library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_84186 <- sunrise.set(48.45072405028467,12.110624313354492, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_84186$sunrise
sunset <- D_84186$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_84186["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_84186["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_84186["timestamp"] <- align.time(D_84186$sunrise, 60*10)
D_84186 <- D_84186[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_84186, aes(x=D_84186$timestamp)) + geom_line(aes(y=D_84186$sr)) + geom_line(aes(y=D_84186$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_84186 2023", x = "Datum", y = "Zeit")
pdf("D_84186_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_84186_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_84186["Sonnenaufgang"] <- strftime(D_84186$sr, format="%H:%M")
D_84186["Sonnenuntergang"] <- strftime(D_84186$ss, format="%H:%M")
write.table(D_84186, file="D_84186_SaSu.csv", dec=',', sep=';', row.names=FALSE)