library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
31688_d <- sunrise.set(52.289226,9.153662, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 31688_d$sunrise
sunset <- 31688_d$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
31688_d["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
31688_d["ss"] <- as.POSIXct(sunset, format = "%H:%M")
31688_d["timestamp"] <- align.time(31688_d$sunrise, 60*10)
31688_d <- 31688_d[c("timestamp", "sr", "ss")]
locsrss <- ggplot(31688_d, aes(x=31688_d$timestamp)) + geom_line(aes(y=31688_d$sr)) + geom_line(aes(y=31688_d$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 31688_d 2023", x = "Datum", y = "Zeit")
pdf("31688_d_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="31688_d_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
31688_d["Sonnenaufgang"] <- strftime(31688_d$sr, format="%H:%M")
31688_d["Sonnenuntergang"] <- strftime(31688_d$ss, format="%H:%M")
write.table(31688_d, file="31688_d_SaSu.csv", dec=',', sep=';', row.names=FALSE)