aggregate {stats} R Documentation

## Compute Summary Statistics of Data Subsets

### Description

Splits the data into subsets, computes summary statistics for each, and returns the result in a convenient form.

### Usage

aggregate(x, ...)

## Default S3 method:
aggregate(x, ...)

## S3 method for class 'data.frame':
aggregate(x, by, FUN, ...)

## S3 method for class 'ts':
aggregate(x, nfrequency = 1, FUN = sum, ndeltat = 1,
ts.eps = getOption("ts.eps"), ...)

### Arguments

 x an R object. by a list of grouping elements, each as long as the variables in x. Names for the grouping variables are provided if they are not given. The elements of the list will be coerced to factors (if they are not already factors). FUN a scalar function to compute the summary statistics which can be applied to all data subsets. nfrequency new number of observations per unit of time; must be a divisor of the frequency of x. ndeltat new fraction of the sampling period between successive observations; must be a divisor of the sampling interval of x. ts.eps tolerance used to decide if nfrequency is a sub-multiple of the original frequency. ... further arguments passed to or used by methods.

### Details

aggregate is a generic function with methods for data frames and time series.

The default method aggregate.default uses the time series method if x is a time series, and otherwise coerces x to a data frame and calls the data frame method.

aggregate.data.frame is the data frame method. If x is not a data frame, it is coerced to one. Then, each of the variables (columns) in x is split into subsets of cases (rows) of identical combinations of the components of by, and FUN is applied to each such subset with further arguments in ... passed to it. (I.e., tapply(VAR, by, FUN, ..., simplify = FALSE) is done for each variable VAR in x, conveniently wrapped into one call to lapply().) Empty subsets are removed, and the result is reformatted into a data frame containing the variables in by and x. The ones arising from by contain the unique combinations of grouping values used for determining the subsets, and the ones arising from x the corresponding summary statistics for the subset of the respective variables in x.

aggregate.ts is the time series method. If x is not a time series, it is coerced to one. Then, the variables in x are split into appropriate blocks of length frequency(x) / nfrequency, and FUN is applied to each such block, with further (named) arguments in ... passed to it. The result returned is a time series with frequency nfrequency holding the aggregated values.

Kurt Hornik

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

### Examples

## Compute the averages for the variables in 'state.x77', grouped
## according to the region (Northeast, South, North Central, West) that
## each state belongs to.
aggregate(state.x77, list(Region = state.region), mean)

## Compute the averages according to region and the occurrence of more
## than 130 days of frost.
aggregate(state.x77,
list(Region = state.region,
Cold = state.x77[,"Frost"] > 130),
mean)
## (Note that no state in 'South' is THAT cold.)

## Compute the average annual approval ratings for American presidents.
aggregate(presidents, nf = 1, FUN = mean)
## Give the summer less weight.
aggregate(presidents, nf = 1, FUN = weighted.mean, w = c(1, 1, 0.5, 1))

[Package stats version 2.1.0 Index]