library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Esperde <- sunrise.set(52.0267791,9.5140517, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Esperde$sunrise
sunset <- Esperde$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Esperde["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Esperde["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Esperde["timestamp"] <- align.time(Esperde$sunrise, 60*10)
Esperde <- Esperde[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Esperde, aes(x=Esperde$timestamp)) + geom_line(aes(y=Esperde$sr)) + geom_line(aes(y=Esperde$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Esperde 2023", x = "Datum", y = "Zeit")
pdf("Esperde_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Esperde_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Esperde["Sonnenaufgang"] <- strftime(Esperde$sr, format="%H:%M")
Esperde["Sonnenuntergang"] <- strftime(Esperde$ss, format="%H:%M")
write.table(Esperde, file="Esperde_SaSu.csv", dec=',', sep=';', row.names=FALSE)