library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Balingen <- sunrise.set(48.28210768042345,8.863134384155273, "2024/01/01", timezone="MET", num.days=370)
sunrise <- Balingen$sunrise
sunset <- Balingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Balingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Balingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Balingen["timestamp"] <- align.time(Balingen$sunrise, 60*10)
Balingen <- Balingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Balingen, aes(x=Balingen$timestamp)) + geom_line(aes(y=Balingen$sr)) + geom_line(aes(y=Balingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Balingen 2024", x = "Datum", y = "Zeit")
pdf("Balingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Balingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Balingen["Sonnenaufgang"] <- strftime(Balingen$sr, format="%H:%M")
Balingen["Sonnenuntergang"] <- strftime(Balingen$ss, format="%H:%M")
write.table(Balingen, file="Balingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)