library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE__10829 <- sunrise.set(52.4803778,13.3597304, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE__10829$sunrise
sunset <- DE__10829$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE__10829["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE__10829["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE__10829["timestamp"] <- align.time(DE__10829$sunrise, 60*10)
DE__10829 <- DE__10829[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE__10829, aes(x=DE__10829$timestamp)) + geom_line(aes(y=DE__10829$sr)) + geom_line(aes(y=DE__10829$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE__10829 2023", x = "Datum", y = "Zeit")
pdf("DE__10829_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE__10829_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE__10829["Sonnenaufgang"] <- strftime(DE__10829$sr, format="%H:%M")
DE__10829["Sonnenuntergang"] <- strftime(DE__10829$ss, format="%H:%M")
write.table(DE__10829, file="DE__10829_SaSu.csv", dec=',', sep=';', row.names=FALSE)