library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
90592 <- sunrise.set(49.3400523,11.2694136, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 90592$sunrise
sunset <- 90592$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
90592["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
90592["ss"] <- as.POSIXct(sunset, format = "%H:%M")
90592["timestamp"] <- align.time(90592$sunrise, 60*10)
90592 <- 90592[c("timestamp", "sr", "ss")]
locsrss <- ggplot(90592, aes(x=90592$timestamp)) + geom_line(aes(y=90592$sr)) + geom_line(aes(y=90592$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 90592 2023", x = "Datum", y = "Zeit")
pdf("90592_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="90592_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
90592["Sonnenaufgang"] <- strftime(90592$sr, format="%H:%M")
90592["Sonnenuntergang"] <- strftime(90592$ss, format="%H:%M")
write.table(90592, file="90592_SaSu.csv", dec=',', sep=';', row.names=FALSE)