library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
26316_Varel <- sunrise.set(53.3961959,8.1368242, "2024/01/01", timezone="MET", num.days=370)
sunrise <- 26316_Varel$sunrise
sunset <- 26316_Varel$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
26316_Varel["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
26316_Varel["ss"] <- as.POSIXct(sunset, format = "%H:%M")
26316_Varel["timestamp"] <- align.time(26316_Varel$sunrise, 60*10)
26316_Varel <- 26316_Varel[c("timestamp", "sr", "ss")]
locsrss <- ggplot(26316_Varel, aes(x=26316_Varel$timestamp)) + geom_line(aes(y=26316_Varel$sr)) + geom_line(aes(y=26316_Varel$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 26316_Varel 2024", x = "Datum", y = "Zeit")
pdf("26316_Varel_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="26316_Varel_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
26316_Varel["Sonnenaufgang"] <- strftime(26316_Varel$sr, format="%H:%M")
26316_Varel["Sonnenuntergang"] <- strftime(26316_Varel$ss, format="%H:%M")
write.table(26316_Varel, file="26316_Varel_SaSu.csv", dec=',', sep=';', row.names=FALSE)