library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Bad_cadzand <- sunrise.set(51.3732708,3.3806895999999824, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Bad_cadzand$sunrise
sunset <- Bad_cadzand$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Bad_cadzand["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Bad_cadzand["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Bad_cadzand["timestamp"] <- align.time(Bad_cadzand$sunrise, 60*10)
Bad_cadzand <- Bad_cadzand[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Bad_cadzand, aes(x=Bad_cadzand$timestamp)) + geom_line(aes(y=Bad_cadzand$sr)) + geom_line(aes(y=Bad_cadzand$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Bad_cadzand 2023", x = "Datum", y = "Zeit")
pdf("Bad_cadzand_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Bad_cadzand_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Bad_cadzand["Sonnenaufgang"] <- strftime(Bad_cadzand$sr, format="%H:%M")
Bad_cadzand["Sonnenuntergang"] <- strftime(Bad_cadzand$ss, format="%H:%M")
write.table(Bad_cadzand, file="Bad_cadzand_SaSu.csv", dec=',', sep=';', row.names=FALSE)