library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Eppelheim <- sunrise.set(49.4006279,8.6299167, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Eppelheim$sunrise
sunset <- Eppelheim$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Eppelheim["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Eppelheim["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Eppelheim["timestamp"] <- align.time(Eppelheim$sunrise, 60*10)
Eppelheim <- Eppelheim[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Eppelheim, aes(x=Eppelheim$timestamp)) + geom_line(aes(y=Eppelheim$sr)) + geom_line(aes(y=Eppelheim$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Eppelheim 2023", x = "Datum", y = "Zeit")
pdf("Eppelheim_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Eppelheim_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Eppelheim["Sonnenaufgang"] <- strftime(Eppelheim$sr, format="%H:%M")
Eppelheim["Sonnenuntergang"] <- strftime(Eppelheim$ss, format="%H:%M")
write.table(Eppelheim, file="Eppelheim_SaSu.csv", dec=',', sep=';', row.names=FALSE)