library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
07002_es <- sunrise.set(39.5719167,2.6542949, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 07002_es$sunrise
sunset <- 07002_es$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
07002_es["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
07002_es["ss"] <- as.POSIXct(sunset, format = "%H:%M")
07002_es["timestamp"] <- align.time(07002_es$sunrise, 60*10)
07002_es <- 07002_es[c("timestamp", "sr", "ss")]
locsrss <- ggplot(07002_es, aes(x=07002_es$timestamp)) + geom_line(aes(y=07002_es$sr)) + geom_line(aes(y=07002_es$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 07002_es 2023", x = "Datum", y = "Zeit")
pdf("07002_es_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="07002_es_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
07002_es["Sonnenaufgang"] <- strftime(07002_es$sr, format="%H:%M")
07002_es["Sonnenuntergang"] <- strftime(07002_es$ss, format="%H:%M")
write.table(07002_es, file="07002_es_SaSu.csv", dec=',', sep=';', row.names=FALSE)