Class to calculate mean and variance in one pass

RunningStats computes summary statistics on a data stream efficiently. Mean and variance are calculated with Welford's online algorithm (https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance). The min, max, sum and count are also tracked. The input data values are not stored in memory, so this class can be used to compute statistics for very large data streams.

Arguments

na_rm

Logical scalar. TRUE to remove NA from the input data or FALSE to retain NA (defaults to TRUE).

Value

An object of class RunningStats. A RunningStats object maintains the current minimum, maximum, mean, variance, sum and count of values that have been read from the stream. It can be updated repeatedly with new values (i.e., chunks of data read from the input stream), but its memory footprint is negligible. Class methods for updating with new values and retrieving current values of statistics are described in Details. RunningStats is a C++ class exposed directly to R (via RCPP_EXPOSED_CLASS). Methods of the class are accessed in R using the $

operator.

Note

The intended use is computing summary statistics for specific subsets or zones of a raster that could be defined in various ways and are generally not contiguous. The algorithm as implemented here incurs the cost of floating point division for each new value updated (i.e., per pixel), but is reasonably efficient for the use case. Note that GDAL internally uses an optimized version of Welford's algorithm to compute raster statistics as described in detail by Rouault, 2016 (https://github.com/OSGeo/gdal/blob/master/gcore/statistics.txt). The class method GDALRaster$getStatistics() is a GDAL API wrapper that computes statistics for a whole raster band.

Usage

rs <- new(RunningStats, na_rm)

## Methods (see Details)
rs$update(newvalues)
rs$get_count()
rs$get_mean()
rs$get_min()
rs$get_max()
rs$get_sum()
rs$get_var()
rs$get_sd()
rs$reset()

Details

new(RunningStats, na_rm) Constructor. Returns an object of class RunningStats.

$update(newvalues) Updates the RunningStats object with a numeric vector of newvalues (i.e., a chunk of values from the data stream). No return value, called for side effects.

$get_count() Returns the count of values received from the data stream.

$get_mean() Returns the mean of values received from the data stream.

$get_min() Returns the minimum value received from the data stream.

$get_max() Returns the maximum value received from the data stream.

$get_sum() Returns the sum of values received from the data stream.

$get_var() Returns the variance of values from the data stream (denominator n - 1).

$get_sd() Returns the standard deviation of values from the data stream (denominator n - 1).

$reset() Clears the RunningStats object to its initialized state (count = 0). No return value, called for side effects.

Examples

set.seed(42)

rs <- new(RunningStats, na_rm=TRUE)
chunk <- runif(1000)
rs$update(chunk)
object.size(rs)
#> 704 bytes

rs$get_count()
#> [1] 1000
length(chunk)
#> [1] 1000

rs$get_mean()
#> [1] 0.4882555
mean(chunk)
#> [1] 0.4882555

rs$get_min()
#> [1] 0.0002388966
min(chunk)
#> [1] 0.0002388966

rs$get_max()
#> [1] 0.9984908
max(chunk)
#> [1] 0.9984908

rs$get_var()
#> [1] 0.08493159
var(chunk)
#> [1] 0.08493159

rs$get_sd()
#> [1] 0.2914302
sd(chunk)
#> [1] 0.2914302

# \donttest{
## 10^9 values read in 10,000 chunks
## should take under 1 minute on most PC hardware
for (i in 1:1e4) {
  chunk <- runif(1e5)
  rs$update(chunk)
}
rs$get_count()
#> [1] 1000001000
rs$get_mean()
#> [1] 0.5000044
rs$get_var()
#> [1] 0.08333479

object.size(rs)
#> 704 bytes
# }