library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) Arlanda <- sunrise.set(59.6497622,17.923780699999952, "2022/01/01", timezone="MET", num.days=370) sunrise <- Arlanda$sunrise sunset <- Arlanda$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") Arlanda["sr"] <- as.POSIXct(sunrise, format = "%H:%M") Arlanda["ss"] <- as.POSIXct(sunset, format = "%H:%M") Arlanda["timestamp"] <- align.time(Arlanda$sunrise, 60*10) Arlanda <- Arlanda[c("timestamp", "sr", "ss")] locsrss <- ggplot(Arlanda, aes(x=Arlanda$timestamp)) + geom_line(aes(y=Arlanda$sr)) + geom_line(aes(y=Arlanda$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Arlanda 2022", x = "Datum", y = "Zeit") pdf("Arlanda_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="Arlanda_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() Arlanda["Sonnenaufgang"] <- strftime(Arlanda$sr, format="%H:%M") Arlanda["Sonnenuntergang"] <- strftime(Arlanda$ss, format="%H:%M") write.table(Arlanda, file="Arlanda_SaSu.csv", dec=',', sep=';', row.names=FALSE)