library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Marl <- sunrise.set(51.68664224100596,7.117394366223152, "2019/01/01", timezone="MET", num.days=370)
sunrise <- Marl$sunrise
sunset <- Marl$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Marl["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Marl["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Marl["timestamp"] <- align.time(Marl$sunrise, 60*10)
Marl <- Marl[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Marl, aes(x=Marl$timestamp)) + geom_line(aes(y=Marl$sr)) + geom_line(aes(y=Marl$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Marl 2019", x = "Datum", y = "Zeit")
pdf("Marl_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Marl_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Marl["Sonnenaufgang"] <- strftime(Marl$sr, format="%H:%M")
Marl["Sonnenuntergang"] <- strftime(Marl$ss, format="%H:%M")
write.table(Marl, file="Marl_SaSu.csv", dec=',', sep=';', row.names=FALSE)