library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
97505 <- sunrise.set(50.0442256,10.1407961, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 97505$sunrise
sunset <- 97505$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
97505["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
97505["ss"] <- as.POSIXct(sunset, format = "%H:%M")
97505["timestamp"] <- align.time(97505$sunrise, 60*10)
97505 <- 97505[c("timestamp", "sr", "ss")]
locsrss <- ggplot(97505, aes(x=97505$timestamp)) + geom_line(aes(y=97505$sr)) + geom_line(aes(y=97505$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 97505 2023", x = "Datum", y = "Zeit")
pdf("97505_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="97505_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
97505["Sonnenaufgang"] <- strftime(97505$sr, format="%H:%M")
97505["Sonnenuntergang"] <- strftime(97505$ss, format="%H:%M")
write.table(97505, file="97505_SaSu.csv", dec=',', sep=';', row.names=FALSE)