library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
66564 <- sunrise.set(49.40594851453409,7.163286842456071, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 66564$sunrise
sunset <- 66564$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
66564["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
66564["ss"] <- as.POSIXct(sunset, format = "%H:%M")
66564["timestamp"] <- align.time(66564$sunrise, 60*10)
66564 <- 66564[c("timestamp", "sr", "ss")]
locsrss <- ggplot(66564, aes(x=66564$timestamp)) + geom_line(aes(y=66564$sr)) + geom_line(aes(y=66564$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 66564 2023", x = "Datum", y = "Zeit")
pdf("66564_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="66564_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
66564["Sonnenaufgang"] <- strftime(66564$sr, format="%H:%M")
66564["Sonnenuntergang"] <- strftime(66564$ss, format="%H:%M")
write.table(66564, file="66564_SaSu.csv", dec=',', sep=';', row.names=FALSE)