library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
89250_Senden <- sunrise.set(48.3255759,10.049269999999978, "2024/01/01", timezone="MET", num.days=370)
sunrise <- 89250_Senden$sunrise
sunset <- 89250_Senden$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
89250_Senden["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
89250_Senden["ss"] <- as.POSIXct(sunset, format = "%H:%M")
89250_Senden["timestamp"] <- align.time(89250_Senden$sunrise, 60*10)
89250_Senden <- 89250_Senden[c("timestamp", "sr", "ss")]
locsrss <- ggplot(89250_Senden, aes(x=89250_Senden$timestamp)) + geom_line(aes(y=89250_Senden$sr)) + geom_line(aes(y=89250_Senden$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 89250_Senden 2024", x = "Datum", y = "Zeit")
pdf("89250_Senden_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="89250_Senden_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
89250_Senden["Sonnenaufgang"] <- strftime(89250_Senden$sr, format="%H:%M")
89250_Senden["Sonnenuntergang"] <- strftime(89250_Senden$ss, format="%H:%M")
write.table(89250_Senden, file="89250_Senden_SaSu.csv", dec=',', sep=';', row.names=FALSE)