library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
88605 <- sunrise.set(47.98527,9.098644, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 88605$sunrise
sunset <- 88605$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
88605["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
88605["ss"] <- as.POSIXct(sunset, format = "%H:%M")
88605["timestamp"] <- align.time(88605$sunrise, 60*10)
88605 <- 88605[c("timestamp", "sr", "ss")]
locsrss <- ggplot(88605, aes(x=88605$timestamp)) + geom_line(aes(y=88605$sr)) + geom_line(aes(y=88605$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 88605 2023", x = "Datum", y = "Zeit")
pdf("88605_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="88605_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
88605["Sonnenaufgang"] <- strftime(88605$sr, format="%H:%M")
88605["Sonnenuntergang"] <- strftime(88605$ss, format="%H:%M")
write.table(88605, file="88605_SaSu.csv", dec=',', sep=';', row.names=FALSE)