# aggregate: 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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"), ...) ```

## Arguments

 `x` an R object. `by` a list of grouping elements, each as long as the variables in the data frame `x`. The elements are coerced to factors before use. `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 `drop=FALSE` has been amended for R 3.5.0 to drop unused combinations. `formula` a formula, such as `y ~ x` or `cbind(y1, y2) ~ x1 + x2`, where the `y` variables are numeric data to be split into groups according to the grouping `x` variables (usually factors). `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 `NA` values. The default is to ignore missing values in the given variables. `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. 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.

## 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]]`.

## Author(s)

Kurt Hornik, with contributions by Arni Magnusson.

## References

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

`apply`, `lapply`, `tapply`.
 ``` 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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47``` ```## 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)) ```