library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_Laufeld <- sunrise.set(50.07684864922647,6.863408088684082, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_Laufeld$sunrise
sunset <- DE_Laufeld$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_Laufeld["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_Laufeld["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_Laufeld["timestamp"] <- align.time(DE_Laufeld$sunrise, 60*10)
DE_Laufeld <- DE_Laufeld[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_Laufeld, aes(x=DE_Laufeld$timestamp)) + geom_line(aes(y=DE_Laufeld$sr)) + geom_line(aes(y=DE_Laufeld$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_Laufeld 2023", x = "Datum", y = "Zeit")
pdf("DE_Laufeld_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_Laufeld_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_Laufeld["Sonnenaufgang"] <- strftime(DE_Laufeld$sr, format="%H:%M")
DE_Laufeld["Sonnenuntergang"] <- strftime(DE_Laufeld$ss, format="%H:%M")
write.table(DE_Laufeld, file="DE_Laufeld_SaSu.csv", dec=',', sep=';', row.names=FALSE)