library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
86609 <- sunrise.set(48.719501,10.768382, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 86609$sunrise
sunset <- 86609$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
86609["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
86609["ss"] <- as.POSIXct(sunset, format = "%H:%M")
86609["timestamp"] <- align.time(86609$sunrise, 60*10)
86609 <- 86609[c("timestamp", "sr", "ss")]
locsrss <- ggplot(86609, aes(x=86609$timestamp)) + geom_line(aes(y=86609$sr)) + geom_line(aes(y=86609$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 86609 2023", x = "Datum", y = "Zeit")
pdf("86609_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="86609_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
86609["Sonnenaufgang"] <- strftime(86609$sr, format="%H:%M")
86609["Sonnenuntergang"] <- strftime(86609$ss, format="%H:%M")
write.table(86609, file="86609_SaSu.csv", dec=',', sep=';', row.names=FALSE)