library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_48268 <- sunrise.set(52.0918867,7.6520413, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_48268$sunrise
sunset <- D_48268$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_48268["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_48268["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_48268["timestamp"] <- align.time(D_48268$sunrise, 60*10)
D_48268 <- D_48268[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_48268, aes(x=D_48268$timestamp)) + geom_line(aes(y=D_48268$sr)) + geom_line(aes(y=D_48268$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_48268 2023", x = "Datum", y = "Zeit")
pdf("D_48268_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_48268_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_48268["Sonnenaufgang"] <- strftime(D_48268$sr, format="%H:%M")
D_48268["Sonnenuntergang"] <- strftime(D_48268$ss, format="%H:%M")
write.table(D_48268, file="D_48268_SaSu.csv", dec=',', sep=';', row.names=FALSE)