fdth (version 1.3-0)

fdt_cat: Frequency distribution table for categorical data

Description

A S3 set of methods to easily perform categorical frequency distribution table (fdt_cat) from vector, data.frame and matrix objects.

Usage

## S3 generic
fdt_cat(x, ...)
## S3 methods
# S3 method for default
fdt_cat(x,
        sort=TRUE,
        decreasing=TRUE, ...)
# S3 method for data.frame
fdt_cat(x,
        by,
        sort=TRUE,
        decreasing=TRUE, ...) 
# S3 method for matrix
fdt_cat(x,
        sort=TRUE,
        decreasing=TRUE, ...)

Value

For fdt_cat the method fdt_cat.default returns a data.frame storing the fdt.

The methods fdt_cat.data.frame and fdt_cat.matrix

return a list of class fdt_cat..multiple. This list has one slot for each categorical variable of the supplied x. Each slot, corresponding to each categorical variable, stores the same slots of the fdt_cat.default described above.

Arguments

x

a vector, data.frame or matrix object. If x is data.frame or matrix it must contain at least one character/factor column.

by

categorical variable used for grouping each categorical response, useful only on data.frame.

sort

logical. Should the fdt_cat be sorted by the absolute frequency into ascending or descending order? (default = TRUE).

decreasing

logical. Should the sort order be increasing or decreasing? (default = TRUE).

...

optional further arguments (required by generic).

Author

Faria, J. C.
Allaman, I. B
Jelihovschi, E. G.

Details

The simplest way to run fdt_cat is supplying only the x object, for example: ct <- fdt_cat(x). In this case all necessary default values (sort = TRUE and decreasing = TRUE) will be used.

These options make the fdt_cat very easy and flexible.

The fdt_cat object stores information to be used by methods summary, print, plot and mfv. The result of plot is a bar plot. The methods summary.fdt_cat, print.fdt_cat and plot.fdt_cat provide a reasonable set of parameters to format and plot the fdt_cat object in a pretty (and publishable) way.

See Also

hist provided by graphics and table, cut both provided by base.

Examples

library(fdth)

# Categorical
x <- sample(x=letters[1:5],
            size=5e2,
            rep=TRUE)

table(x)

(ft.c <- fdt_cat(x))

(ft.c <- fdt_cat(x,
                 sort=FALSE))

#================================================
# Data.frame: multivariated with two categorical
#================================================
mdf <- data.frame(c1=sample(LETTERS[1:3], 1e2, rep=TRUE),
                  c2=as.factor(sample(1:10, 1e2, rep=TRUE)),
                  n1=c(NA, NA, rnorm(96, 10, 1), NA, NA),
                  n2=rnorm(100, 60, 4),
                  n3=rnorm(100, 50, 4),
                  stringsAsFactors=TRUE)

head(mdf)

(ft.c <- fdt_cat(mdf))

(ft.c <- fdt_cat(mdf,
                 dec=FALSE))

(ft.c <- fdt_cat(mdf,
                 sort=FALSE))

(ft.c <- fdt_cat(mdf,
                 by='c1'))

#================================================
# Matrix: two categorical
#================================================
x <- matrix(sample(x=letters[1:10],
                   size=100,
                   rep=TRUE),
            nc=2,
            dimnames=list(NULL,
                          c('c1', 'c2')))

head(x)

(ft.c <- fdt_cat(x))