library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
16269 <- sunrise.set(52.7015681,14.091710700000021, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 16269$sunrise
sunset <- 16269$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
16269["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
16269["ss"] <- as.POSIXct(sunset, format = "%H:%M")
16269["timestamp"] <- align.time(16269$sunrise, 60*10)
16269 <- 16269[c("timestamp", "sr", "ss")]
locsrss <- ggplot(16269, aes(x=16269$timestamp)) + geom_line(aes(y=16269$sr)) + geom_line(aes(y=16269$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 16269 2023", x = "Datum", y = "Zeit")
pdf("16269_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="16269_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
16269["Sonnenaufgang"] <- strftime(16269$sr, format="%H:%M")
16269["Sonnenuntergang"] <- strftime(16269$ss, format="%H:%M")
write.table(16269, file="16269_SaSu.csv", dec=',', sep=';', row.names=FALSE)