library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) B��sum <- sunrise.set(54.1273322,8.8588936, "2023/01/01", timezone="MET", num.days=370) sunrise <- B��sum$sunrise sunset <- B��sum$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") B��sum["sr"] <- as.POSIXct(sunrise, format = "%H:%M") B��sum["ss"] <- as.POSIXct(sunset, format = "%H:%M") B��sum["timestamp"] <- align.time(B��sum$sunrise, 60*10) B��sum <- B��sum[c("timestamp", "sr", "ss")] locsrss <- ggplot(B��sum, aes(x=B��sum$timestamp)) + geom_line(aes(y=B��sum$sr)) + geom_line(aes(y=B��sum$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - B��sum 2023", x = "Datum", y = "Zeit") pdf("B��sum_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="B��sum_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() B��sum["Sonnenaufgang"] <- strftime(B��sum$sr, format="%H:%M") B��sum["Sonnenuntergang"] <- strftime(B��sum$ss, format="%H:%M") write.table(B��sum, file="B��sum_SaSu.csv", dec=',', sep=';', row.names=FALSE)