library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) Degenfeld <- sunrise.set(48.725995,9.878862000000026, "2023/01/01", timezone="MET", num.days=370) sunrise <- Degenfeld$sunrise sunset <- Degenfeld$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") Degenfeld["sr"] <- as.POSIXct(sunrise, format = "%H:%M") Degenfeld["ss"] <- as.POSIXct(sunset, format = "%H:%M") Degenfeld["timestamp"] <- align.time(Degenfeld$sunrise, 60*10) Degenfeld <- Degenfeld[c("timestamp", "sr", "ss")] locsrss <- ggplot(Degenfeld, aes(x=Degenfeld$timestamp)) + geom_line(aes(y=Degenfeld$sr)) + geom_line(aes(y=Degenfeld$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Degenfeld 2023", x = "Datum", y = "Zeit") pdf("Degenfeld_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="Degenfeld_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() Degenfeld["Sonnenaufgang"] <- strftime(Degenfeld$sr, format="%H:%M") Degenfeld["Sonnenuntergang"] <- strftime(Degenfeld$ss, format="%H:%M") write.table(Degenfeld, file="Degenfeld_SaSu.csv", dec=',', sep=';', row.names=FALSE)