library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Blankenheim <- sunrise.set(50.41462199999999,6.63403900000003, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Blankenheim$sunrise
sunset <- Blankenheim$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Blankenheim["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Blankenheim["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Blankenheim["timestamp"] <- align.time(Blankenheim$sunrise, 60*10)
Blankenheim <- Blankenheim[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Blankenheim, aes(x=Blankenheim$timestamp)) + geom_line(aes(y=Blankenheim$sr)) + geom_line(aes(y=Blankenheim$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Blankenheim 2023", x = "Datum", y = "Zeit")
pdf("Blankenheim_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Blankenheim_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Blankenheim["Sonnenaufgang"] <- strftime(Blankenheim$sr, format="%H:%M")
Blankenheim["Sonnenuntergang"] <- strftime(Blankenheim$ss, format="%H:%M")
write.table(Blankenheim, file="Blankenheim_SaSu.csv", dec=',', sep=';', row.names=FALSE)