I'm new to time series analysis and using xts (and R in general) so please forgive the basic nature of the question.
I'm wanting to aggregate the data by a time frame (e.g. months) and by a second factor variable. To illustrate my question, please see the following:
require(xts)
# Create example df and convert it to an xts object
date <- sample(seq(as.Date("2015/01/01"), as.Date("2016/12/31"), by="day"),12)
colour <- c("Red", "Red", "Blue", "Blue", "Blue", "Blue", "Red", "Red", "Red",
"Red", "Blue", "Blue")
value <- sample(1:10, 12, replace = TRUE)
df <- cbind.data.frame(date, colour, value)
df <- xts(df[,-1], order.by = df$date)
colour value
2015-01-30 "Blue" "2"
2015-03-15 "Blue" "9"
2015-03-22 "Blue" "9"
2015-08-13 "Blue" "5"
2015-09-01 "Blue" "8"
2015-11-10 "Red" "7"
2016-04-26 "Blue" "2"
2016-07-06 "Red" "9"
2016-07-07 "Red" "6"
2016-07-08 "Red" "2"
2016-10-01 "Red" "6"
2016-11-07 "Red" "2"
apply.monthly(df$value, FUN = mean)
value
2015-01-30 2.000000
2015-03-22 9.000000
2015-08-13 5.000000
2015-09-01 8.000000
2015-11-10 7.000000
2016-04-26 2.000000
2016-07-08 5.666667
2016-10-01 6.000000
2016-11-07 2.000000
How about this?
aggregate(as.numeric(df$value),
list(Month = format(index(df), "%Y-%m"),
Colour = df$colour),
mean)
In response to your comment below:
# You can replace the format with the following to get a year month object
zoo::as.yearmon(index(df))
# Or you can covert to date by using the first of every month
as.Date(paste(format(index(df), "%Y-%m"), "-01", sep = ""))
You might find more ideas here: Converting year and month ("yyyy-mm" format) to a date in R?