# aggregate

##### Compute Summary Statistics of Data Subsets

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

##### Usage

`aggregate(x, …)`# S3 method for default
aggregate(x, …)

# S3 method for data.frame
aggregate(x, by, FUN, …, simplify = TRUE, drop = TRUE)

# S3 method for formula
aggregate(formula, data, FUN, …,
subset, na.action = na.omit)

# S3 method for 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 available since R 3.3.0, and may change in some cases where unused combinations are still dropped.- 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.`

for
`i``by[[`

.`i`]]

##### References

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

##### See Also

##### Examples

`library(stats)`

```
## 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))
```

*Documentation reproduced from package stats, version 3.4.1, License: Part of R 3.4.1*

### Community examples

**mark@niemannross.com**at Dec 20, 2018 stats v3.5.2

[LinkedIn Learning Video](linkedin-learning.pxf.io/rweekly_aggregate) ```r # Description: Example file for aggregate # main idea: aggregate is R for SQL "group by" # grab some data to work with data("ChickWeight") # let's say I want the median weight of each chick # basic format aggregate(ChickWeight$weight, by=list(chkID = ChickWeight$Chick), FUN=median) aggregate(ChickWeight$weight, by=list(chkID = ChickWeight$Diet), FUN=median) # notice it isn't sorted # use ~ notation # ~ is for modeling. Left of ~ is "y". Right is model. so y ~ model # in other words, left of ~ is the result. right of ~ are selectors aggregate(weight ~ Chick, data=ChickWeight, median) # list() behaves differently than "~". median needs numeric data aggregate(weight ~ Chick + Diet, data=ChickWeight, median) # this works # this doesn't. But it should. Factors don't work with median. aggregate(x=ChickWeight, by=list(ChickID = ChickWeight$Chick, Dietary=ChickWeight$Diet), median) # convert factors to numeric str(fixedChickWeight) fixedChickWeight <- ChickWeight # make a copy of ChickWeight fixedChickWeight$Chick <- as.numeric(levels(ChickWeight$Chick)[ChickWeight$Chick]) fixedChickWeight$Diet <- as.numeric(levels(ChickWeight$Diet)[ChickWeight$Diet]) str(fixedChickWeight) #now this works aggregate(x=fixedChickWeight, by=list(ChickID = fixedChickWeight$Chick, Dietary=fixedChickWeight$Diet), median) # Alternatives to aggregate browseURL("http://dplyr.tidyverse.org/") browseURL("https://github.com/mnr/R-Language-Mini-Tutorials/blob/master/SQLdf.R") ```