library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
90522_de <- sunrise.set(49.4325206,10.9711906, "2024/01/01", timezone="MET", num.days=370)
sunrise <- 90522_de$sunrise
sunset <- 90522_de$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
90522_de["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
90522_de["ss"] <- as.POSIXct(sunset, format = "%H:%M")
90522_de["timestamp"] <- align.time(90522_de$sunrise, 60*10)
90522_de <- 90522_de[c("timestamp", "sr", "ss")]
locsrss <- ggplot(90522_de, aes(x=90522_de$timestamp)) + geom_line(aes(y=90522_de$sr)) + geom_line(aes(y=90522_de$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 90522_de 2024", x = "Datum", y = "Zeit")
pdf("90522_de_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="90522_de_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
90522_de["Sonnenaufgang"] <- strftime(90522_de$sr, format="%H:%M")
90522_de["Sonnenuntergang"] <- strftime(90522_de$ss, format="%H:%M")
write.table(90522_de, file="90522_de_SaSu.csv", dec=',', sep=';', row.names=FALSE)