R: Function to calculate groupwise summary statistics

summaryBy {doBy} | R Documentation

## Function to calculate groupwise summary statistics

### Description

Function to calculate groupwise summary statistics, much like the summary procedure of SAS

### Usage

    summaryBy(formula, data = parent.frame(), id = NULL, FUN = mean,
              keep.names=FALSE, p2d=FALSE, order=TRUE, full.dimension=FALSE,
              var.names=NULL, fun.names=NULL, ...)

### Arguments

`formula` | A formula object, see examples below
`data` | A data frame

`id` | A formula specifying variables which data are not grouped by but which should appear in the output. See examples below.
`FUN` | A list of functions to be applied, see examples below.
`keep.names` | If TRUE and if there is only ONE function in FUN, then the variables in the output will have the same name as the variables in the input, see 'examples'.

`p2d` | Should parentheses in output variable names be replaced by dots?
`order` | Should the resulting dataframe be ordered according to the variables on the right hand side of the formula? (using orderBy
`full.dimension` | If TRUE then rows of summary statistics are repeated such that the result will have the same number of rows as the input dataset.
`var.names` | Option for user to specify the names of the variables on the left hand side.
`fun.names` | Option for user to specify function names to apply to the variables on the left hand side.
`...` | Additional arguments to FUN. This could for example be NA actions.

### Details

Extra arguments ('...') are passed onto the functions in FUN. Hence care must be taken that all functions in FUN accept these arguments - OR one can explicitly write a functions which get around this. This can particularly be an issue in connection with handling NAs. See examples below.

Some code for this function has been suggested by Jim Robison-Cox.

### Value

A data frame

### Author(s)

Søren Højsgaard, sorenh@math.aau.dk

### See Also

`ave`, `descStat`, `lapplyBy`, `orderBy`, `scaleBy`, `splitBy`, `transformBy`,

### Examples

    data(dietox)
    dietox12    <- subset(dietox,Time==12)

    summaryBy(Weight+Feed~Evit+Cu, data=dietox12,
              FUN=c(mean,var,length))

    summaryBy(list(c("Weight","Feed"), c("Evit","Cu")), data=dietox12,
              FUN=c(mean,var,length))

    summaryBy(Weight+Feed~Evit+Cu+Time, data=subset(dietox,Time>1),
       FUN=c(mean,var,length))

    ## Calculations on transformed data:

    summaryBy(log(Weight)+Feed~Evit+Cu, data=dietox12)

    ## Calculations on all numerical variables (not mentioned elsewhere):

    summaryBy(.~Evit+Cu,                data=dietox12,
       id=~Litter, FUN=mean)

    ## There are missing values in the 'airquality' data, so we remove these
    ## before calculating mean and variance with 'na.rm=TRUE'. However the
    ## length function does not accept any such argument. Hence we get
    ## around this by defining our own summary function in which length is
    ## not supplied with this argument while mean and var are:

    sumfun <- function(x, ...){
      c(m=mean(x, ...), v=var(x, ...), l=length(x))
    }
    summaryBy(Ozone+Solar.R~Month, data=airquality, FUN=sumfun, na.rm=TRUE)

    ## Using '.' on the right hand side of a formula means to stratify by
    ## all variables not used elsewhere:

    data(warpbreaks)
    summaryBy(breaks ~ wool+tension, warpbreaks)
    summaryBy(breaks ~., warpbreaks)
    summaryBy(.~ wool+tension, warpbreaks)

    ## Keep the names of the variables (works only if FUN only returns one
    ## value):

    summaryBy(Ozone+Wind~Month, data=airquality,FUN=c(mean),na.rm=TRUE,
      keep.names=TRUE)

    ## Using full.dimension=TRUE

    ## Consider:
    summaryBy(breaks~wool, data=warpbreaks)
    ## Rows of result are replicated below
    summaryBy(breaks~wool, data=warpbreaks, full.dimension=TRUE)
    ## Notice: Previous result is effectively the same as
    with(warpbreaks, ave(breaks, wool))
    ## A possible application of full.dimension=TRUE is if we want to
    ## standardize (center and scale) data within groups:
    ss <- summaryBy(breaks~wool, data=warpbreaks, full.dimension=TRUE, FUN=c(mean,sd))
    (warpbreaks$breaks-ss$breaks.mean)/ss$breaks.sd

[Package doBy version 4.5-15 Index]