library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_19246 <- sunrise.set(53.5616031,10.9470514, "2024/01/01", timezone="MET", num.days=370)
sunrise <- DE_19246$sunrise
sunset <- DE_19246$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_19246["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_19246["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_19246["timestamp"] <- align.time(DE_19246$sunrise, 60*10)
DE_19246 <- DE_19246[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_19246, aes(x=DE_19246$timestamp)) + geom_line(aes(y=DE_19246$sr)) + geom_line(aes(y=DE_19246$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_19246 2024", x = "Datum", y = "Zeit")
pdf("DE_19246_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_19246_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_19246["Sonnenaufgang"] <- strftime(DE_19246$sr, format="%H:%M")
DE_19246["Sonnenuntergang"] <- strftime(DE_19246$ss, format="%H:%M")
write.table(DE_19246, file="DE_19246_SaSu.csv", dec=',', sep=';', row.names=FALSE)