library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bautzen <- sunrise.set(51.18229299999999,14.423774900000012, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Bautzen$sunrise
sunset <- Bautzen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bautzen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bautzen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bautzen["timestamp"] <- align.time(Bautzen$sunrise, 60*10)
Bautzen <- Bautzen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bautzen, aes(x=Bautzen$timestamp)) + geom_line(aes(y=Bautzen$sr)) + geom_line(aes(y=Bautzen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bautzen 2023", x = "Datum", y = "Zeit")
pdf("Bautzen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bautzen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bautzen["Sonnenaufgang"] <- strftime(Bautzen$sr, format="%H:%M")
Bautzen["Sonnenuntergang"] <- strftime(Bautzen$ss, format="%H:%M")
write.table(Bautzen, file="Bautzen_SaSu.csv", dec=',', sep=';', row.names=FALSE)