Description Usage Arguments Details Value Author(s) See Also Examples

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 `NA`

s 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`

.

When the first argument is a vector and the `id`

is a data frame
(even with only one variable),
`reShape`

will produce a data frame, and the unique groups are
identified by combinations of the values of all variables in `id`

.
If a data frame `constant`

is specified, the variables in this data
frame are assumed to be constant within combinations of `id`

variables (if not, an arbitrary observation in `constant`

will be
selected for each group). A row of `constant`

corresponding to the
target `id`

combination is then carried along when creating the
data frame result.

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.

1 2 |

`x` |
a matrix or vector, or, when |

`...` |
other optional vectors, if |

`id` |
A numeric, character, category, or factor variable containing subject
identifiers, or a data frame of such variables that in combination form
groups of interest. Required if |

`colvar` |
A numeric, character, category, or factor variable containing column
identifiers. |

`base` |
vector of character strings containing base names of repeated measurements |

`reps` |
number of times variables named in |

`times` |
when |

`timevar` |
specifies the name of the time variable to create if |

`constant` |
a data frame with the same number of rows in |

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`

.

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. If
`x`

is a vector and `id`

is a data frame, a data frame will be
the result.

Frank Harrell

Department of Biostatistics

Vanderbilt University School of Medicine

[email protected]

`reshape`

, `as.vector`

,
`matrix`

, `dimnames`

,
`outer`

, `table`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ```
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'))
tab <- table(Opening, Solder)
tab
reShape(tab)
# attach(tab) # do further processing
# 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)
# Create a data frame, reshaping a long dataset in which groups are
# formed not just by subject id but by combinations of subject id and
# visit number. Also carry forward a variable that is supposed to be
# constant within subject-visit number combinations. In this example,
# it is not constant, so an arbitrary visit number will be selected.
w <- data.frame(id=c('a','a','a','a','b','b','b','d','d','d'),
visit=c( 1, 1, 2, 2, 1, 1, 2, 2, 2, 2),
k=c('A','A','B','B','C','C','D','E','F','G'),
var=c('x','y','x','y','x','y','y','x','y','z'),
val=1:10)
with(w,
reShape(val, id=data.frame(id,visit),
constant=data.frame(k), colvar=var))
# 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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