Convert a table with counts to a matrix or data.frame representing those counts.

Some historical sets are reported as summary tables of counts in a limited number of bins. Transforming these tables to data.frames representing the original values is useful for pedagogical purposes. (E.g., transforming the original Galton table of height x cubits in order to demonstrate regression.) The column and row names must be able to be converted to numeric values.

table2matrix(x, labs = NULL)
table2df(x, count=NULL,labs = NULL)

Arguments

x

A two dimensional table of counts with row and column names that can be converted to numeric values.
count

if present, then duplicate each row count times
labs

Labels for the rows and columns. These will be used for the names of the two columns of the resulting matrix

Details

The original Galton (1888) of heights by cubits (arm length) is in tabular form. To show this as a correlation or as a scatter plot, it is useful to convert the table to a matrix or data frame of two columns.

This function may also be used to convert an item response pattern table into a data table. e.g., the Bock data set bock.

Value

A matrix (or data.frame) of sum(x) rows and two columns.

See also

cubits and bock data sets

Examples

data(cubits)
cubit <- table2matrix(cubits,labs=c("height","cubit"))
describe(cubit)
#>        vars   n  mean   sd median trimmed  mad  min   max range  skew kurtosis
#> height    1 348 67.07 2.36  67.00   67.11 2.97 63.0 71.00  8.00 -0.09    -0.92
#> cubit     2 348 18.10 0.78  18.25   18.11 0.74 16.5 19.75  3.25 -0.09    -0.60
#>          se
#> height 0.13
#> cubit  0.04
ellipses(cubit,n=1)
data(bock)
responses <- table2df(bock.table[,2:6],count=bock.table[,7],labs= paste("lsat6.",1:5,sep=""))
describe(responses)
#>         vars    n mean   sd median trimmed mad min max range  skew kurtosis
#> lsat6.1    1 1000 0.92 0.27      1    1.00   0   0   1     1 -3.20     8.22
#> lsat6.2    2 1000 0.71 0.45      1    0.76   0   0   1     1 -0.92    -1.16
#> lsat6.3    3 1000 0.55 0.50      1    0.57   0   0   1     1 -0.22    -1.95
#> lsat6.4    4 1000 0.76 0.43      1    0.83   0   0   1     1 -1.24    -0.48
#> lsat6.5    5 1000 0.87 0.34      1    0.96   0   0   1     1 -2.17     2.72
#>           se
#> lsat6.1 0.01
#> lsat6.2 0.01
#> lsat6.3 0.02
#> lsat6.4 0.01
#> lsat6.5 0.01