bindData {gdata} | R Documentation

## Bind two data frames into a multivariate data frame

### Description

Usually data frames represent one set of variables and one needs to bind/join them for multivariate analysis. When `merge` is not the approriate solution, `bindData` might perform an appropriate binding for two data frames. This is especially usefull when some variables are measured once, while others are repeated.

### Usage

      bindData(x, y, common)

### Arguments

`x` | data.frame
`y` | data.frame
`common` | character, list of column names that are common to both input data frames

### Details

Data frames are joined in a such a way, that the new data frame has c + (n_1 - c) + (n_2 - c) columns, where c is the number of common columns, and n_1 and n_2 are the number of columns in the first and in the second data frame, respectively.

### Value

A data frame.

### Author(s)

Gregor Grojanc

### See Also

`merge`, `wideByFactor`

### Examples

    n1 <- 6
    n2 <- 12
    n3 <- 4
    ## Single trait 1
    num <- c(5:n1, 10:13)
    (tmp1 <- data.frame(y1=rnorm(n=n1),
                        f1=factor(rep(c("A", "B"), n1/2)),
                        ch=letters[num],
                        fa=factor(letters[num]),
                        nu=(num) + 0.5,
                        id=factor(num), stringsAsFactors=FALSE))

    ## Single trait 2 with repeated records, some subjects also in tmp1
    num <- 4:9
    (tmp2 <- data.frame(y2=rnorm(n=n2),
                        f2=factor(rep(c("C", "D"), n2/2)),
                        ch=letters[rep(num, times=2)],
                        fa=factor(letters[rep(c(num), times=2)]),
                        nu=c((num) + 0.5, (num) + 0.25),
                        id=factor(rep(num, times=2)), stringsAsFactors=FALSE))

    ## Single trait 3 with completely distinct set of subjects
    num <- 1:4
    (tmp3 <- data.frame(y3=rnorm(n=n3),
                        f3=factor(rep(c("E", "F"), n3/2)),
                        ch=letters[num],
                        fa=factor(letters[num]),
                        nu=(num) + 0.5,
                        id=factor(num), stringsAsFactors=FALSE))

    ## Combine all datasets
    (tmp12 <- bindData(x=tmp1, y=tmp2, common=c("id", "nu", "ch", "fa")))
    (tmp123 <- bindData(x=tmp12, y=tmp3, common=c("id", "nu", "ch", "fa")))

    ## Sort by subject
    tmp123[order(tmp123$ch), ]

[Package gdata version 2.18.0 Index]