library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
3351_SG <- sunrise.set(51.8367842,4.678247, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 3351_SG$sunrise
sunset <- 3351_SG$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
3351_SG["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
3351_SG["ss"] <- as.POSIXct(sunset, format = "%H:%M")
3351_SG["timestamp"] <- align.time(3351_SG$sunrise, 60*10)
3351_SG <- 3351_SG[c("timestamp", "sr", "ss")]
locsrss <- ggplot(3351_SG, aes(x=3351_SG$timestamp)) + geom_line(aes(y=3351_SG$sr)) + geom_line(aes(y=3351_SG$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 3351_SG 2023", x = "Datum", y = "Zeit")
pdf("3351_SG_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="3351_SG_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
3351_SG["Sonnenaufgang"] <- strftime(3351_SG$sr, format="%H:%M")
3351_SG["Sonnenuntergang"] <- strftime(3351_SG$ss, format="%H:%M")
write.table(3351_SG, file="3351_SG_SaSu.csv", dec=',', sep=';', row.names=FALSE)