I'm curious if anyone out there can come up with a (faster) way to calculate rolling statistics (rolling mean, median, percentiles, etc.) over a variable interval of time (windowing).
That is, suppose one is given randomly timed observations (i.e. not daily, or weekly data, observations just have a time stamp, as in ticks data), and suppose you'd like to look at center and dispersion statistics that you are able to widen and tighten the interval of time over which these statistics are calculated.
I made a simple for loop that does this. But it obviously runs very slow (In fact I think my loop is still running over a small sample of data I set up to test its speed). I've been trying to get something like ddply to do this - which seems straitforward to get to run for daily stats - but I can't seem to work my way out of it.
Example:
Sample Set-up:
df <- data.frame(Date = runif(1000,0,30))
df$Price <- I((df$Date)^0.5 * (rnorm(1000,30,4)))
df$Date <- as.Date(df$Date, origin = "1970-01-01")
Example Function (that runs really slow with many observations
SummaryStats <- function(dataframe, interval){
# Returns daily simple summary stats,
# at varying intervals
# dataframe is the data frame in question, with Date and Price obs
# interval is the width of time to be treated as a day
firstDay <- min(dataframe$Date)
lastDay <- max(dataframe$Date)
result <- data.frame(Date = NULL,
Average = NULL, Median = NULL,
Count = NULL,
Percentile25 = NULL, Percentile75 = NULL)
for (Day in firstDay:lastDay){
dataframe.sub = subset(dataframe,
Date > (Day - (interval/2))
& Date < (Day + (interval/2)))
nu = data.frame(Date = Day,
Average = mean(dataframe.sub$Price),
Median = median(dataframe.sub$Price),
Count = length(dataframe.sub$Price),
P25 = quantile(dataframe.sub$Price, 0.25),
P75 = quantile(dataframe.sub$Price, 0.75))
result = rbind(result,nu)
}
return(result)
}
Your advice would be welcome!
Rcpp is a good approach if speed is your primary concern. I'll use the rolling mean statistic to explain by example.
Benchmarks: Rcpp versus R
x = sort(runif(25000,0,4*pi))
y = sin(x) + rnorm(length(x),0.5,0.5)
system.time( rollmean_r(x,y,xout=x,width=1.1) ) # ~60 seconds
system.time( rollmean_cpp(x,y,xout=x,width=1.1) ) # ~0.0007 seconds
Code for Rcpp and R function
cppFunction('
NumericVector rollmean_cpp( NumericVector x, NumericVector y,
NumericVector xout, double width) {
double total=0;
unsigned int n=x.size(), nout=xout.size(), i, ledge=0, redge=0;
NumericVector out(nout);
for( i=0; i<nout; i++ ) {
while( x[ redge ] - xout[i] <= width && redge<n )
total += y[redge++];
while( xout[i] - x[ ledge ] > width && ledge<n )
total -= y[ledge++];
if( ledge==redge ) { out[i]=NAN; total=0; continue; }
out[i] = total / (redge-ledge);
}
return out;
}')
rollmean_r = function(x,y,xout,width) {
out = numeric(length(xout))
for( i in seq_along(xout) ) {
window = x >= (xout[i]-width) & x <= (xout[i]+width)
out[i] = .Internal(mean( y[window] ))
}
return(out)
}
Now for an explantion of rollmean_cpp. x and y are the data. xout is a vector of points at which the rolling statistic is requested. width is the width*2 of the rolling window. Note that the indeces for the ends of sliding window are stored in ledge and redge. These are essentially pointers to the respective elements in x and y. These indeces could be very beneficial for calling other C++ functions (e.g., median and the like) that take a vector and starting and ending indeces as input.
For those who want a "verbose" version of rollmean_cpp for debugging (lengthy):
cppFunction('
NumericVector rollmean_cpp( NumericVector x, NumericVector y,
NumericVector xout, double width) {
double total=0, oldtotal=0;
unsigned int n=x.size(), nout=xout.size(), i, ledge=0, redge=0;
NumericVector out(nout);
for( i=0; i<nout; i++ ) {
Rcout << "Finding window "<< i << " for x=" << xout[i] << "..." << std::endl;
total = 0;
// numbers to push into window
while( x[ redge ] - xout[i] <= width && redge<n ) {
Rcout << "Adding (x,y) = (" << x[redge] << "," << y[redge] << ")" ;
Rcout << "; edges=[" << ledge << "," << redge << "]" << std::endl;
total += y[redge++];
}
// numbers to pop off window
while( xout[i] - x[ ledge ] > width && ledge<n ) {
Rcout << "Removing (x,y) = (" << x[ledge] << "," << y[ledge] << ")";
Rcout << "; edges=[" << ledge+1 << "," << redge-1 << "]" << std::endl;
total -= y[ledge++];
}
if(ledge==n) Rcout << " OVER ";
if( ledge==redge ) {
Rcout<<" NO DATA IN INTERVAL " << std::endl << std::endl;
oldtotal=total=0; out[i]=NAN; continue;}
Rcout << "For interval [" << xout[i]-width << "," <<
xout[i]+width << "], all points in interval [" << x[ledge] <<
", " << x[redge-1] << "]" << std::endl ;
Rcout << std::endl;
out[i] = ( oldtotal + total ) / (redge-ledge);
oldtotal=total+oldtotal;
}
return out;
}')
x = c(1,2,3,6,90,91)
y = c(9,8,7,5.2,2,1)
xout = c(1,2,2,3,6,6.1,13,90,100)
a = rollmean_cpp(x,y,xout=xout,2)
# Finding window 0 for x=1...
# Adding (x,y) = (1,9); edges=[0,0]
# Adding (x,y) = (2,8); edges=[0,1]
# Adding (x,y) = (3,7); edges=[0,2]
# For interval [-1,3], all points in interval [1, 3]
#
# Finding window 1 for x=2...
# For interval [0,4], all points in interval [1, 3]
#
# Finding window 2 for x=2...
# For interval [0,4], all points in interval [1, 3]
#
# Finding window 3 for x=3...
# For interval [1,5], all points in interval [1, 3]
#
# Finding window 4 for x=6...
# Adding (x,y) = (6,5.2); edges=[0,3]
# Removing (x,y) = (1,9); edges=[1,3]
# Removing (x,y) = (2,8); edges=[2,3]
# Removing (x,y) = (3,7); edges=[3,3]
# For interval [4,8], all points in interval [6, 6]
#
# Finding window 5 for x=6.1...
# For interval [4.1,8.1], all points in interval [6, 6]
#
# Finding window 6 for x=13...
# Removing (x,y) = (6,5.2); edges=[4,3]
# NO DATA IN INTERVAL
#
# Finding window 7 for x=90...
# Adding (x,y) = (90,2); edges=[4,4]
# Adding (x,y) = (91,1); edges=[4,5]
# For interval [88,92], all points in interval [90, 91]
#
# Finding window 8 for x=100...
# Removing (x,y) = (90,2); edges=[5,5]
# Removing (x,y) = (91,1); edges=[6,5]
# OVER NO DATA IN INTERVAL
print(a)
# [1] 8.0 8.0 8.0 8.0 5.2 5.2 NaN 1.5 NaN
Let's see... you are doing a loop( very slow in R), making unnecessary copies of data in creating subset, and using rbind to accumulate you data set. If you avoid those, things will speed up considerably. Try this...
Summary_Stats <- function(Day, dataframe, interval){
c1 <- dataframe$Date > Day - interval/2 &
dataframe$Date < Day + interval/2
c(
as.numeric(Day),
mean(dataframe$Price[c1]),
median(dataframe$Price[c1]),
sum(c1),
quantile(dataframe$Price[c1], 0.25),
quantile(dataframe$Price[c1], 0.75)
)
}
Summary_Stats(df$Date[2],dataframe=df, interval=20)
firstDay <- min(df$Date)
lastDay <- max(df$Date)
system.time({
x <- sapply(firstDay:lastDay, Summary_Stats, dataframe=df, interval=20)
x <- as.data.frame(t(x))
names(x) <- c("Date","Average","Median","Count","P25","P75")
x$Date <- as.Date(x$Date)
})
dim(x)
head(x)
In reply to my question to "Kevin" above, I think I figured something out below.
This function takes ticks data (time observations come in at random intervals are and indicated by a time stamp) and calculates the the mean over an interval.
library(Rcpp)
cppFunction('
NumericVector rollmean_c2( NumericVector x, NumericVector y, double width,
double Min, double Max) {
double total = 0, redge,center;
unsigned int n = (Max - Min) + 1,
i, j=0, k, ledge=0, redgeIndex;
NumericVector out(n);
for (i = 0; i < n; i++){
center = Min + i + 0.5;
redge = center - width / 2;
redgeIndex = 0;
total = 0;
while (x[redgeIndex] < redge){
redgeIndex++;
}
j = redgeIndex;
while (x[j] < redge + width){
total += y[j++];
}
out[i] = total / (j - redgeIndex);
}
return out;
}')
# Set up example data
x = seq(0,4*pi,length.out=2500)
y = sin(x) + rnorm(length(x),0.5,0.5)
plot(x,y,pch=20,col="black",
main="Sliding window mean; width=1",
sub="rollmean_c in red rollmean_r overlaid in white.")
c.out = rollmean_c2(x,y,width=1,Min = min(x), Max = max(x))
lines(0.5:12.5,c.out,col="red",lwd=3)
Think of all of the points connected as a chain. Think of this chain as a graph, where each data point is a node. Then, for each node, we want to find all other nodes that are distance w or less away. To do this, I first generate a matrix that gives pairwise distances. The nth row gives the distance for nodes n nodes apart.
# First, some data
x = sort(runif(25000,0,4*pi))
y = sin(x) + rnorm(length(x),0,0.5)
# calculate the rows of the matrix one by one
# until the distance between the two closest nodes is greater than w
# This algorithm is actually faster than `dist` because it usually stops
# much sooner
dl = list()
dl[[1]] = diff(x)
i = 1
while( min(dl[[i]]) <= w ) {
pdl = dl[[i]]
dl[[i+1]] = pdl[-length(pdl)] + dl[[1]][-(1:i)]
i = i+1
}
# turn the list of the rows into matrices
rarray = do.call( rbind, lapply(dl,inf.pad,length(x)) )
larray = do.call( rbind, lapply(dl,inf.pad,length(x),"right") )
# extra function
inf.pad = function(x,size,side="left") {
if(side=="left") {
x = c( x, rep(Inf, size-length(x) ) )
} else {
x = c( rep(Inf, size-length(x) ), x )
}
x
}
I then use the matrices to determine the edge of each window. For this example, I set w=2.
# How many data points to look left or right at each data point
lookr = colSums( rarray <= w )
lookl = colSums( larray <= w )
# convert these "look" variables to indeces of the input vector
ri = 1:length(x) + lookr
li = 1:length(x) - lookl
With the windows defined, it's pretty simple to use the *apply functions to get the final answer.
rolling.mean = vapply( mapply(':',li,ri), function(i) .Internal(mean(y[i])), 1 )
All of the above code took about 50 seconds on my computer. This is a little faster than the rollmean_r function in my other answer. However, the especially nice thing here is that the indeces are provided. You could then use whatever R function you like with the *apply functions. For example,
rolling.mean = vapply( mapply(':',li,ri),
function(i) .Internal(mean(y[i])), 1 )
takes about 5 seconds. And,
rolling.median = vapply( mapply(':',li,ri),
function(i) median(y[i]), 1 )
takes about 14 seconds. If you wanted to, you could use the Rcpp function in my other answer to get the indeces.
来源：https://stackoverflow.com/questions/20134823/r-faster-way-to-calculate-rolling-statistics-over-a-variable-interval