Compute Summary Statistics of Data Subsets
Splits the data into subsets, computes summary statistics for each, and returns the result in a convenient form.
aggregate(x, ...)"aggregate"(x, ...)"aggregate"(x, by, FUN, ..., simplify = TRUE, drop = TRUE)"aggregate"(formula, data, FUN, ..., subset, na.action = na.omit)"aggregate"(x, nfrequency = 1, FUN = sum, ndeltat = 1, ts.eps = getOption("ts.eps"), ...)
- an R object.
- a list of grouping elements, each as long as the variables
in the data frame
x. The elements are coerced to factors before use.
- a function to compute the summary statistics which can be applied to all data subsets.
- a logical indicating whether results should be simplified to a vector or matrix if possible.
- a logical indicating whether to drop unused combinations of grouping values.
- a formula, such as
y ~ xor
cbind(y1, y2) ~ x1 + x2, where the
yvariables are numeric data to be split into groups according to the grouping
xvariables (usually factors).
- a data frame (or list) from which the variables in formula should be taken.
- an optional vector specifying a subset of observations to be used.
- a function which indicates what should happen when
the data contain
NAvalues. The default is to ignore missing values in the given variables.
- new number of observations per unit of time; must
be a divisor of the frequency of
- new fraction of the sampling period between
successive observations; must be a divisor of the sampling
- tolerance used to decide if
nfrequencyis a sub-multiple of the original frequency.
- 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
x is a time series, and otherwise coerces
to a data frame and calls the data frame method.
aggregate.data.frame is the data frame method. If
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
split into subsets of cases (rows) of identical combinations of the
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
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
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.ts is the time series method, and requires
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)
... 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
c("mts", "ts").For the data frame method, a data frame with columns corresponding to the grouping variables in
byfollowed by aggregated columns from
x. If the
byhas names, the non-empty times are used to label the columns in the results, with unnamed grouping variables being named
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
## 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))