library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_18586 <- sunrise.set(54.30811135287019,13.68072509765625, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_18586$sunrise
sunset <- DE_18586$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_18586["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_18586["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_18586["timestamp"] <- align.time(DE_18586$sunrise, 60*10)
DE_18586 <- DE_18586[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_18586, aes(x=DE_18586$timestamp)) + geom_line(aes(y=DE_18586$sr)) + geom_line(aes(y=DE_18586$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_18586 2023", x = "Datum", y = "Zeit")
pdf("DE_18586_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_18586_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_18586["Sonnenaufgang"] <- strftime(DE_18586$sr, format="%H:%M")
DE_18586["Sonnenuntergang"] <- strftime(DE_18586$ss, format="%H:%M")
write.table(DE_18586, file="DE_18586_SaSu.csv", dec=',', sep=';', row.names=FALSE)