library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Es_pego <- sunrise.set(38.844222,-0.114509, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Es_pego$sunrise
sunset <- Es_pego$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Es_pego["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Es_pego["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Es_pego["timestamp"] <- align.time(Es_pego$sunrise, 60*10)
Es_pego <- Es_pego[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Es_pego, aes(x=Es_pego$timestamp)) + geom_line(aes(y=Es_pego$sr)) + geom_line(aes(y=Es_pego$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Es_pego 2023", x = "Datum", y = "Zeit")
pdf("Es_pego_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Es_pego_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Es_pego["Sonnenaufgang"] <- strftime(Es_pego$sr, format="%H:%M")
Es_pego["Sonnenuntergang"] <- strftime(Es_pego$ss, format="%H:%M")
write.table(Es_pego, file="Es_pego_SaSu.csv", dec=',', sep=';', row.names=FALSE)