roll-up (aka 'group by') functions for time series data in R

ts-rollup.R

require(zoo)
require(xts)

# TSR is an R time series comprised of a datetime index and a single column of data
# created like so:

# mock a datetime index, by creating a data vector comprised of the previous 500 days
nd = Sys.Date()
st = 500 - nd
idx = seq(from=st, to = nd, by='days')
data = 10* runif(length(idx))

# create the R time series, using the R package 'xts'

TSR = as.xts(data, order.by=idx)

# to roll-up this daily xts series:

# step 1: create index of endpoints for the time period to roll-up to

np_week = endpoints(TSR, on='weeks')
np_month = endpoints(TSR, on='months')

# step 2: rollup the time series
# this is essentially a a 'group by' operation so you need you to specify a function
# to which the continuous data is passed for aggregation (most common are sum, and mean)

tsr_week = period.apply(TSR, INDEX=np_week, FUN=mean)
trs_month = period.apply(TSR, INDEX=np_month, FUN=mean)





ts_split = function(x) { 
	as.Date(unlist(strsplit(as.character(x), split=' ', 
							fixed=TRUE))[1], 
							format="%Y-%m-%d", 
							origin="1970-01-01") 
}

acf1 = acf(TSR_day, 
		   lag.max=70, 
		   drop.lag.0=T,
			plot=F
)


#---------- plotting -------------#

op = par(mar=c(4,4,1,2), mfrow=c(2,1), cex=0.8, 
		 col.lab="steelblue4", col=#D96A14"


plot(acf1$lag[,1,1], acf1$acf[,1,1], 
	 ann=F, cex.axis=.8, xlab=F, 
	 ylab=F, main=NULL, col="#D96A14", 
	 type='h'
)


#---------------------------- filters -------------------------------#

# linear filter
lf1 = filter( x, filter, method=c("convolution", "recursive"),
				sides=2, circular=F, init)

# three-point simple moving average
# ie, if the points reprsent days, then this is a 3-day moving avg
# just take a given day, and the day before, and the day after, then average them (/3)

mv_avg = function(x) { 
	y = numeric(length(x)-2)
	for (i in 2:(length(x)-1)) {
		y[i] = (x[i-1] + x[i] + x[i+1])/3
	}
	y
}