reShape {Hmisc} | R Documentation

## Reshape Matrices and Serial Data

### Description

If the first argument is a matrix, `reShape` strings out its values and creates row and column vectors specifying the row and column each element came from. This is useful for sending matrices to Trellis functions, for analyzing or plotting results of `table` or `crosstabs`, or for reformatting serial data stored in a matrix (with rows representing multiple time points) into vectors. The number of observations in the new variables will be the product of the number of rows and number of columns in the input matrix. If the first argument is a vector, the `id` and `colvar` variables are used to restructure it into a matrix, with NAs for elements that corresponded to combinations of `id` and `colvar` values that did not exist in the data. When more than one vector is given, multiple matrices are created. This is useful for restructuring irregular serial data into regular matrices. It is also useful for converting data produced by `expand.grid` into a matrix (see the last example). The number of rows of the new matrices equals the number of unique values of `id`, and the number of columns equals the number of unique values of `colvar`.

A different behavior of `reShape` is achieved when `base` and `reps` are specified. In that case `x` must be a list or data frame, and those data are assumed to contain one or more non-repeating measurements (e.g., baseline measurements) and one or more repeated measurements represented by variables named by pasting together the character strings in the vector `base` with the integers 1, 2, ..., `reps`. The input data are rearranged by repeating each value of the baseline variables `reps` times and by transposing each observation's values of one of the set of repeated measurements as `reps` observations under the variable whose name does not have an integer pasted to the end. if `x` has a `row.names` attribute, those observation identifiers are each repeated `reps` times in the output object. See the last example.

### Usage

    reShape(x, ..., id, colvar, base, reps, times=1:reps,
            timevar='seqno')

### Arguments

`x` | a matrix or vector, or, when `base` is specified, a list or data frame
`...` | other optional vectors, if `x` is a vector
`id` | A numeric, character, category, or factor variable containing subject identifiers. Required if `x` is a vector, ignored otherwise.
`colvar` | A numeric, character, category, or factor variable containing column identifiers. `colvar` is using a "time of data collection" variable. Required if `x` is a vector, ignored otherwise.
`base` | vector of character strings containing base names of repeated measurements
`reps` | number of times variables named in `base` are repeated. This must be a constant.
`times` | when `base` is given, `times` is the vector of times to create if you do not want to use consecutive integers beginning with 1.
`timevar` | specifies the name of the time variable to create if `times` is given, if you do not want to use `seqno`

### Details

In converting `dimnames` to vectors, the resulting variables are numeric if all elements of the matrix dimnames can be converted to numeric, otherwise the corresponding row or column variable remains character. When the `dimnames` if `x` have a `names` attribute, those two names become the new variable names. If `x` is a vector and another vector is also given (in `...`), the matrices in the resulting list are named the same as the input vector calling arguments. You can specify customized names for these on-the-fly by using e.g. `reShape(X=x, Y=y, id= , colvar= )`. The new names will then be `X` and `Y` instead of `x` and `y`. A new variable named `seqnno` is also added to the resulting object. `seqno` indicates the sequential repeated measurement number. When `base` and `times` are specified, this new variable is named the character value of `timevar` and the values are given by a table lookup into the vector `times`.

### Value

If `x` is a matrix, returns a list containing the row variable, the column variable, and the `as.vector(x)` vector, named the same as the calling argument was called for `x`. If `x` is a vector and no other vectors were specified as `...`, the result is a matrix. If at least one vector was given to `...`, the result is a list containing `k` matrices, where `k` one plus the number of vectors in `...`. If `x` is a list or data frame, the same type of object is returned.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
f.harrell@vanderbilt.edu

### See Also

`as.vector`, `matrix`, `dimnames`, `outer`, `table`

### Examples

    if(.R.) {
      set.seed(1)
      Solder  <- factor(sample(c('Thin','Thick'),200,TRUE),c('Thin','Thick'))
      Opening <- factor(sample(c('S','M','L'),  200,TRUE),c('S','M','L'))
    } else attach(solder[solder$skips > 10, ])
    tab <- table(Opening, Solder)
    tab
    reShape(tab)
    # attach(tab)  # do further processing

    if(!.R.) {
     g <- crosstabs( ~ Solder + Opening, data = solder, subset = skips > 10)
     rowpct <- 100*attr(g,'marginals')$"N/RowTotal"   # compute row pcts
     rowpct

     r <- reShape(rowpct)
     # note names "Solder" and "Opening" came originally from formula
     # given to crosstabs
     r
     dotplot(Solder ~ rowpct, groups=Opening, panel=panel.superpose, data=r)
    }

    # An example where a matrix is created from irregular vectors
    follow <- data.frame(id=c('a','a','b','b','b','d'),
                         month=c(1, 2,  1,  2,  3,  2),
                         cholesterol=c(225,226, 320,319,318, 270))
    follow
    attach(follow)
    reShape(cholesterol, id=id, colvar=month)
    detach('follow')
    # Could have done :
    # reShape(cholesterol, triglyceride=trig, id=id, colvar=month)

    # Get predictions from a regression model for 2 systematically
    # varying predictors.  Convert the predictions into a matrix, with
    # rows corresponding to the predictor having the most values, and
    # columns corresponding to the other predictor
    # d <- expand.grid(x2=0:1, x1=1:100)
    # pred <- predict(fit, d)
    # reShape(pred, id=d$x1, colvar=d$x2)  # makes 100 x 2 matrix

    # Reshape a wide data frame containing multiple variables representing
    # repeated measurements (3 repeats on 2 variables; 4 subjects)
    set.seed(33)
    n <- 4
    w <- data.frame(age=rnorm(n, 40, 10),
                    sex=sample(c('female','male'), n,TRUE),
                    sbp1=rnorm(n, 120, 15),
                    sbp2=rnorm(n, 120, 15),
                    sbp3=rnorm(n, 120, 15),
                    dbp1=rnorm(n,  80, 15),
                    dbp2=rnorm(n,  80, 15),
                    dbp3=rnorm(n,  80, 15), row.names=letters[1:n])
    options(digits=3)
    w

    u <- reShape(w, base=c('sbp','dbp'), reps=3)
    u
    reShape(w, base=c('sbp','dbp'), reps=3, timevar='week', times=c(0,3,12))

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