library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
21769 <- sunrise.set(53.6335533.9,9.1242729, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 21769$sunrise
sunset <- 21769$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
21769["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
21769["ss"] <- as.POSIXct(sunset, format = "%H:%M")
21769["timestamp"] <- align.time(21769$sunrise, 60*10)
21769 <- 21769[c("timestamp", "sr", "ss")]
locsrss <- ggplot(21769, aes(x=21769$timestamp)) + geom_line(aes(y=21769$sr)) + geom_line(aes(y=21769$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 21769 2023", x = "Datum", y = "Zeit")
pdf("21769_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="21769_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
21769["Sonnenaufgang"] <- strftime(21769$sr, format="%H:%M")
21769["Sonnenuntergang"] <- strftime(21769$ss, format="%H:%M")
write.table(21769, file="21769_SaSu.csv", dec=',', sep=';', row.names=FALSE)