library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_67159 <- sunrise.set(49.4474032,8.2229346, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_67159$sunrise
sunset <- D_67159$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_67159["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_67159["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_67159["timestamp"] <- align.time(D_67159$sunrise, 60*10)
D_67159 <- D_67159[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_67159, aes(x=D_67159$timestamp)) + geom_line(aes(y=D_67159$sr)) + geom_line(aes(y=D_67159$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_67159 2023", x = "Datum", y = "Zeit")
pdf("D_67159_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_67159_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_67159["Sonnenaufgang"] <- strftime(D_67159$sr, format="%H:%M")
D_67159["Sonnenuntergang"] <- strftime(D_67159$ss, format="%H:%M")
write.table(D_67159, file="D_67159_SaSu.csv", dec=',', sep=';', row.names=FALSE)