library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_03172 <- sunrise.set(51.98054536958894,14.702882766723632, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_03172$sunrise
sunset <- DE_03172$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_03172["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_03172["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_03172["timestamp"] <- align.time(DE_03172$sunrise, 60*10)
DE_03172 <- DE_03172[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_03172, aes(x=DE_03172$timestamp)) + geom_line(aes(y=DE_03172$sr)) + geom_line(aes(y=DE_03172$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_03172 2023", x = "Datum", y = "Zeit")
pdf("DE_03172_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_03172_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_03172["Sonnenaufgang"] <- strftime(DE_03172$sr, format="%H:%M")
DE_03172["Sonnenuntergang"] <- strftime(DE_03172$ss, format="%H:%M")
write.table(DE_03172, file="DE_03172_SaSu.csv", dec=',', sep=';', row.names=FALSE)