library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
AT_7121 <- sunrise.set(47.91989408770393,16.869120597839355, "2023/01/01", timezone="MET", num.days=370)
sunrise <- AT_7121$sunrise
sunset <- AT_7121$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
AT_7121["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
AT_7121["ss"] <- as.POSIXct(sunset, format = "%H:%M")
AT_7121["timestamp"] <- align.time(AT_7121$sunrise, 60*10)
AT_7121 <- AT_7121[c("timestamp", "sr", "ss")]
locsrss <- ggplot(AT_7121, aes(x=AT_7121$timestamp)) + geom_line(aes(y=AT_7121$sr)) + geom_line(aes(y=AT_7121$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - AT_7121 2023", x = "Datum", y = "Zeit")
pdf("AT_7121_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="AT_7121_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
AT_7121["Sonnenaufgang"] <- strftime(AT_7121$sr, format="%H:%M")
AT_7121["Sonnenuntergang"] <- strftime(AT_7121$ss, format="%H:%M")
write.table(AT_7121, file="AT_7121_SaSu.csv", dec=',', sep=';', row.names=FALSE)