frameApply {gdata} | R Documentation

## Subset analysis on data frames

### Description

Apply a function to row subsets of a data frame.

### Usage

    frameApply(x, by=NULL, on=by[1], fun=function(xi) c(Count=nrow(xi)),
               subset=TRUE, simplify=TRUE, byvar.sep="\\$\\@\\$", ...)

### Arguments

`x` | a data frame
`by` | names of columns in `x` specifying the variables to use to form the subgroups. None of the `by` variables should have the name "sep" (you will get an error if one of them does; a bit of laziness in the code). Unused levels of the `by` variables will be dropped. Use `by = NULL` (the default) to indicate that all of the data is to be treated as a single (trivial) subgroup.
`on` | names of columns in `x` specifying columns over which `fun` is to be applied. These can include columns specified in `by`, (as with the default) although that is not usually the case.
`fun` | a function that can operate on data frames that are row subsets of `x[on]`. If `simplify = TRUE`, the return value of the function should always be either a try-error (see `try`), or a vector of fixed length (i.e. same length for every subset), preferably with named elements.
`subset` | logical vector (can be specified in terms of variables in data). This row subset of `x` is taken before doing anything else.
`simplify` | logical. If TRUE (the default), return value will be a data frame including the `by` columns and a column for each element of the return vector of `fun`. If FALSE, the return value will be a list, sometimes necessary for less structured output (see description of return value below).
`byvar.sep` | character. This can be any character string not found anywhere in the values of the `by` variables. The `by` variables will be pasted together using this as the separator, and the result will be used as the index to form the subgroups.
`...` | additional arguments to `fun`.

### Details

This function accomplishes something similar to `by`. The main difference is that `frameApply` is designed to return data frames and lists instead of objects of class 'by'. Also, `frameApply` works only on the unique combinations of the `by` that are actually present in the data, not on the entire cartesian product of the `by` variables. In some cases this results in great gains in efficiency, although `frameApply` is hardly an efficient function.

### Value

a data frame if `simplify = TRUE` (the default), assuming there is sufficiently structured output from `fun`. If `simplify = FALSE` and `by` is not NULL, the return value will be a list with two elements. The first element, named "by", will be a data frame with the unique rows of `x[by]`, and the second element, named "result" will be a list where the ith component gives the result for the ith row of the "by" element.

### Author(s)

Jim Rogers james.a.rogers@pfizer.com

### Examples

    data(ELISA, package="gtools")

    # Default is slightly unintuitive, but commonly useful:
    frameApply(ELISA, by = c("PlateDay", "Read"))

    # Wouldn't actually recommend this model! Just a demo:
    frameApply(ELISA, on = c("Signal", "Concentration"), by = c("PlateDay", "Read"),
               fun = function(dat) coef(lm(Signal ~ Concentration, data =
    dat)))

    frameApply(ELISA, on = "Signal", by = "Concentration",
               fun = function(dat, ...) {
                        x <- dat[[1]]
                        out <- c(Mean = mean(x, ...),
                                 SD = sd(x, ...),
                                 N = sum(!is.na(x)))
                      },
               na.rm = TRUE,
               subset = !is.na(Concentration))

[Package gdata version 2.18.0 Index]