library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
BE_8900 <- sunrise.set(50.819818262156545,2.889404296875, "2023/01/01", timezone="MET", num.days=370)
sunrise <- BE_8900$sunrise
sunset <- BE_8900$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
BE_8900["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
BE_8900["ss"] <- as.POSIXct(sunset, format = "%H:%M")
BE_8900["timestamp"] <- align.time(BE_8900$sunrise, 60*10)
BE_8900 <- BE_8900[c("timestamp", "sr", "ss")]
locsrss <- ggplot(BE_8900, aes(x=BE_8900$timestamp)) + geom_line(aes(y=BE_8900$sr)) + geom_line(aes(y=BE_8900$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - BE_8900 2023", x = "Datum", y = "Zeit")
pdf("BE_8900_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="BE_8900_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
BE_8900["Sonnenaufgang"] <- strftime(BE_8900$sr, format="%H:%M")
BE_8900["Sonnenuntergang"] <- strftime(BE_8900$ss, format="%H:%M")
write.table(BE_8900, file="BE_8900_SaSu.csv", dec=',', sep=';', row.names=FALSE)