library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
37688 <- sunrise.set(51.64556092576849,9.408915625421246, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 37688$sunrise
sunset <- 37688$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
37688["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
37688["ss"] <- as.POSIXct(sunset, format = "%H:%M")
37688["timestamp"] <- align.time(37688$sunrise, 60*10)
37688 <- 37688[c("timestamp", "sr", "ss")]
locsrss <- ggplot(37688, aes(x=37688$timestamp)) + geom_line(aes(y=37688$sr)) + geom_line(aes(y=37688$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 37688 2023", x = "Datum", y = "Zeit")
pdf("37688_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="37688_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
37688["Sonnenaufgang"] <- strftime(37688$sr, format="%H:%M")
37688["Sonnenuntergang"] <- strftime(37688$ss, format="%H:%M")
write.table(37688, file="37688_SaSu.csv", dec=',', sep=';', row.names=FALSE)