print: Print methods for fdt objects

In fdth: Frequency Distribution Tables, Histograms and Polygons

Print methods for fdt objects

Description

S3 methods to return a data.frame (the frequency distribution table - fdt) for fdt.default and fdt.multiple objects; data.frame (the frequency distribution table - fdt_cat) for fdt_cat.default and fdt_cat.multiple objects.

Usage

## S3 methods
## S3 method for class 'fdt.default'
print(x,
      columns = 1:6,
      round = 2,
      format.classes = FALSE,
      pattern = '%09.3e',
      row.names = FALSE,
      right = TRUE, ...)

## S3 method for class 'fdt.multiple'
print(x,
      columns = 1:6,
      round = 2,
      format.classes = FALSE,
      pattern = '%09.3e',
      row.names = FALSE,
      right = TRUE, ...)

## S3 method for class 'fdt_cat.default'
print(x,
      columns = 1:6,
      round = 2,
      row.names = FALSE,
      right = TRUE, ...)

## S3 method for class 'fdt_cat.multiple'
print(x,
      columns = 1:6,
      round = 2,
      row.names = FALSE,
      right = TRUE, ...)

Arguments

x	a ‘⁠fdt⁠’ object.
columns	a vector of integers to select columns of the data.frame table.
round	rounds ‘⁠fdt⁠’ columns to the specified number of decimal places (default 2).
format.classes	logical, if TRUE the first column of the data.frame table will be formatted using a regular expression. The default is “%09.3e”.
pattern	same as fmt in sprintf.
row.names	logical (or character vector), indicating whether (or what) row names should be printed. The default is FALSE.
right	logical, indicating whether or not strings should be right-aligned. The default is right-alignment.
...	potential further arguments (required by generic).

Details

For print.fdt, it is possible to select which columns of the table (a data.frame) will be shown, as well as the pattern of the first column, for print.fdt_cat it is only possible to select which columns of the table (a data.frame) will be shown. The columns are:

1. ‘⁠Class limits⁠’

2. ‘⁠f⁠’ - absolute frequency

3. ‘⁠rf⁠’ - relative frequency

4. ‘⁠rf(%)⁠’ - relative frequency, %

5. ‘⁠cf⁠’ - cumulative frequency

6. ‘⁠cf(%)⁠’ - cumulative frequency, %

The available parameters offer an easy and powerful way to format the ‘⁠fdt⁠’ for publications and other purposes.

Value

A single data.frame for fdt.default and fdt_cat.default, or multiple data.frames for fdt.multiple and fdt_cat.multiple.

Author(s)

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

Examples

library (fdth)

#=====================
# Vectors: univariate
#=====================
set.seed(1)

x <- rnorm(n = 1e3,
           mean = 5,
           sd = 1)

ft <- fdt(x)

str(ft)

ft

print(ft) # same result

print(ft,
      format = TRUE)      # It may not be what you want for publication!

print(ft,
      format = TRUE,
      pattern = '%.2f')   # Better, but can it be improved?

print(ft,
      col = c(1:2, 4, 6),
      format = TRUE, 
      pattern = '%.2f')   # Yes, it can!

range(x)                 # To inspect the range of x

print(fdt(x,
          start = 1,
          end = 9,
          h = 1),
      col = c(1:2, 4, 6),
      format = TRUE,
      pattern = '%d')     # Is it nice now?

ft[['table']]                        # Stores the frequency distribution table (fdt)
ft[['breaks']]                       # Stores the breaks of fdt
ft[['breaks']]['start']              # Stores the left value of the first class
ft[['breaks']]['end']                # Stores the right value of the last class
ft[['breaks']]['h']                  # Stores the class interval
as.logical(ft[['breaks']]['right'])  # Stores the right option

#======================================================
# Data.frames: multivariate with categorical variables
#======================================================
mdf <- data.frame(X1 = rep(LETTERS[1:4], 25),
                  X2 = as.factor(rep(1:10, 10)),
                  Y1 = c(NA,
                         NA,
                         rnorm(96,
                               10,
                               1),
                         NA,
                         NA),
                  Y2 = rnorm(100,
                             60,
                             4),
                  Y3 = rnorm(100,
                             50,
                             4),
                  Y4 = rnorm(100,
                             40,
                             4),
                  stringsAsFactors = TRUE)
                  
(ft <- fdt_cat(mdf))
                  
print(ft)

(ft <- fdt(mdf,
           na.rm = TRUE))

print(ft)

str(ft)

print(ft, # the s
      format = TRUE)

print(ft,
      format = TRUE,
      pattern = '%05.2f')  # regular expression

print(ft,
      col = c(1:2, 4, 6), 
      format = TRUE, 
      pattern = '%05.2f')

print(ft,
      col = c(1:2, 4, 6))

print(ft,
      col = c(1:2, 4, 6),
      format = TRUE,
      pattern = '%05.2f')

levels(mdf$X1)

print(fdt(mdf,
          k = 5,
          by = 'X1',
          na.rm = TRUE))

levels(mdf$X2)

print(fdt(mdf,
          breaks = 'FD',
          by = 'X2',
          na.rm = TRUE),
      round = 3)

print(fdt(mdf,
          k = 5,
          by = 'X2',
          na.rm = TRUE),
      format = TRUE,
      round = 3)

print(fdt(iris,
          k = 5),
      format = TRUE,
      patter = '%04.2f')

levels(iris$Species)

print(fdt(iris,
          k = 5,
          by = 'Species'),
      format = TRUE, 
      patter = '%04.2f')

#========================
# Matrices: multivariate
#========================
print(fdt(state.x77), 
      col = c(1:2, 4, 6),
      format = TRUE)

print(fdt(volcano,
          right = TRUE),
      col = c(1:2, 4, 6),
      round = 3,
      format = TRUE,
      pattern = '%05.1f')

fdth documentation built on May 26, 2026, 1:06 a.m.