aggregate | R Documentation |

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

aggregate(x, ...) ## Default S3 method: aggregate(x, ...) ## S3 method for class 'data.frame' aggregate(x, by, FUN, ..., simplify = TRUE, drop = TRUE) ## S3 method for class 'formula' aggregate(formula, data, FUN, ..., subset, na.action = na.omit) ## S3 method for class 'ts' aggregate(x, nfrequency = 1, FUN = sum, ndeltat = 1, ts.eps = getOption("ts.eps"), ...)

`x` |
an R object. |

`by` |
a list of grouping elements, each as long as the variables
in the data frame |

`FUN` |
a function to compute the summary statistics which can be applied to all data subsets. |

`simplify` |
a logical indicating whether results should be simplified to a vector or matrix if possible. |

`drop` |
a logical indicating whether to drop unused combinations
of grouping values. The non-default case |

`formula` |
a formula, such as |

`data` |
a data frame (or list) from which the variables in formula should be taken. |

`subset` |
an optional vector specifying a subset of observations to be used. |

`na.action` |
a function which indicates what should happen when
the data contain |

`nfrequency` |
new number of observations per unit of time; must
be a divisor of the frequency of |

`ndeltat` |
new fraction of the sampling period between
successive observations; must be a divisor of the sampling
interval of |

`ts.eps` |
tolerance used to decide if |

`...` |
further arguments passed to or used by methods. |

`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. 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 summaries for the
subset of the respective variables in `x`

. If `simplify`

is
true, summaries are simplified to vectors or matrices if they have a
common length of one or greater than one, respectively; otherwise,
lists of summary results according to subsets are obtained. Rows with
missing values in any of the `by`

variables will be omitted from
the result. (Note that versions of **R** prior to 2.11.0 required
`FUN`

to be a scalar function.)

`aggregate.formula`

is a standard formula interface to
`aggregate.data.frame`

.

`aggregate.ts`

is the time series method, and requires `FUN`

to be a scalar function. 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.

`FUN`

is passed to `match.fun`

, and hence it can be a
function or a symbol or character string naming a function.

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.`

for
`i``by[[`

.
`i`]]

In future, the first argument of the `"formula"`

method may be named
`x`

rather than `formula`

. Portable uses should not name
that argument.

Kurt Hornik, with contributions by Arni Magnusson.

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

`apply`

, `lapply`

, `tapply`

.

## 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") ## Formulas, one ~ one, one ~ many, many ~ one, and many ~ many: aggregate(weight ~ feed, data = chickwts, mean) aggregate(breaks ~ wool + tension, data = warpbreaks, mean) aggregate(cbind(Ozone, Temp) ~ Month, data = airquality, mean) aggregate(cbind(ncases, ncontrols) ~ alcgp + tobgp, data = esoph, sum) ## Dot notation: aggregate(. ~ Species, data = iris, mean) aggregate(len ~ ., data = ToothGrowth, mean) ## Often followed by xtabs(): ag <- aggregate(len ~ ., data = ToothGrowth, mean) xtabs(len ~ ., data = ag) ## 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))

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