library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_16303 <- sunrise.set(53.131735996846665,14.281282424926757, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_16303$sunrise
sunset <- DE_16303$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_16303["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_16303["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_16303["timestamp"] <- align.time(DE_16303$sunrise, 60*10)
DE_16303 <- DE_16303[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_16303, aes(x=DE_16303$timestamp)) + geom_line(aes(y=DE_16303$sr)) + geom_line(aes(y=DE_16303$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_16303 2023", x = "Datum", y = "Zeit")
pdf("DE_16303_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_16303_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_16303["Sonnenaufgang"] <- strftime(DE_16303$sr, format="%H:%M")
DE_16303["Sonnenuntergang"] <- strftime(DE_16303$ss, format="%H:%M")
write.table(DE_16303, file="DE_16303_SaSu.csv", dec=',', sep=';', row.names=FALSE)