library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
CH_9042 <- sunrise.set(47.411390710631615,9.44425106048584, "2023/01/01", timezone="MET", num.days=370)
sunrise <- CH_9042$sunrise
sunset <- CH_9042$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
CH_9042["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
CH_9042["ss"] <- as.POSIXct(sunset, format = "%H:%M")
CH_9042["timestamp"] <- align.time(CH_9042$sunrise, 60*10)
CH_9042 <- CH_9042[c("timestamp", "sr", "ss")]
locsrss <- ggplot(CH_9042, aes(x=CH_9042$timestamp)) + geom_line(aes(y=CH_9042$sr)) + geom_line(aes(y=CH_9042$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - CH_9042 2023", x = "Datum", y = "Zeit")
pdf("CH_9042_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="CH_9042_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
CH_9042["Sonnenaufgang"] <- strftime(CH_9042$sr, format="%H:%M")
CH_9042["Sonnenuntergang"] <- strftime(CH_9042$ss, format="%H:%M")
write.table(CH_9042, file="CH_9042_SaSu.csv", dec=',', sep=';', row.names=FALSE)