library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_88696 <- sunrise.set(47.81327339915106,9.173787832260132, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_88696$sunrise
sunset <- D_88696$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_88696["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_88696["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_88696["timestamp"] <- align.time(D_88696$sunrise, 60*10)
D_88696 <- D_88696[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_88696, aes(x=D_88696$timestamp)) + geom_line(aes(y=D_88696$sr)) + geom_line(aes(y=D_88696$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_88696 2023", x = "Datum", y = "Zeit")
pdf("D_88696_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_88696_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_88696["Sonnenaufgang"] <- strftime(D_88696$sr, format="%H:%M")
D_88696["Sonnenuntergang"] <- strftime(D_88696$ss, format="%H:%M")
write.table(D_88696, file="D_88696_SaSu.csv", dec=',', sep=';', row.names=FALSE)