library(lubridate) library(ggplot2) library(StreamMetabolism) library(xts) library(reshape) library(scales) B��blingen <- sunrise.set(27.6648274,-81.51575350000002, "2022/01/01", timezone="MET", num.days=370) sunrise <- B��blingen$sunrise sunset <- B��blingen$sunset sunrise <- strftime(sunrise, format="%R", tz="MET") sunset <- strftime(sunset, format="%R", tz="MET") B��blingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M") B��blingen["ss"] <- as.POSIXct(sunset, format = "%H:%M") B��blingen["timestamp"] <- align.time(B��blingen$sunrise, 60*10) B��blingen <- B��blingen[c("timestamp", "sr", "ss")] locsrss <- ggplot(B��blingen, aes(x=B��blingen$timestamp)) + geom_line(aes(y=B��blingen$sr)) + geom_line(aes(y=B��blingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - B��blingen 2022", x = "Datum", y = "Zeit") pdf("B��blingen_SA_SU.pdf", paper="a4r", width=11) locsrss dev.off() png(filename="B��blingen_SA_SU.png", width = 1400, height = 800, units = "px") locsrss dev.off() B��blingen["Sonnenaufgang"] <- strftime(B��blingen$sr, format="%H:%M") B��blingen["Sonnenuntergang"] <- strftime(B��blingen$ss, format="%H:%M") write.table(B��blingen, file="B��blingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)