library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
De_22949 <- sunrise.set(53.6888482,10.195212099999935, "2023/01/01", timezone="MET", num.days=370)
sunrise <- De_22949$sunrise
sunset <- De_22949$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
De_22949["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
De_22949["ss"] <- as.POSIXct(sunset, format = "%H:%M")
De_22949["timestamp"] <- align.time(De_22949$sunrise, 60*10)
De_22949 <- De_22949[c("timestamp", "sr", "ss")]
locsrss <- ggplot(De_22949, aes(x=De_22949$timestamp)) + geom_line(aes(y=De_22949$sr)) + geom_line(aes(y=De_22949$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - De_22949 2023", x = "Datum", y = "Zeit")
pdf("De_22949_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="De_22949_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
De_22949["Sonnenaufgang"] <- strftime(De_22949$sr, format="%H:%M")
De_22949["Sonnenuntergang"] <- strftime(De_22949$ss, format="%H:%M")
write.table(De_22949, file="De_22949_SaSu.csv", dec=',', sep=';', row.names=FALSE)