Apply a Function to a Variable Within Factor Levels

Applies a function, typically to compute a single statistic, like a mean, median, or standard deviation, within levels of a factor or within combinations of levels of two or more factors to produce a table of statistics. This function provides a formula interface to the standard R tapply function.

Usage

Tapply(formula, fun, data, na.action = na.pass, ..., targs = list())

Arguments

formula: a two-sided formula of the form variable ~ factor.1 + factor.2 + ... + factor.n or, equivalently, variable ~ factor.1*factor.2* ... *factor.n. The variables on the right-hand side of the formula are normally factors or are otherwise coerced to factors.
fun: a function, like mean, median, or sd, that takes a vector first argument and typically returns a single number as its value.
data: an optional data frame within which to find the variable and factor(s).
na.action: a function to handle missing values, as in statistical modeling functions like lm; the default is na.pass.
...: arguments to pass to the function given in the fun argument, such as (commonly) na.rm=TRUE.
targs: a list of optional arguments to pass to tapply.

Details

The function given by fun is applied to the values of the left-hand-side variable in formula within (combination of) levels of the factor(s) given in the right-hand side of formula, producing a table of statistics.

Value

The object returned by tapply, typically simply printed.

Author

John Fox jfox@mcmaster.ca

References

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition. Sage.

Examples

Tapply(conformity ~ partner.status + fcategory, mean, data=Moore)
#>               fcategory
#> partner.status     high  low   medium
#>           high 11.85714 17.4 14.27273
#>           low  12.62500  8.9  7.25000
Tapply(conformity ~ partner.status + fcategory, mean, data=Moore, 
    trim=0.2)
#>               fcategory
#> partner.status     high       low medium
#>           high 11.40000 17.333333  15.00
#>           low  12.16667  8.666667   7.25

Moore[1, 2] <- NA
Tapply(conformity ~ partner.status + fcategory, mean, data=Moore)
#>               fcategory
#> partner.status     high  low   medium
#>           high 11.85714 17.4 14.27273
#>           low  12.62500   NA  7.25000
Tapply(conformity ~ partner.status + fcategory, mean, data=Moore, 
  na.rm=TRUE)
#>               fcategory
#> partner.status     high  low   medium
#>           high 11.85714 17.4 14.27273
#>           low  12.62500  9.0  7.25000
Tapply(conformity ~ partner.status + fcategory, mean, data=Moore, 
  na.action=na.omit)  # equivalent
#>               fcategory
#> partner.status     high  low   medium
#>           high 11.85714 17.4 14.27273
#>           low  12.62500  9.0  7.25000
remove("Moore")