library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
90491 <- sunrise.set(49.4254092,11.079655300000013, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 90491$sunrise
sunset <- 90491$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
90491["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
90491["ss"] <- as.POSIXct(sunset, format = "%H:%M")
90491["timestamp"] <- align.time(90491$sunrise, 60*10)
90491 <- 90491[c("timestamp", "sr", "ss")]
locsrss <- ggplot(90491, aes(x=90491$timestamp)) + geom_line(aes(y=90491$sr)) + geom_line(aes(y=90491$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 90491 2023", x = "Datum", y = "Zeit")
pdf("90491_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="90491_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
90491["Sonnenaufgang"] <- strftime(90491$sr, format="%H:%M")
90491["Sonnenuntergang"] <- strftime(90491$ss, format="%H:%M")
write.table(90491, file="90491_SaSu.csv", dec=',', sep=';', row.names=FALSE)