fdt_cat {fdth}

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
## Default S3 method:
fdt_cat(x,
        sort=TRUE,
        decreasing=TRUE, ...)

## S3 method for class 'data.frame'
fdt_cat(x,
        by,
        sort=TRUE,
        decreasing=TRUE, ...) 

## S3 method for class 'matrix'
fdt_cat(x,
        sort=TRUE,
        decreasing=TRUE, ...)

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).

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.

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.

Author(s)

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

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))