library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
83109 <- sunrise.set(47.90014621531362,12.082085609436035, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 83109$sunrise
sunset <- 83109$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
83109["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
83109["ss"] <- as.POSIXct(sunset, format = "%H:%M")
83109["timestamp"] <- align.time(83109$sunrise, 60*10)
83109 <- 83109[c("timestamp", "sr", "ss")]
locsrss <- ggplot(83109, aes(x=83109$timestamp)) + geom_line(aes(y=83109$sr)) + geom_line(aes(y=83109$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 83109 2023", x = "Datum", y = "Zeit")
pdf("83109_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="83109_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
83109["Sonnenaufgang"] <- strftime(83109$sr, format="%H:%M")
83109["Sonnenuntergang"] <- strftime(83109$ss, format="%H:%M")
write.table(83109, file="83109_SaSu.csv", dec=',', sep=';', row.names=FALSE)