library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
88662_��berlingen <- sunrise.set(47.7661746,9.170277199999986, "2023/01/01", timezone="MET", num.days=370)
sunrise <- 88662_��berlingen$sunrise
sunset <- 88662_��berlingen$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
88662_��berlingen["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
88662_��berlingen["ss"] <- as.POSIXct(sunset, format = "%H:%M")
88662_��berlingen["timestamp"] <- align.time(88662_��berlingen$sunrise, 60*10)
88662_��berlingen <- 88662_��berlingen[c("timestamp", "sr", "ss")]
locsrss <- ggplot(88662_��berlingen, aes(x=88662_��berlingen$timestamp)) + geom_line(aes(y=88662_��berlingen$sr)) + geom_line(aes(y=88662_��berlingen$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - 88662_��berlingen 2023", x = "Datum", y = "Zeit")
pdf("88662_��berlingen_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="88662_��berlingen_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
88662_��berlingen["Sonnenaufgang"] <- strftime(88662_��berlingen$sr, format="%H:%M")
88662_��berlingen["Sonnenuntergang"] <- strftime(88662_��berlingen$ss, format="%H:%M")
write.table(88662_��berlingen, file="88662_��berlingen_SaSu.csv", dec=',', sep=';', row.names=FALSE)