discretize: Discretize continuous data.
In cdparcoord: Top Frequency-Based Parallel Coordinates

Description Usage Arguments Details Author(s) Examples

Converts continuous columns to discrete.

1	discretize(dataset, input = NULL, ndigs=2, nlevels=10, presumedFactor=FALSE)

`dataset`	Dataset to discretize, data frame/table.
`input`	Optional specification for partitioning, giving the number of partitions and labels for each partition. List of lists, one list per column to be converted. The outermost list indicates the columns to be converted, and each inner list holds the name of the column, the number of partitions, and a list of labels for each partition.
`ndigs`	Number of digits to retain in forming labels/values for the discretized data, if `input` is not supplied. E.g. if `ndigs` is 2 and the original datum is 38.12, it becomes 38.
`nlevels`	Number of partitions to form for each variable, if `input` is NULL.
`presumedFactor`	If TRUE, any variable having fewer than `nlevels` levels will be presumed to be an informal factor, and thus will not be discretized.

If input is not specified, each numeric column in the data will be discretized, with one exception: If a column is numeric but has fewer distinct values than nlevels, and if presumedFactor is TRUE, it is presumed to be an informal R factor and will not be converted. However, it is best to use makeFactor on such variables.

Norm Matloff <matloff@cs.ucdavis.edu>, Vincent Yang <vinyang@ucdavis.edu>, and Harrison Nguyen <hhnguy@ucdavis.edu>

    data(prgeng)
    pe <- prgeng[,c(1,3,5,7:9)]  # extract vars of interest
    pe25 <- pe[pe$wageinc < 250000,]  # delete extreme values
    pe25disc <- discretize(pe25)  # age, wageinc and wkswrkd discretized

    data(mlb)
    # extract the height, weight, age, and position of players
    m <- mlb[,4:7]

    inp1 <- list("name" = "Height",
                 "partitions"=4,
                 "labels"=c("short", "shortmid", "tallmid", "tall"))

    inp2 <- list("name" = "Weight",
                 "partitions"=3,
                 "labels"=c("light", "med", "heavy"))

    inp3 <- list("name" = "Age",
                 "partitions"=3,
                 "labels"=c("young", "med", "old"))

    # create one list to pass everything to discretize()
    discreteinput <- list(inp1, inp2, inp3)
    head(discreteinput)

    # at this point, all of the data has been discretized
    discretizedmlb <- discretize(m, discreteinput)
    head(discretizedmlb)