library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DEwinterlingen <- sunrise.set(7.62,51.96, "2019/01/01", timezone="MET", num.days=370)
sunrise <- DEwinterlingen$sunrise
sunset <- DEwinterlingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DEwinterlingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DEwinterlingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DEwinterlingen["timestamp"] <- align.time(DEwinterlingen$sunrise, 60*10)
DEwinterlingen <- DEwinterlingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DEwinterlingen, aes(x=DEwinterlingen$timestamp)) + geom_line(aes(y=DEwinterlingen$sr)) + geom_line(aes(y=DEwinterlingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DEwinterlingen 2019", x = "Datum", y = "Zeit")
pdf("DEwinterlingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DEwinterlingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DEwinterlingen["Sonnenaufgang"] <- strftime(DEwinterlingen$sr, format="%H:%M")
DEwinterlingen["Sonnenuntergang"] <- strftime(DEwinterlingen$ss, format="%H:%M")
write.table(DEwinterlingen, file="DEwinterlingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)