library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_10179 <- sunrise.set(52.5121401,13.414046, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_10179$sunrise
sunset <- DE_10179$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_10179["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_10179["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_10179["timestamp"] <- align.time(DE_10179$sunrise, 60*10)
DE_10179 <- DE_10179[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_10179, aes(x=DE_10179$timestamp)) + geom_line(aes(y=DE_10179$sr)) + geom_line(aes(y=DE_10179$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_10179 2023", x = "Datum", y = "Zeit")
pdf("DE_10179_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_10179_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_10179["Sonnenaufgang"] <- strftime(DE_10179$sr, format="%H:%M")
DE_10179["Sonnenuntergang"] <- strftime(DE_10179$ss, format="%H:%M")
write.table(DE_10179, file="DE_10179_SaSu.csv", dec=',', sep=';', row.names=FALSE)