collect
Collect Objects
collect
gathers several objects into one, matching the
elements or subsets of the objects by names
or dimnames
.
Usage
collect(…,names=NULL,inclusive=TRUE)
# S3 method for default
collect(…,names=NULL,inclusive=TRUE)
# S3 method for array
collect(…,names=NULL,inclusive=TRUE)
# S3 method for matrix
collect(…,names=NULL,inclusive=TRUE)
# S3 method for table
collect(…,names=NULL,sourcename=".origin",fill=0)
# S3 method for data.frame
collect(…,names=NULL,inclusive=TRUE,
fussy=FALSE,warn=TRUE,sourcename=".origin")
# S3 method for data.set
collect(…,names=NULL,inclusive=TRUE,
fussy=FALSE,warn=TRUE,sourcename=".origin")
Arguments
- …
more atomic vectors, arrays, matrices, tables, data.frames or data.sets
- names
optional character vector; in case of the default and array methods, giving
dimnames
for the new dimension that identifies the collected objects; in case of the data.frame and data.set methods, levels of a factor indentifying the collected objects.- inclusive
logical, defaults to TRUE; should unmatched elements included? See details below.
- fussy
logical, defaults to FALSE; should it count as an error, if variables with same names of collected data.frames/data.sets have different attributes?
- warn
logical, defaults to TRUE; should an warning be given, if variables with same names of collected data.frames/data.sets have different attributes?
- sourcename
name of the factor that identifies the collected data.frames or data.sets
- fill
numeric; with what to fill empty table cells, defaults to zero, assuming the table contains counts
Value
If x
and all following … arguments are vectors of the same mode (numeric,character, or logical)
the result is a matrix with as many columns as vectors. If argument inclusive
is TRUE,
then the number of rows equals the number of names that appear at least once in each of the
vector names and the matrix is filled with NA
where necessary,
otherwise the number of rows equals the number of names that are present in all
vector names.
If x
and all … arguments are matrices or arrays of the same mode (numeric,character, or logical)
and \(n\) dimension the result will be a \(n+1\) dimensional array or table. The extend of the
\(n+1\)th dimension equals the number of matrix, array or table arguments,
the extends of the lower dimension depends on the inclusive
argument:
either they equal to the number of dimnames that appear at least once for each given
dimension and the array is filled with NA
where necessary,
or they equal to the number of dimnames that appear in all arguments
for each given dimension.
If x
and all … arguments are data frames or data sets, the
result is a data frame or data set.
The number of variables of the resulting data frame or data set depends on
the inclusive
argument. If it is true, the number of variables
equals the number of variables that appear in each of the arguments at least once
and variables are filled with NA
where necessary, otherwise the
number of variables equals the number of variables that are present in
all arguments.
Examples
# NOT RUN {
x <- c(a=1,b=2)
y <- c(a=10,c=30)
x
y
collect(x,y)
collect(x,y,inclusive=FALSE)
X <- matrix(1,nrow=2,ncol=2,dimnames=list(letters[1:2],LETTERS[1:2]))
Y <- matrix(2,nrow=3,ncol=2,dimnames=list(letters[1:3],LETTERS[1:2]))
Z <- matrix(3,nrow=2,ncol=3,dimnames=list(letters[1:2],LETTERS[1:3]))
X
Y
Z
collect(X,Y,Z)
collect(X,Y,Z,inclusive=FALSE)
X <- matrix(1,nrow=2,ncol=2,dimnames=list(a=letters[1:2],b=LETTERS[1:2]))
Y <- matrix(2,nrow=3,ncol=2,dimnames=list(a=letters[1:3],c=LETTERS[1:2]))
Z <- matrix(3,nrow=2,ncol=3,dimnames=list(a=letters[1:2],c=LETTERS[1:3]))
collect(X,Y,Z)
collect(X,Y,Z,inclusive=FALSE)
df1 <- data.frame(a=rep(1,5),b=rep(1,5))
df2 <- data.frame(a=rep(2,5),b=rep(2,5),c=rep(2,5))
collect(df1,df2)
collect(df1,df2,inclusive=FALSE)
data(UCBAdmissions)
Male <- as.table(UCBAdmissions[,1,])
Female <- as.table(UCBAdmissions[,2,])
collect(Male,Female,sourcename="Gender")
collect(unclass(Male),unclass(Female))
Male1 <- as.table(UCBAdmissions[,1,-1])
Female2 <- as.table(UCBAdmissions[,2,-2])
Female3 <- as.table(UCBAdmissions[,2,-3])
collect(Male=Male1,Female=Female2,sourcename="Gender")
collect(Male=Male1,Female=Female3,sourcename="Gender")
collect(Male=Male1,Female=Female3,sourcename="Gender",fill=NA)
f1 <- gl(3,5,labels=letters[1:3])
f2 <- gl(3,6,labels=letters[1:3])
collect(f1=table(f1),f2=table(f2))
ds1 <- data.set(x = 1:3)
ds2 <- data.set(x = 4:9,
y = 1:6)
collect(ds1,ds2)
# }