library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE,_15890 <- sunrise.set(52.152503239937495,14.627180099487305, "2023/01/01", timezone="MET", num.days=370)
sunrise <- DE,_15890$sunrise
sunset <- DE,_15890$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE,_15890["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE,_15890["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE,_15890["timestamp"] <- align.time(DE,_15890$sunrise, 60*10)
DE,_15890 <- DE,_15890[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE,_15890, aes(x=DE,_15890$timestamp)) + geom_line(aes(y=DE,_15890$sr)) + geom_line(aes(y=DE,_15890$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE,_15890 2023", x = "Datum", y = "Zeit")
pdf("DE,_15890_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE,_15890_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE,_15890["Sonnenaufgang"] <- strftime(DE,_15890$sr, format="%H:%M")
DE,_15890["Sonnenuntergang"] <- strftime(DE,_15890$ss, format="%H:%M")
write.table(DE,_15890, file="DE,_15890_SaSu.csv", dec=',', sep=';', row.names=FALSE)