library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Brunsbek <- sunrise.set(53.59999999999999,10.266666699999973, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Brunsbek$sunrise
sunset <- Brunsbek$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Brunsbek["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Brunsbek["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Brunsbek["timestamp"] <- align.time(Brunsbek$sunrise, 60*10)
Brunsbek <- Brunsbek[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Brunsbek, aes(x=Brunsbek$timestamp)) + geom_line(aes(y=Brunsbek$sr)) + geom_line(aes(y=Brunsbek$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Brunsbek 2023", x = "Datum", y = "Zeit")
pdf("Brunsbek_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Brunsbek_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Brunsbek["Sonnenaufgang"] <- strftime(Brunsbek$sr, format="%H:%M")
Brunsbek["Sonnenuntergang"] <- strftime(Brunsbek$ss, format="%H:%M")
write.table(Brunsbek, file="Brunsbek_SaSu.csv", dec=',', sep=';', row.names=FALSE)