library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_01609 <- sunrise.set(51.419549726700566,13.407096862792968, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_01609$sunrise
sunset <- DE_01609$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_01609["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_01609["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_01609["timestamp"] <- align.time(DE_01609$sunrise, 60*10)
DE_01609 <- DE_01609[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_01609, aes(x=DE_01609$timestamp)) + geom_line(aes(y=DE_01609$sr)) + geom_line(aes(y=DE_01609$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_01609 2023", x = "Datum", y = "Zeit")
pdf("DE_01609_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_01609_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_01609["Sonnenaufgang"] <- strftime(DE_01609$sr, format="%H:%M")
DE_01609["Sonnenuntergang"] <- strftime(DE_01609$ss, format="%H:%M")
write.table(DE_01609, file="DE_01609_SaSu.csv", dec=',', sep=';', row.names=FALSE)