library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Br��ckau <- sunrise.set(50.9621914,12.2117231, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Br��ckau$sunrise
sunset <- Br��ckau$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Br��ckau["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Br��ckau["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Br��ckau["timestamp"] <- align.time(Br��ckau$sunrise, 60*10)
Br��ckau <- Br��ckau[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Br��ckau, aes(x=Br��ckau$timestamp)) + geom_line(aes(y=Br��ckau$sr)) + geom_line(aes(y=Br��ckau$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Br��ckau 2023", x = "Datum", y = "Zeit")
pdf("Br��ckau_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Br��ckau_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Br��ckau["Sonnenaufgang"] <- strftime(Br��ckau$sr, format="%H:%M")
Br��ckau["Sonnenuntergang"] <- strftime(Br��ckau$ss, format="%H:%M")
write.table(Br��ckau, file="Br��ckau_SaSu.csv", dec=',', sep=';', row.names=FALSE)