One-Dimensional Table of Frequences and/or Percentages

Table is a generic function that produces a table of counts or weighted counts and/or the corresponding percentages of an atomic vector, factor or "item.vector" object. This function is intended for use with Aggregate or genTable. The "item.vector" method is the workhorse of codebook.

Usage

# S4 method for atomic
Table(x,weights=NULL,counts=TRUE,percentage=FALSE,...)
# S4 method for factor
Table(x,weights=NULL,counts=TRUE,percentage=FALSE,...)
# S4 method for item.vector
Table(x,weights=NULL,counts=TRUE,percentage=(style=="codebook"),
              style=c("table","codebook","nolabels"),
              include.missings=(style=="codebook"),
              missing.marker=if(style=="codebook") "M" else "*",...)

Arguments

x: an atomic vector, factor or "item.vector" object
counts: logical value, should the table contain counts?
percentage: logical value, should the table contain percentages? Either the counts or the percentage arguments or both should be TRUE.
style: character string, the style of the names or rownames of the table.
weights: a numeric vector of weights of the same length as x.
include.missings: a logical value; should missing values included into the table?
missing.marker: a character string, used to mark missing values in the table (row)names.
...: other, currently ignored arguments.

Value

The atomic vector and factor methods return either a vector of counts or vector of percentages or a matrix of counts and percentages. The same applies to the "item.vector" vector method unless

include.missing=TRUE and percentage=TRUE, in which case total percentages and percentages of valid values are given.

Examples

  with(as.data.frame(UCBAdmissions),Table(Admit,Freq))
#> Admitted Rejected 
#>     1755     2771 
  Aggregate(Table(Admit,Freq)~.,data=UCBAdmissions)
#>    Gender Dept Admitted Rejected
#> 1    Male    A      512      313
#> 2  Female    A       89       19
#> 3    Male    B      353      207
#> 4  Female    B       17        8
#> 5    Male    C      120      205
#> 6  Female    C      202      391
#> 7    Male    D      138      279
#> 8  Female    D      131      244
#> 9    Male    E       53      138
#> 10 Female    E       94      299
#> 11   Male    F       22      351
#> 12 Female    F       24      317

  A <- sample(c(1:5,9),size=100,replace=TRUE)
  labels(A) <- c(a=1,b=2,c=3,d=4,e=5,dk=9)
  missing.values(A) <- 9
  Table(A,percentage=TRUE)
#>     Counts  Percent
#> a 22.00000 27.16049
#> b 11.00000 13.58025
#> c 13.00000 16.04938
#> d 14.00000 17.28395
#> e 21.00000 25.92593