| aggregate {stats} | 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, ...)
## 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 |
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 |
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.
(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.
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.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.
Kurt Hornik
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")
## 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))