library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
D_52156 <- sunrise.set(50.534871401083706,6.254911422729492, "2023/01/01", timezone="MET", num.days=370)
sunrise <- D_52156$sunrise
sunset <- D_52156$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
D_52156["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
D_52156["ss"] <- as.POSIXct(sunset, format = "%H:%M")
D_52156["timestamp"] <- align.time(D_52156$sunrise, 60*10)
D_52156 <- D_52156[c("timestamp", "sr", "ss")]
locsrss <- ggplot(D_52156, aes(x=D_52156$timestamp)) + geom_line(aes(y=D_52156$sr)) + geom_line(aes(y=D_52156$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - D_52156 2023", x = "Datum", y = "Zeit")
pdf("D_52156_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="D_52156_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
D_52156["Sonnenaufgang"] <- strftime(D_52156$sr, format="%H:%M")
D_52156["Sonnenuntergang"] <- strftime(D_52156$ss, format="%H:%M")
write.table(D_52156, file="D_52156_SaSu.csv", dec=',', sep=';', row.names=FALSE)