library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
48,_5 <- sunrise.set(47.99878169999999,5.0029916, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 48,_5$sunrise
sunset <- 48,_5$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
48,_5["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
48,_5["ss"] <- as.POSIXct(sunset, format = "%H:%M")
48,_5["timestamp"] <- align.time(48,_5$sunrise, 60*10)
48,_5 <- 48,_5[c("timestamp", "sr", "ss")]
locsrss <- ggplot(48,_5, aes(x=48,_5$timestamp)) + geom_line(aes(y=48,_5$sr)) + geom_line(aes(y=48,_5$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 48,_5 2023", x = "Datum", y = "Zeit")
pdf("48,_5_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="48,_5_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
48,_5["Sonnenaufgang"] <- strftime(48,_5$sr, format="%H:%M")
48,_5["Sonnenuntergang"] <- strftime(48,_5$ss, format="%H:%M")
write.table(48,_5, file="48,_5_SaSu.csv", dec=',', sep=';', row.names=FALSE)