library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_12163 <- sunrise.set(52.461448535246404,13.317317962646484, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE_12163$sunrise
sunset <- DE_12163$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_12163["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_12163["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_12163["timestamp"] <- align.time(DE_12163$sunrise, 60*10)
DE_12163 <- DE_12163[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_12163, aes(x=DE_12163$timestamp)) + geom_line(aes(y=DE_12163$sr)) + geom_line(aes(y=DE_12163$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_12163 2023", x = "Datum", y = "Zeit")
pdf("DE_12163_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_12163_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_12163["Sonnenaufgang"] <- strftime(DE_12163$sr, format="%H:%M")
DE_12163["Sonnenuntergang"] <- strftime(DE_12163$ss, format="%H:%M")
write.table(DE_12163, file="DE_12163_SaSu.csv", dec=',', sep=';', row.names=FALSE)