library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_13591 <- sunrise.set(52.5348604,13.1431864, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_13591$sunrise
sunset <- D_13591$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_13591["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_13591["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_13591["timestamp"] <- align.time(D_13591$sunrise, 60*10)
D_13591 <- D_13591[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_13591, aes(x=D_13591$timestamp)) + geom_line(aes(y=D_13591$sr)) + geom_line(aes(y=D_13591$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_13591 2023", x = "Datum", y = "Zeit")
pdf("D_13591_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_13591_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_13591["Sonnenaufgang"] <- strftime(D_13591$sr, format="%H:%M")
D_13591["Sonnenuntergang"] <- strftime(D_13591$ss, format="%H:%M")
write.table(D_13591, file="D_13591_SaSu.csv", dec=',', sep=';', row.names=FALSE)