library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Frasdorf <- sunrise.set(47.80271459999999,12.28550180000002, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Frasdorf$sunrise
sunset <- Frasdorf$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Frasdorf["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Frasdorf["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Frasdorf["timestamp"] <- align.time(Frasdorf$sunrise, 60*10)
Frasdorf <- Frasdorf[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Frasdorf, aes(x=Frasdorf$timestamp)) + geom_line(aes(y=Frasdorf$sr)) + geom_line(aes(y=Frasdorf$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Frasdorf 2023", x = "Datum", y = "Zeit")
pdf("Frasdorf_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Frasdorf_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Frasdorf["Sonnenaufgang"] <- strftime(Frasdorf$sr, format="%H:%M")
Frasdorf["Sonnenuntergang"] <- strftime(Frasdorf$ss, format="%H:%M")
write.table(Frasdorf, file="Frasdorf_SaSu.csv", dec=',', sep=';', row.names=FALSE)