library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Arlanda <- sunrise.set(59.6497622,17.923780699999952, "2022/01/01", timezone="MET", num.days=370)
sunrise <- Arlanda$sunrise
sunset <- Arlanda$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Arlanda["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Arlanda["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Arlanda["timestamp"] <- align.time(Arlanda$sunrise, 60*10)
Arlanda <- Arlanda[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Arlanda, aes(x=Arlanda$timestamp)) + geom_line(aes(y=Arlanda$sr)) + geom_line(aes(y=Arlanda$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Arlanda 2022", x = "Datum", y = "Zeit")
pdf("Arlanda_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Arlanda_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Arlanda["Sonnenaufgang"] <- strftime(Arlanda$sr, format="%H:%M")
Arlanda["Sonnenuntergang"] <- strftime(Arlanda$ss, format="%H:%M")
write.table(Arlanda, file="Arlanda_SaSu.csv", dec=',', sep=';', row.names=FALSE)