library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
66787 <- sunrise.set(49.240760189041836,6.752842619067906, "2024/01/01", timezone="MET", num.days=370)
sunrise <- 66787$sunrise
sunset <- 66787$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
66787["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
66787["ss"] <- as.POSIXct(sunset, format = "%H:%M")
66787["timestamp"] <- align.time(66787$sunrise, 60*10)
66787 <- 66787[c("timestamp", "sr", "ss")]
locsrss <- ggplot(66787, aes(x=66787$timestamp)) + geom_line(aes(y=66787$sr)) + geom_line(aes(y=66787$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 66787 2024", x = "Datum", y = "Zeit")
pdf("66787_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="66787_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
66787["Sonnenaufgang"] <- strftime(66787$sr, format="%H:%M")
66787["Sonnenuntergang"] <- strftime(66787$ss, format="%H:%M")
write.table(66787, file="66787_SaSu.csv", dec=',', sep=';', row.names=FALSE)