CustomHistory.html?dayend=7&monthend=8&yearend=2013&req_city=NA&req_state=NA&req_statename=NA&format=1")
weather4 <- read.csv("https://www.wunderground.com/history/airport/KNHK/2013/8/8/CustomHistory.html?dayend=17&monthend=9&yearend=2013&req_city=NA&req_state=NA&req_statename=NA&format=1")

weather1$PrecipitationIn <- as.numeric(as.character(weather1$PrecipitationIn)) # delete weird "T" factor; ???
# mood/productivity (MP) sourced from personal log
weather1$MP <- c(1,3,4,4,4,4,3,4,2,3,4,4,2,3,2,3,3,2,3,2,3,4,3,2,3,2,3,1,2,4,3,3,3,3,3,
                 3,3,2,3,4,3,3,4,4,4,4,2,3,4,3,3,3,4,3,3,1,3,2,3,3,4,3,2,3,2,4,4,4,3,3,
                 4,4,3,4,3,3,2,3,3,2,2,4,3,2,4,4,4,3,3,2,4,4,4,3,3,3,3,2,3,4,3,2,2,4,3,
                 3,2,2,2,2,3,4,2,4,4,3,3,3,2,4,2,2,2,2,2,3,3,4,2,1,3,3,2,3,3,4,2,3,2,3,
                 3,2,4,4)
weather2$MP <- c(2,4,4,4,3,4,3,3,4,3,2,3,4,4,4,3,2,3,3,2,3,3,3,2,2,2,2,2,3,4,3,4,2,4,3,
                 3,2,2,2,3,3,3,3,4,3,3,3,4,3,3,3,2,4,2,3,3,4,4,3,3,3,4,3,3,4,3,4,2,3,3,
                 4,4,3,3,4,4,3,4,3,2,3,3,3,4,3,2,3,2,2,2,3,3,3,4,4,3,4,4,3,3,2,3,3,3,3,
                 4,4,3,4,2,2,2,3,4,3,4,3,4,3,4,4,3,3,2,3,2,4,4,3,4,2,3,4,2,3,3,2,2,2,3,
                 2,3,3,4,2,3,4,3,4,3,3,2,2,3,4,4,3,4,2,2,3,2,3,2,2,2,4,3,3,4,2,2,3,3,3,
                 4,4,3,2,3,2,2,2,3,3,3,4,3,4,3,3,3,2,2,3,3,3,4,4,3,2,2,2,3,3,4,3,4,3,4,
                 3,2,4,4,3,3,2,4,3,3,3,2,3,3,2,3,2,3,3,3,3,2,3,3,3,3,3,3,4,2,3,2,4,3,3,
                 2,2,3,3,3,2,4,3,3,3,3,4,4,3,3,3,3,2,3,2,2,3,3,2,3,2,3,2,3,3,2,2,3,3,3,
                 4,4,3,2,2,3,2,3,2,4,4,4,3,3,3,4,3,4,3,4,4,2,2,2,4,4,3,2,2,3,4,3,2,4,2,
                 3,4,2,4,4,2,3,2,3,3,3,2,2,2,3,3,2,2,3,4,4,2,3,2,2,3,4,4,3,2,2,3,4,4,3,
                 3,3,3,2,4,3,3,4,3,3,3,4,3,4,4,4,4,3,3,3,2,2,2,4,3,4,4,2,2,3,4,4,3,2)
weather3$MP <- c(3,4,2,3,4,3,3,4,3)
weather4$MP <- c(3,3,2,2,3,2,4,4,4,3,2,2,5,4,3,4,4,3,3,4,4,3,2,2,3,4,3,3,4,4,3,4,3,3,
                 4,3,4,4,4,2,3)

# compute length of day for each location
library(insol)
weather1$DayLength <- daylength(40,73, julian(as.Date(as.character(weather1$EDT))), 1)[,3]
weather3$DayLength <- daylength(40,73, julian(as.Date(as.character(weather3$EDT))), 1)[,3]
weather2$DayLength <- daylength(38,76, julian(as.Date(as.character(weather2$EDT))), 1)[,3]
weather4$DayLength <- daylength(38,76, julian(as.Date(as.character(weather4$EDT))), 1)[,3]
# combine & start cleaning
weather <- rbind(weather1,weather2)
weather$WindDirDegrees.br... <- sub("<br>", "", weather$WindDirDegrees.br...)
weather$WindDirDegrees.br... <- as.integer(weather$WindDirDegrees.br...)
weather$Events <- as.integer(weather$Events)
weather$Events[weather$Events>1] <- 2
# something of a hack but we'll impute any missing rain precipitation values as 0
weather[is.na(weather)] <- 0