library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Falkenhain <- sunrise.set(51.3961281,12.873097899999948, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Falkenhain$sunrise
sunset <- Falkenhain$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Falkenhain["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Falkenhain["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Falkenhain["timestamp"] <- align.time(Falkenhain$sunrise, 60*10)
Falkenhain <- Falkenhain[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Falkenhain, aes(x=Falkenhain$timestamp)) + geom_line(aes(y=Falkenhain$sr)) + geom_line(aes(y=Falkenhain$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Falkenhain 2023", x = "Datum", y = "Zeit")
pdf("Falkenhain_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Falkenhain_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Falkenhain["Sonnenaufgang"] <- strftime(Falkenhain$sr, format="%H:%M")
Falkenhain["Sonnenuntergang"] <- strftime(Falkenhain$ss, format="%H:%M")
write.table(Falkenhain, file="Falkenhain_SaSu.csv", dec=',', sep=';', row.names=FALSE)