library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
18147 <- sunrise.set(54.11212533925015,12.152724266052246, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 18147$sunrise
sunset <- 18147$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
18147["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
18147["ss"] <- as.POSIXct(sunset, format = "%H:%M")
18147["timestamp"] <- align.time(18147$sunrise, 60*10)
18147 <- 18147[c("timestamp", "sr", "ss")]
locsrss <- ggplot(18147, aes(x=18147$timestamp)) + geom_line(aes(y=18147$sr)) + geom_line(aes(y=18147$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 18147 2023", x = "Datum", y = "Zeit")
pdf("18147_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="18147_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
18147["Sonnenaufgang"] <- strftime(18147$sr, format="%H:%M")
18147["Sonnenuntergang"] <- strftime(18147$ss, format="%H:%M")
write.table(18147, file="18147_SaSu.csv", dec=',', sep=';', row.names=FALSE)