library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Egmating <- sunrise.set(48.0046489,11.79704270000002, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Egmating$sunrise
sunset <- Egmating$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Egmating["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Egmating["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Egmating["timestamp"] <- align.time(Egmating$sunrise, 60*10)
Egmating <- Egmating[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Egmating, aes(x=Egmating$timestamp)) + geom_line(aes(y=Egmating$sr)) + geom_line(aes(y=Egmating$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Egmating 2024", x = "Datum", y = "Zeit")
pdf("Egmating_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Egmating_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Egmating["Sonnenaufgang"] <- strftime(Egmating$sr, format="%H:%M")
Egmating["Sonnenuntergang"] <- strftime(Egmating$ss, format="%H:%M")
write.table(Egmating, file="Egmating_SaSu.csv", dec=',', sep=';', row.names=FALSE)