library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) Falkenhain <- sunrise.set(51.3961281,12.873097899999948, "2023/01/01", timezone="MET", num.days=370) sunrise <- Falkenhain$sunrise sunset <- Falkenhain$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") Falkenhain["sr"] <- as.POSIXct(sunrise, format = "%H:%M") Falkenhain["ss"] <- as.POSIXct(sunset, format = "%H:%M") Falkenhain["timestamp"] <- align.time(Falkenhain$sunrise, 60*10) Falkenhain <- Falkenhain[c("timestamp", "sr", "ss")] locsrss <- ggplot(Falkenhain, aes(x=Falkenhain$timestamp)) + geom_line(aes(y=Falkenhain$sr)) + geom_line(aes(y=Falkenhain$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Falkenhain 2023", x = "Datum", y = "Zeit") pdf("Falkenhain_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="Falkenhain_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() Falkenhain["Sonnenaufgang"] <- strftime(Falkenhain$sr, format="%H:%M") Falkenhain["Sonnenuntergang"] <- strftime(Falkenhain$ss, format="%H:%M") write.table(Falkenhain, file="Falkenhain_SaSu.csv", dec=',', sep=';', row.names=FALSE)