library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
09603 <- sunrise.set(50.97496717499484,13.275432586669921, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 09603$sunrise
sunset <- 09603$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
09603["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
09603["ss"] <- as.POSIXct(sunset, format = "%H:%M")
09603["timestamp"] <- align.time(09603$sunrise, 60*10)
09603 <- 09603[c("timestamp", "sr", "ss")]
locsrss <- ggplot(09603, aes(x=09603$timestamp)) + geom_line(aes(y=09603$sr)) + geom_line(aes(y=09603$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 09603 2023", x = "Datum", y = "Zeit")
pdf("09603_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="09603_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
09603["Sonnenaufgang"] <- strftime(09603$sr, format="%H:%M")
09603["Sonnenuntergang"] <- strftime(09603$ss, format="%H:%M")
write.table(09603, file="09603_SaSu.csv", dec=',', sep=';', row.names=FALSE)