library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
58642 <- sunrise.set(51.3656583,7.610033499999999, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 58642$sunrise
sunset <- 58642$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
58642["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
58642["ss"] <- as.POSIXct(sunset, format = "%H:%M")
58642["timestamp"] <- align.time(58642$sunrise, 60*10)
58642 <- 58642[c("timestamp", "sr", "ss")]
locsrss <- ggplot(58642, aes(x=58642$timestamp)) + geom_line(aes(y=58642$sr)) + geom_line(aes(y=58642$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 58642 2023", x = "Datum", y = "Zeit")
pdf("58642_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="58642_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
58642["Sonnenaufgang"] <- strftime(58642$sr, format="%H:%M")
58642["Sonnenuntergang"] <- strftime(58642$ss, format="%H:%M")
write.table(58642, file="58642_SaSu.csv", dec=',', sep=';', row.names=FALSE)