library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_09125 <- sunrise.set(50.7895475,12.942055799999935, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_09125$sunrise
sunset <- D_09125$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_09125["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_09125["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_09125["timestamp"] <- align.time(D_09125$sunrise, 60*10)
D_09125 <- D_09125[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_09125, aes(x=D_09125$timestamp)) + geom_line(aes(y=D_09125$sr)) + geom_line(aes(y=D_09125$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_09125 2023", x = "Datum", y = "Zeit")
pdf("D_09125_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_09125_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_09125["Sonnenaufgang"] <- strftime(D_09125$sr, format="%H:%M")
D_09125["Sonnenuntergang"] <- strftime(D_09125$ss, format="%H:%M")
write.table(D_09125, file="D_09125_SaSu.csv", dec=',', sep=';', row.names=FALSE)