combMean: Mean Recombination In datadr: Divide and Recombine for Large, Complex Data

Description

Mean recombination – Calculate the elementwise mean of a vector in each value

Usage

 `1` ```combMean(...) ```

Arguments

 `...` additional attributes to define the combiner (currently only used internally)

Details

`combMean` is passed to the argument `combine` in `recombine`

This method assumes that the values of the key-value pairs each consist of a numeric vector (with the same length). The mean is calculated elementwise across all the keys.

Author(s)

Ryan Hafen

`divide`, `recombine`, `combCollect`, `combDdo`, `combDdf`, `combRbind`, `combMeanCoef`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```# Create a distributed data frame using the iris data set bySpecies <- divide(iris, by = "Species") # Add a transformation that returns a vector of sums for each subset, one # mean for each variable bySpeciesTrans <- addTransform(bySpecies, function(x) apply(x, 2, sum)) bySpeciesTrans[[1]] # Calculate the elementwise mean of the vector of sums produced by # the transform, across the keys out1 <- recombine(bySpeciesTrans, combine = combMean) out1 # A more concise (and readable) way to do it bySpecies %>% addTransform(function(x) apply(x, 2, sum)) %>% recombine(combMean) # This manual, non-datadr approach illustrates the above computation # This step mimics the transformation above sums <- aggregate(. ~ Species, data = iris, sum) sums # And this step mimics the mean recombination out2 <- apply(sums[,-1], 2, mean) out2 # These are the same identical(out1, out2) ```