library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ch_8500 <- sunrise.set(47.5615194,8.9003382, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Ch_8500$sunrise
sunset <- Ch_8500$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ch_8500["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ch_8500["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ch_8500["timestamp"] <- align.time(Ch_8500$sunrise, 60*10)
Ch_8500 <- Ch_8500[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ch_8500, aes(x=Ch_8500$timestamp)) + geom_line(aes(y=Ch_8500$sr)) + geom_line(aes(y=Ch_8500$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ch_8500 2023", x = "Datum", y = "Zeit")
pdf("Ch_8500_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ch_8500_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ch_8500["Sonnenaufgang"] <- strftime(Ch_8500$sr, format="%H:%M")
Ch_8500["Sonnenuntergang"] <- strftime(Ch_8500$ss, format="%H:%M")
write.table(Ch_8500, file="Ch_8500_SaSu.csv", dec=',', sep=';', row.names=FALSE)