Hi all,
I have a data frame with made up chicken population number that is recorded every 3 days for a total of 10 data points each month. (This is a made-up data I'm working on to practice before working with my actual data.)
Some months are missing 1 or 2 values and I would like to make it a timeseries to eventually calculate Sen's Slope. Can someone please help me put in a placeholder so that each month has 10 rows of data each? The 4th line of code I have for the interpolation does not work.
Thank you so much in advance!
This is what I have so far:
# making a date column
df$date <- as.Date(with(df, paste(year, month, day,sep="/")), "%Y/%m/%d")
# ordering by date to run the ts()
i <- order(df$date)
# making the timeseries
allts<-ts(data$chickens[i],start=c(1,1),frequency=10)
# interpolating missing values
allts<-na_seadec(allts,algorithm="interpolation",find_frequency=F,maxgap=Inf)
This is what my data looks like:
year | month | day | chickens |
---|---|---|---|
2000 | 1 | 2 | 1 |
2000 | 1 | 5 | 2 |
2000 | 1 | 8 | 3 |
2000 | 1 | 11 | 4 |
2000 | 1 | 14 | 5 |
2000 | 1 | 17 | 6 |
2000 | 1 | 20 | 7 |
2000 | 1 | 23 | 8 |
2000 | 1 | 26 | 9 |
2000 | 2 | 3 | 2 |
2000 | 2 | 6 | 4 |
2000 | 2 | 9 | 6 |
2000 | 2 | 12 | 8 |
2000 | 2 | 15 | 10 |
2000 | 2 | 18 | 12 |
2000 | 2 | 21 | 14 |
2000 | 2 | 24 | 16 |
2000 | 2 | 27 | 18 |
2000 | 3 | 1 | 3 |
2000 | 3 | 4 | 6 |
2000 | 3 | 7 | 9 |
2000 | 3 | 10 | 12 |
2000 | 3 | 13 | 15 |
2000 | 3 | 16 | 18 |
2000 | 3 | 19 | 21 |
2000 | 3 | 22 | 24 |
2000 | 3 | 25 | 27 |
2000 | 3 | 28 | 30 |
2000 | 4 | 3 | 4 |
2000 | 4 | 6 | 8 |
2000 | 4 | 9 | 12 |
2000 | 4 | 12 | 16 |
2000 | 4 | 15 | 20 |
2000 | 4 | 18 | 24 |
2000 | 4 | 21 | 28 |
2000 | 4 | 24 | 32 |
2000 | 4 | 27 | 36 |
2000 | 5 | 3 | 5 |
2000 | 5 | 6 | 10 |
2000 | 5 | 9 | 15 |
2000 | 5 | 12 | 20 |
2000 | 5 | 15 | 25 |
2000 | 5 | 18 | 30 |
2000 | 5 | 21 | 35 |
2000 | 5 | 24 | 40 |
2000 | 5 | 27 | 45 |
2000 | 6 | 3 | 6 |
2000 | 6 | 6 | 12 |
2000 | 6 | 9 | 18 |
2000 | 6 | 12 | 24 |
2000 | 6 | 15 | 30 |
2000 | 6 | 18 | 36 |
2000 | 6 | 21 | 42 |
2000 | 6 | 24 | 48 |
2000 | 6 | 27 | 54 |
2000 | 7 | 3 | 7 |
2000 | 7 | 6 | 14 |
2000 | 7 | 9 | 21 |
2000 | 7 | 12 | 28 |
2000 | 7 | 15 | 35 |
2000 | 7 | 18 | 42 |
2000 | 7 | 21 | 49 |
2000 | 7 | 24 | 56 |
2000 | 7 | 27 | 63 |
2000 | 8 | 3 | 8 |
2000 | 8 | 6 | 16 |
2000 | 8 | 9 | 24 |
2000 | 8 | 12 | 32 |
2000 | 8 | 15 | 40 |
2000 | 8 | 18 | 48 |
2000 | 8 | 21 | 56 |
2000 | 8 | 24 | 64 |
2000 | 8 | 27 | 72 |
2000 | 9 | 3 | 9 |
2000 | 9 | 6 | 18 |
2000 | 9 | 9 | 27 |
2000 | 9 | 12 | 36 |
2000 | 9 | 15 | 45 |
2000 | 9 | 18 | 54 |
2000 | 9 | 21 | 63 |
2000 | 9 | 24 | 72 |
2000 | 9 | 27 | 81 |
2000 | 10 | 3 | 10 |
2000 | 10 | 6 | 20 |
2000 | 10 | 9 | 30 |
2000 | 10 | 12 | 40 |
2000 | 10 | 15 | 50 |
2000 | 10 | 18 | 60 |
2000 | 10 | 21 | 70 |
2000 | 10 | 24 | 80 |
2000 | 10 | 27 | 90 |
2000 | 11 | 3 | 11 |
2000 | 11 | 6 | 22 |
2000 | 11 | 9 | 33 |
2000 | 11 | 12 | 44 |
2000 | 11 | 15 | 55 |
2000 | 11 | 18 | 66 |
2000 | 11 | 21 | 77 |
2000 | 11 | 24 | 88 |
2000 | 11 | 27 | 99 |
2000 | 12 | 6 | 12 |
2000 | 12 | 9 | 24 |
2000 | 12 | 12 | 36 |
2000 | 12 | 18 | 48 |
2000 | 12 | 21 | 60 |
2000 | 12 | 24 | 72 |
2000 | 12 | 27 | 84 |