library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Filippstad <- sunrise.set(59.72615841282299,14.222287050659133, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Filippstad$sunrise
sunset <- Filippstad$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Filippstad["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Filippstad["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Filippstad["timestamp"] <- align.time(Filippstad$sunrise, 60*10)
Filippstad <- Filippstad[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Filippstad, aes(x=Filippstad$timestamp)) + geom_line(aes(y=Filippstad$sr)) + geom_line(aes(y=Filippstad$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Filippstad 2023", x = "Datum", y = "Zeit")
pdf("Filippstad_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Filippstad_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Filippstad["Sonnenaufgang"] <- strftime(Filippstad$sr, format="%H:%M")
Filippstad["Sonnenuntergang"] <- strftime(Filippstad$ss, format="%H:%M")
write.table(Filippstad, file="Filippstad_SaSu.csv", dec=',', sep=';', row.names=FALSE)