library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) Dakar <- sunrise.set(14.7645042,-17.366028599999936, "2022/01/01", timezone="MET", num.days=370) sunrise <- Dakar$sunrise sunset <- Dakar$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") Dakar["sr"] <- as.POSIXct(sunrise, format = "%H:%M") Dakar["ss"] <- as.POSIXct(sunset, format = "%H:%M") Dakar["timestamp"] <- align.time(Dakar$sunrise, 60*10) Dakar <- Dakar[c("timestamp", "sr", "ss")] locsrss <- ggplot(Dakar, aes(x=Dakar$timestamp)) + geom_line(aes(y=Dakar$sr)) + geom_line(aes(y=Dakar$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Dakar 2022", x = "Datum", y = "Zeit") pdf("Dakar_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="Dakar_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() Dakar["Sonnenaufgang"] <- strftime(Dakar$sr, format="%H:%M") Dakar["Sonnenuntergang"] <- strftime(Dakar$ss, format="%H:%M") write.table(Dakar, file="Dakar_SaSu.csv", dec=',', sep=';', row.names=FALSE)