library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) D_94474 <- sunrise.set(48.62906539772418,13.18584680557251, "2023/01/01", timezone="MET", num.days=370) sunrise <- D_94474$sunrise sunset <- D_94474$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") D_94474["sr"] <- as.POSIXct(sunrise, format = "%H:%M") D_94474["ss"] <- as.POSIXct(sunset, format = "%H:%M") D_94474["timestamp"] <- align.time(D_94474$sunrise, 60*10) D_94474 <- D_94474[c("timestamp", "sr", "ss")] locsrss <- ggplot(D_94474, aes(x=D_94474$timestamp)) + geom_line(aes(y=D_94474$sr)) + geom_line(aes(y=D_94474$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_94474 2023", x = "Datum", y = "Zeit") pdf("D_94474_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="D_94474_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() D_94474["Sonnenaufgang"] <- strftime(D_94474$sr, format="%H:%M") D_94474["Sonnenuntergang"] <- strftime(D_94474$ss, format="%H:%M") write.table(D_94474, file="D_94474_SaSu.csv", dec=',', sep=';', row.names=FALSE)