library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D��ren_Birgel <- sunrise.set(50.771621,6.446128000000044, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D��ren_Birgel$sunrise
sunset <- D��ren_Birgel$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D��ren_Birgel["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D��ren_Birgel["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D��ren_Birgel["timestamp"] <- align.time(D��ren_Birgel$sunrise, 60*10)
D��ren_Birgel <- D��ren_Birgel[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D��ren_Birgel, aes(x=D��ren_Birgel$timestamp)) + geom_line(aes(y=D��ren_Birgel$sr)) + geom_line(aes(y=D��ren_Birgel$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D��ren_Birgel 2023", x = "Datum", y = "Zeit")
pdf("D��ren_Birgel_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D��ren_Birgel_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D��ren_Birgel["Sonnenaufgang"] <- strftime(D��ren_Birgel$sr, format="%H:%M")
D��ren_Birgel["Sonnenuntergang"] <- strftime(D��ren_Birgel$ss, format="%H:%M")
write.table(D��ren_Birgel, file="D��ren_Birgel_SaSu.csv", dec=',', sep=';', row.names=FALSE)