library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ch_8001 <- sunrise.set(47.3741561,8.5396323, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Ch_8001$sunrise
sunset <- Ch_8001$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ch_8001["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ch_8001["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ch_8001["timestamp"] <- align.time(Ch_8001$sunrise, 60*10)
Ch_8001 <- Ch_8001[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ch_8001, aes(x=Ch_8001$timestamp)) + geom_line(aes(y=Ch_8001$sr)) + geom_line(aes(y=Ch_8001$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ch_8001 2023", x = "Datum", y = "Zeit")
pdf("Ch_8001_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ch_8001_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ch_8001["Sonnenaufgang"] <- strftime(Ch_8001$sr, format="%H:%M")
Ch_8001["Sonnenuntergang"] <- strftime(Ch_8001$ss, format="%H:%M")
write.table(Ch_8001, file="Ch_8001_SaSu.csv", dec=',', sep=';', row.names=FALSE)