library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_18249 <- sunrise.set(53.7827022647503,11.843433380126953, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_18249$sunrise
sunset <- DE_18249$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_18249["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_18249["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_18249["timestamp"] <- align.time(DE_18249$sunrise, 60*10)
DE_18249 <- DE_18249[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_18249, aes(x=DE_18249$timestamp)) + geom_line(aes(y=DE_18249$sr)) + geom_line(aes(y=DE_18249$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_18249 2023", x = "Datum", y = "Zeit")
pdf("DE_18249_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_18249_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_18249["Sonnenaufgang"] <- strftime(DE_18249$sr, format="%H:%M")
DE_18249["Sonnenuntergang"] <- strftime(DE_18249$ss, format="%H:%M")
write.table(DE_18249, file="DE_18249_SaSu.csv", dec=',', sep=';', row.names=FALSE)