Collect Objects — collect • memisc

collect gathers several objects into one, matching the elements or subsets of the objects by names or dimnames.

Usage

collect(...,names=NULL,inclusive=TRUE)
# Default S3 method
collect(...,names=NULL,inclusive=TRUE)
# S3 method for class 'array'
collect(...,names=NULL,inclusive=TRUE)
# S3 method for class 'matrix'
collect(...,names=NULL,inclusive=TRUE)
# S3 method for class 'table'
collect(...,names=NULL,sourcename=".origin",fill=0)
# S3 method for class 'data.frame'
collect(...,names=NULL,inclusive=TRUE,
                                  fussy=FALSE,warn=TRUE,
                                  detailed.warnings=FALSE,use.last=FALSE,
                                  sourcename=".origin")
# S3 method for class 'data.set'
collect(...,names=NULL,inclusive=TRUE,
                                  fussy=FALSE,warn=TRUE,
                                  detailed.warnings=FALSE,use.last=FALSE,
                                  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?
detailed.warnings: logical, whether the attributes of each variable should be printed if they differ, and if warn or fuzzy is TRUE.
use.last: logical, defaults to FALSE. If the function is applied to data frames or similar objects, attributes of variables may differ between data frames (or other objects, respectively). If this argument is TRUE, then the attributes are harmonised based on the variables in the last data frame/object, otherwise the attributes of variables in the first data frame/object are used for harmonisation.
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

x <- c(a=1,b=2)
y <- c(a=10,c=30)

x
#> a b 
#> 1 2 
y
#>  a  c 
#> 10 30 

collect(x,y)
#>    x  y
#> a  1 10
#> b  2 NA
#> c NA 30
collect(x,y,inclusive=FALSE)
#>   x  y
#> a 1 10

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
#>   A B
#> a 1 1
#> b 1 1
Y
#>   A B
#> a 2 2
#> b 2 2
#> c 2 2
Z
#>   A B C
#> a 3 3 3
#> b 3 3 3

collect(X,Y,Z)
#> , , X
#> 
#>    A  B  C
#> a  1  1 NA
#> b  1  1 NA
#> c NA NA NA
#> 
#> , , Y
#> 
#>   A B  C
#> a 2 2 NA
#> b 2 2 NA
#> c 2 2 NA
#> 
#> , , Z
#> 
#>    A  B  C
#> a  3  3  3
#> b  3  3  3
#> c NA NA NA
#> 
collect(X,Y,Z,inclusive=FALSE)
#> , , X
#> 
#>   A B
#> a 1 1
#> b 1 1
#> 
#> , , Y
#> 
#>   A B
#> a 2 2
#> b 2 2
#> 
#> , , Z
#> 
#>   A B
#> a 3 3
#> b 3 3
#> 

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)
#> , , X
#> 
#>    A  B  C
#> a  1  1 NA
#> b  1  1 NA
#> c NA NA NA
#> 
#> , , Y
#> 
#>   A B  C
#> a 2 2 NA
#> b 2 2 NA
#> c 2 2 NA
#> 
#> , , Z
#> 
#>    A  B  C
#> a  3  3  3
#> b  3  3  3
#> c NA NA NA
#> 
collect(X,Y,Z,inclusive=FALSE)
#> , , X
#> 
#>   A B
#> a 1 1
#> b 1 1
#> 
#> , , Y
#> 
#>   A B
#> a 2 2
#> b 2 2
#> 
#> , , Z
#> 
#>   A B
#> a 3 3
#> b 3 3
#> 

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)
#>    a b  c .origin
#> 1  1 1 NA     df1
#> 2  1 1 NA     df1
#> 3  1 1 NA     df1
#> 4  1 1 NA     df1
#> 5  1 1 NA     df1
#> 6  2 2  2     df2
#> 7  2 2  2     df2
#> 8  2 2  2     df2
#> 9  2 2  2     df2
#> 10 2 2  2     df2
collect(df1,df2,inclusive=FALSE)
#>    a b .origin
#> 1  1 1     df1
#> 2  1 1     df1
#> 3  1 1     df1
#> 4  1 1     df1
#> 5  1 1     df1
#> 6  2 2     df2
#> 7  2 2     df2
#> 8  2 2     df2
#> 9  2 2     df2
#> 10 2 2     df2

data(UCBAdmissions)
Male <- as.table(UCBAdmissions[,1,])
Female <- as.table(UCBAdmissions[,2,])
collect(Male,Female,sourcename="Gender")
#> , , Gender = Male
#> 
#>           Dept
#> Admit        A   B   C   D   E   F
#>   Admitted 512 353 120 138  53  22
#>   Rejected 313 207 205 279 138 351
#> 
#> , , Gender = Female
#> 
#>           Dept
#> Admit        A   B   C   D   E   F
#>   Admitted  89  17 202 131  94  24
#>   Rejected  19   8 391 244 299 317
#> 
collect(unclass(Male),unclass(Female))
#> , , unclass(Male)
#> 
#>            A   B   C   D   E   F
#> Admitted 512 353 120 138  53  22
#> Rejected 313 207 205 279 138 351
#> 
#> , , unclass(Female)
#> 
#>           A  B   C   D   E   F
#> Admitted 89 17 202 131  94  24
#> Rejected 19  8 391 244 299 317
#> 

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")
#> , , Gender = Male
#> 
#>           Dept
#> Admit        B   C   D   E   F   A
#>   Admitted 353 120 138  53  22   0
#>   Rejected 207 205 279 138 351   0
#> 
#> , , Gender = Female
#> 
#>           Dept
#> Admit        B   C   D   E   F   A
#>   Admitted   0 202 131  94  24  89
#>   Rejected   0 391 244 299 317  19
#> 
collect(Male=Male1,Female=Female3,sourcename="Gender")
#> , , Gender = Male
#> 
#>           Dept
#> Admit        B   C   D   E   F   A
#>   Admitted 353 120 138  53  22   0
#>   Rejected 207 205 279 138 351   0
#> 
#> , , Gender = Female
#> 
#>           Dept
#> Admit        B   C   D   E   F   A
#>   Admitted  17   0 131  94  24  89
#>   Rejected   8   0 244 299 317  19
#> 
collect(Male=Male1,Female=Female3,sourcename="Gender",fill=NA)
#> , , Gender = Male
#> 
#>           Dept
#> Admit        B   C   D   E   F A
#>   Admitted 353 120 138  53  22  
#>   Rejected 207 205 279 138 351  
#> 
#> , , Gender = Female
#> 
#>           Dept
#> Admit        B C   D   E   F   A
#>   Admitted  17   131  94  24  89
#>   Rejected   8   244 299 317  19
#> 

f1 <- gl(3,5,labels=letters[1:3])
f2 <- gl(3,6,labels=letters[1:3])
collect(f1=table(f1),f2=table(f2))
#>     .origin
#> Freq f1 f2
#>    a  5  6
#>    b  5  6
#>    c  5  6

ds1 <- data.set(x = 1:3)
ds2 <- data.set(x = 4:9,
                y = 1:6)
collect(ds1,ds2)
#> 
#> Data set with 9 observations and 3 variables
#> 
#>   x  y .origin
#> 1 1 NA     ds1
#> 2 2 NA     ds1
#> 3 3 NA     ds1
#> 4 4  1     ds2
#> 5 5  2     ds2
#> 6 6  3     ds2
#> 7 7  4     ds2
#> 8 8  5     ds2
#> 9 9  6     ds2