library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
83567 <- sunrise.set(48.1302866,12.3351218, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 83567$sunrise
sunset <- 83567$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
83567["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
83567["ss"] <- as.POSIXct(sunset, format = "%H:%M")
83567["timestamp"] <- align.time(83567$sunrise, 60*10)
83567 <- 83567[c("timestamp", "sr", "ss")]
locsrss <- ggplot(83567, aes(x=83567$timestamp)) + geom_line(aes(y=83567$sr)) + geom_line(aes(y=83567$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 83567 2023", x = "Datum", y = "Zeit")
pdf("83567_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="83567_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
83567["Sonnenaufgang"] <- strftime(83567$sr, format="%H:%M")
83567["Sonnenuntergang"] <- strftime(83567$ss, format="%H:%M")
write.table(83567, file="83567_SaSu.csv", dec=',', sep=';', row.names=FALSE)