library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) 50859_k��ln <- sunrise.set(50.9534818,6.835814199999959, "2022/01/01", timezone="MET", num.days=370) sunrise <- 50859_k��ln$sunrise sunset <- 50859_k��ln$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") 50859_k��ln["sr"] <- as.POSIXct(sunrise, format = "%H:%M") 50859_k��ln["ss"] <- as.POSIXct(sunset, format = "%H:%M") 50859_k��ln["timestamp"] <- align.time(50859_k��ln$sunrise, 60*10) 50859_k��ln <- 50859_k��ln[c("timestamp", "sr", "ss")] locsrss <- ggplot(50859_k��ln, aes(x=50859_k��ln$timestamp)) + geom_line(aes(y=50859_k��ln$sr)) + geom_line(aes(y=50859_k��ln$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 50859_k��ln 2022", x = "Datum", y = "Zeit") pdf("50859_k��ln_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="50859_k��ln_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() 50859_k��ln["Sonnenaufgang"] <- strftime(50859_k��ln$sr, format="%H:%M") 50859_k��ln["Sonnenuntergang"] <- strftime(50859_k��ln$ss, format="%H:%M") write.table(50859_k��ln, file="50859_k��ln_SaSu.csv", dec=',', sep=';', row.names=FALSE)