library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) �� <- sunrise.set(67.88056139999999,12.982643300000063, "2023/01/01", timezone="MET", num.days=370) sunrise <- ��$sunrise sunset <- ��$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") ��["sr"] <- as.POSIXct(sunrise, format = "%H:%M") ��["ss"] <- as.POSIXct(sunset, format = "%H:%M") ��["timestamp"] <- align.time(��$sunrise, 60*10) �� <- ��[c("timestamp", "sr", "ss")] locsrss <- ggplot(��, aes(x=��$timestamp)) + geom_line(aes(y=��$sr)) + geom_line(aes(y=��$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - �� 2023", x = "Datum", y = "Zeit") pdf("��_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="��_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() ��["Sonnenaufgang"] <- strftime(��$sr, format="%H:%M") ��["Sonnenuntergang"] <- strftime(��$ss, format="%H:%M") write.table(��, file="��_SaSu.csv", dec=',', sep=';', row.names=FALSE)