library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
30890 <- sunrise.set(52.34375350000001,9.480611199999998, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 30890$sunrise
sunset <- 30890$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
30890["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
30890["ss"] <- as.POSIXct(sunset, format = "%H:%M")
30890["timestamp"] <- align.time(30890$sunrise, 60*10)
30890 <- 30890[c("timestamp", "sr", "ss")]
locsrss <- ggplot(30890, aes(x=30890$timestamp)) + geom_line(aes(y=30890$sr)) + geom_line(aes(y=30890$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 30890 2023", x = "Datum", y = "Zeit")
pdf("30890_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="30890_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
30890["Sonnenaufgang"] <- strftime(30890$sr, format="%H:%M")
30890["Sonnenuntergang"] <- strftime(30890$ss, format="%H:%M")
write.table(30890, file="30890_SaSu.csv", dec=',', sep=';', row.names=FALSE)