library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ethn <- sunrise.set(49.39063549999999,9.96131420000006, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Ethn$sunrise
sunset <- Ethn$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ethn["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ethn["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ethn["timestamp"] <- align.time(Ethn$sunrise, 60*10)
Ethn <- Ethn[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ethn, aes(x=Ethn$timestamp)) + geom_line(aes(y=Ethn$sr)) + geom_line(aes(y=Ethn$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ethn 2024", x = "Datum", y = "Zeit")
pdf("Ethn_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ethn_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ethn["Sonnenaufgang"] <- strftime(Ethn$sr, format="%H:%M")
Ethn["Sonnenuntergang"] <- strftime(Ethn$ss, format="%H:%M")
write.table(Ethn, file="Ethn_SaSu.csv", dec=',', sep=';', row.names=FALSE)