library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_16866 <- sunrise.set(52.93436201715625,12.403564453125, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_16866$sunrise
sunset <- DE_16866$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_16866["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_16866["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_16866["timestamp"] <- align.time(DE_16866$sunrise, 60*10)
DE_16866 <- DE_16866[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_16866, aes(x=DE_16866$timestamp)) + geom_line(aes(y=DE_16866$sr)) + geom_line(aes(y=DE_16866$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_16866 2023", x = "Datum", y = "Zeit")
pdf("DE_16866_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_16866_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_16866["Sonnenaufgang"] <- strftime(DE_16866$sr, format="%H:%M")
DE_16866["Sonnenuntergang"] <- strftime(DE_16866$ss, format="%H:%M")
write.table(DE_16866, file="DE_16866_SaSu.csv", dec=',', sep=';', row.names=FALSE)