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`.
`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, which must have a non-zero number of rows. 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`. Rows with missing values in any of the `by` variables will be omitted from the result.

`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. Note that this make most sense for a quarterly or yearly result when the original series covers a whole number of quarters or years: in particular aggregating a monthly series to quarters starting in February does not give a conventional quarterly series.

### Value

For the time series method, a time series of class `"ts"` or class `c("mts", "ts")`.
For the data frame method, a data frame with columns corresponding to the grouping variables in `by` followed by aggregated columns from `x`. If the `by` has names, the non-empty times are used to label the columns in the results, with unnamed grouping variables being named `Group.i` for `by[[i]]`.
Note: prior to R 2.6.0 the grouping variables were reported as factors with levels in alphabetical order in the current locale. Now the variable in the result is found by subsetting the original variable.

### Author(s)

Kurt Hornik

### References

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

### See Also

`apply`, `lapply`, `tapply`.

### 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.)

    ## example with character variables and NAs
    testDF <- data.frame(v1 = c(1,3,5,7,8,3,5,NA,4,5,7,9),
                         v2 = c(11,33,55,77,88,33,55,NA,44,55,77,99) )
    by1 <- c("red","blue",1,2,NA,"big",1,2,"red",1,NA,12)
    by2 <- c("wet","dry",99,95,NA,"damp",95,99,"red",99,NA,NA)
    aggregate(x = testDF, by = list(by1, by2), FUN = "mean")

    # and if you want to treat NAs as a group
    fby1 <- factor(by1, exclude = "")
    fby2 <- factor(by2, exclude = "")
    aggregate(x = testDF, by = list(fby1, fby2), FUN = "mean")

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

[Package stats version 2.8.1 Index]