library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) 30890 <- sunrise.set(52.34375350000001,9.480611199999998, "2023/01/01", timezone="MET", num.days=370) sunrise <- 30890$sunrise sunset <- 30890$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") 30890["sr"] <- as.POSIXct(sunrise, format = "%H:%M") 30890["ss"] <- as.POSIXct(sunset, format = "%H:%M") 30890["timestamp"] <- align.time(30890$sunrise, 60*10) 30890 <- 30890[c("timestamp", "sr", "ss")] locsrss <- ggplot(30890, aes(x=30890$timestamp)) + geom_line(aes(y=30890$sr)) + geom_line(aes(y=30890$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 30890 2023", x = "Datum", y = "Zeit") pdf("30890_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="30890_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() 30890["Sonnenaufgang"] <- strftime(30890$sr, format="%H:%M") 30890["Sonnenuntergang"] <- strftime(30890$ss, format="%H:%M") write.table(30890, file="30890_SaSu.csv", dec=',', sep=';', row.names=FALSE)