library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
58636 <- sunrise.set(51.38115440000001,7.716816899999999, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 58636$sunrise
sunset <- 58636$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
58636["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
58636["ss"] <- as.POSIXct(sunset, format = "%H:%M")
58636["timestamp"] <- align.time(58636$sunrise, 60*10)
58636 <- 58636[c("timestamp", "sr", "ss")]
locsrss <- ggplot(58636, aes(x=58636$timestamp)) + geom_line(aes(y=58636$sr)) + geom_line(aes(y=58636$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 58636 2023", x = "Datum", y = "Zeit")
pdf("58636_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="58636_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
58636["Sonnenaufgang"] <- strftime(58636$sr, format="%H:%M")
58636["Sonnenuntergang"] <- strftime(58636$ss, format="%H:%M")
write.table(58636, file="58636_SaSu.csv", dec=',', sep=';', row.names=FALSE)