library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
De_04838 <- sunrise.set(51.4869004,12.6411982, "2023/01/01", timezone="MET", num.days=370)
sunrise <- De_04838$sunrise
sunset <- De_04838$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
De_04838["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
De_04838["ss"] <- as.POSIXct(sunset, format = "%H:%M")
De_04838["timestamp"] <- align.time(De_04838$sunrise, 60*10)
De_04838 <- De_04838[c("timestamp", "sr", "ss")]
locsrss <- ggplot(De_04838, aes(x=De_04838$timestamp)) + geom_line(aes(y=De_04838$sr)) + geom_line(aes(y=De_04838$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - De_04838 2023", x = "Datum", y = "Zeit")
pdf("De_04838_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="De_04838_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
De_04838["Sonnenaufgang"] <- strftime(De_04838$sr, format="%H:%M")
De_04838["Sonnenuntergang"] <- strftime(De_04838$ss, format="%H:%M")
write.table(De_04838, file="De_04838_SaSu.csv", dec=',', sep=';', row.names=FALSE)