library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
B��dingen <- sunrise.set(50.2953031926079,9.032655600994872, "2023/01/01", timezone="MET", num.days=370)
sunrise <- B��dingen$sunrise
sunset <- B��dingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
B��dingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
B��dingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
B��dingen["timestamp"] <- align.time(B��dingen$sunrise, 60*10)
B��dingen <- B��dingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(B��dingen, aes(x=B��dingen$timestamp)) + geom_line(aes(y=B��dingen$sr)) + geom_line(aes(y=B��dingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - B��dingen 2023", x = "Datum", y = "Zeit")
pdf("B��dingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="B��dingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
B��dingen["Sonnenaufgang"] <- strftime(B��dingen$sr, format="%H:%M")
B��dingen["Sonnenuntergang"] <- strftime(B��dingen$ss, format="%H:%M")
write.table(B��dingen, file="B��dingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)