library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Fo <- sunrise.set(61.892635,-6.911806, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Fo$sunrise
sunset <- Fo$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Fo["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Fo["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Fo["timestamp"] <- align.time(Fo$sunrise, 60*10)
Fo <- Fo[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Fo, aes(x=Fo$timestamp)) + geom_line(aes(y=Fo$sr)) + geom_line(aes(y=Fo$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Fo 2023", x = "Datum", y = "Zeit")
pdf("Fo_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Fo_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Fo["Sonnenaufgang"] <- strftime(Fo$sr, format="%H:%M")
Fo["Sonnenuntergang"] <- strftime(Fo$ss, format="%H:%M")
write.table(Fo, file="Fo_SaSu.csv", dec=',', sep=';', row.names=FALSE)