library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
B��singen <- sunrise.set(48.236894345711214,8.553376796789507, "2023/01/01", timezone="MET", num.days=370)
sunrise <- B��singen$sunrise
sunset <- B��singen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
B��singen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
B��singen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
B��singen["timestamp"] <- align.time(B��singen$sunrise, 60*10)
B��singen <- B��singen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(B��singen, aes(x=B��singen$timestamp)) + geom_line(aes(y=B��singen$sr)) + geom_line(aes(y=B��singen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - B��singen 2023", x = "Datum", y = "Zeit")
pdf("B��singen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="B��singen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
B��singen["Sonnenaufgang"] <- strftime(B��singen$sr, format="%H:%M")
B��singen["Sonnenuntergang"] <- strftime(B��singen$ss, format="%H:%M")
write.table(B��singen, file="B��singen_SaSu.csv", dec=',', sep=';', row.names=FALSE)