library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) de_45329 <- sunrise.set(51.5108223,7.014328299999988, "2023/01/01", timezone="MET", num.days=370) sunrise <- de_45329$sunrise sunset <- de_45329$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") de_45329["sr"] <- as.POSIXct(sunrise, format = "%H:%M") de_45329["ss"] <- as.POSIXct(sunset, format = "%H:%M") de_45329["timestamp"] <- align.time(de_45329$sunrise, 60*10) de_45329 <- de_45329[c("timestamp", "sr", "ss")] locsrss <- ggplot(de_45329, aes(x=de_45329$timestamp)) + geom_line(aes(y=de_45329$sr)) + geom_line(aes(y=de_45329$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - de_45329 2023", x = "Datum", y = "Zeit") pdf("de_45329_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="de_45329_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() de_45329["Sonnenaufgang"] <- strftime(de_45329$sr, format="%H:%M") de_45329["Sonnenuntergang"] <- strftime(de_45329$ss, format="%H:%M") write.table(de_45329, file="de_45329_SaSu.csv", dec=',', sep=';', row.names=FALSE)