library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_Hamburg <- sunrise.set(53.5665641,9.984619499999999, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_Hamburg$sunrise
sunset <- D_Hamburg$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_Hamburg["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_Hamburg["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_Hamburg["timestamp"] <- align.time(D_Hamburg$sunrise, 60*10)
D_Hamburg <- D_Hamburg[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_Hamburg, aes(x=D_Hamburg$timestamp)) + geom_line(aes(y=D_Hamburg$sr)) + geom_line(aes(y=D_Hamburg$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_Hamburg 2023", x = "Datum", y = "Zeit")
pdf("D_Hamburg_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_Hamburg_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_Hamburg["Sonnenaufgang"] <- strftime(D_Hamburg$sr, format="%H:%M")
D_Hamburg["Sonnenuntergang"] <- strftime(D_Hamburg$ss, format="%H:%M")
write.table(D_Hamburg, file="D_Hamburg_SaSu.csv", dec=',', sep=';', row.names=FALSE)