library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
27386_De <- sunrise.set(53.0666434,9.48352030000001, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 27386_De$sunrise
sunset <- 27386_De$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
27386_De["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
27386_De["ss"] <- as.POSIXct(sunset, format = "%H:%M")
27386_De["timestamp"] <- align.time(27386_De$sunrise, 60*10)
27386_De <- 27386_De[c("timestamp", "sr", "ss")]
locsrss <- ggplot(27386_De, aes(x=27386_De$timestamp)) + geom_line(aes(y=27386_De$sr)) + geom_line(aes(y=27386_De$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 27386_De 2023", x = "Datum", y = "Zeit")
pdf("27386_De_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="27386_De_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
27386_De["Sonnenaufgang"] <- strftime(27386_De$sr, format="%H:%M")
27386_De["Sonnenuntergang"] <- strftime(27386_De$ss, format="%H:%M")
write.table(27386_De, file="27386_De_SaSu.csv", dec=',', sep=';', row.names=FALSE)