Type:	Package
Title:	Laboratory of Teaching to Statistics and Mathematics
Version:	0.2.0
Description:	An educational package for teaching statistics and mathematics in both primary and higher education. The objective is to assist in the teaching/learning process, both for student study planning and teacher teaching strategies. The leem package aims to provide, in a simple yet in-depth manner, knowledge of statistics and mathematics to anyone who wants to study these areas of knowledge.
Depends:	R (≥ 4.1.0)
Imports:	tcltk, tkRplotR, tkrplot, manipulate, crayon, grDevices, graphics, utils, diagram, methods
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://bendeivide.github.io/project/leem/, https://github.com/bendeivide/leem
BugReports:	https://github.com/bendeivide/leem/issues
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-03-08 14:28:10 UTC; ben10
Author:	Ben Deivide [aut, cre], Andre Barboza [aut], Alexandre Celestino [ctb], Juliane Nassarala [ctb]
Maintainer:	Ben Deivide <ben.deivide@gmail.com>
Repository:	CRAN
Date/Publication:	2025-03-08 21:50:02 UTC

The Gumbel Distribution

Description

Density, distribution function, quantile function and random generation for the normal distribution with parameters: location and scale

Usage

dgumbel(x, location, scale)

pgumbel(q, location, scale, lower.tail = TRUE)

qgumbel(p, location = 0, scale = 1, lower.tail = TRUE)

Arguments

Details

The CDF of Gumbel distribution is:

F(x;\mu ,\beta )=e^{-e^{-(x-\mu )/\beta }}, \quad \mu \in \mathbf{R}, \beta > 0,

where \mu is location parameter (location) and \beta is scale parameter (scale). The PDF of Gumbel distribution is:

\frac{1}{\beta }e^{-(z+e^{-z})},

where z={\frac {x-\mu }{\beta }}. The quantile is:

\mu -\beta \ln(-\ln(p)), \quad 0 < p < 1.

Examples

# PDF
dgumbel(1, 0, 1)
# CDF
pgumbel(1, 0, 1)
# Quantile
qgumbel(0.2, 0, 1)

Cumulative distribution function

Description

P Compute the cumulative distribution function for multiple distributions

Usage

P(
  q,
  dist = "normal",
  lower.tail = TRUE,
  rounding = 5,
  porcentage = FALSE,
  gui = "plot",
  main = NULL,
  ...
)

Arguments

Details

The argument that can have length 2, when we use the functions that give us the probability regions, given by: %<X<%, %<=X<%, %<X<=%, %<=X<=%, %>X>%, %>X=>%, %>X=>% and %>=X=>%. The additional arguments represent the parameters of the distributions, that is:

Value

P returns the probability and its graphical representation. The result can be given as a percentage or not.

Examples

# Loading package
library(leem)
# Example 1 - t-Student distribution
## Not run: 
P(q = 2, dist = "t-student", df = 10)
P(q = 2, dist = "t-student", df = 10, gui = 'rstudio')
P(q = 2, dist = "t-student", df = 10, gui = 'tcltk')
P(-1 %<X<% 1, dist = "t-student", df = 10)

## End(Not run)
# Example 2 - Normal distribution
P(-2,  dist = "normal", mean = 3, sd = 2,
  main = expression(f(x) == (1 / sqrt(n * sigma^2)) *
  exp(-1/2 * (x - mu)^2/sigma^2)))

Quantile distribution function.

Description

Q Quantile function for multiple distributions.

Usage

Q(
  p,
  dist = "normal",
  lower.tail = TRUE,
  two.sided = FALSE,
  rounding = 2,
  gui = "plot",
  mfrow = c(1, 2),
  type = "both",
  ...
)

Arguments

Details

The expression of quantile function is given by:

Q(p)=\inf {x\in \mathbb{R}: p \le F(x)},

where p is the first argument of Q() and x its return value;

Value

Q returns the quantile and its graphical representation for a given distribution. The output is a vector.

Examples

# Attaching package
library(leem)
## Not run: 
Q(p = 0.8, dist = "normal", mean = 200, sd=30)

## End(Not run)

Regions of probability

Description

These binary operators return a vector of length 2, describing the desired probability region.

Usage

a %>x>% b

a %>X>% b

a %<X<% b

a %<x<% b

a %>=X>=% b

a %>=x>=% b

a %<=X<=% b

a %<=x<=% b

a %>=X>% b

a %>=x>% b

a %>X>=% b

a %>x>=% b

a %<=X<% b

a %<=x<% b

a %<X<=% b

a %<x<=% b

Arguments

Value

A vector of lenght 2.

Examples

# Example 1 - Discrete
2L %>x>% 5L
2L %>X>% 5L
2L %<X<% 5L
2L %<x<% 5L
2L %>=X>=% 5L
2L %>=x>=% 5L
2L %<=X<=% 5L
2L %<=x<=% 5L
2L %>=X>% 5L
2L %>=x>% 5L
2L %>x>=% 5L
2L %>X>=% 5L
2L %<=X<% 5L
2L %<=x<% 5L
2L %<X<=% 5L
2L %<x<=% 5L

# Example 2 - Continuous
2 %>x>% 5
2 %>X>% 5
2 %<X<% 5
2 %<x<% 5
2 %>=X>=% 5
2 %>=x>=% 5
2 %<=X<=% 5
2 %<=x<=% 5
2 %>=X>% 5
2 %>=x>% 5
2 %>x>=% 5
2 %>X>=% 5
2 %<=X<% 5
2 %<=x<% 5
2 %<X<=% 5
2 %<x<=% 5

Amplitude

Description

Compute the sample range

Usage

amplitude(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  amplitude(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  amplitude()

Plot of probability function of any discrete variable

Description

Help in building the plot of the probability function of any discrete variable

Usage

apf(x, p, main = NULL, xlab = NULL, ylab = NULL)

Arguments

Details

Consider the X distribution:

where p_X(x) and x are probability function and values of X. See Example 1.

Value

The output is plot of distribution function. See Example 1.

Examples

# Example 1
x <- 1:5
p <- c(0.23, 0.27,0.30, 0.12, 0.08)
apf(x, p)

Barplot graph

Description

Class method leem for generic barplot

Usage

## S3 method for class 'leem'
barplot(
  height,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  posx1 = 0,
  posx2 = 0,
  xang = 0,
  labels = NULL,
  ...
)

Arguments

Examples

library(graphics)
# Example 1 - Simple example
library(leem)
rep(1:5, 5:1) |>
  new_leem() |>
  barplot()
# Example 2 - Color bars
rep(1:5, 5:1) |>
  new_leem() |>
  barplot(barcol = heat.colors(5))
# Example 3 - Ordered data
library(leem)
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
 new_leem() |>
 tabfreq(ordered = c("basic", "high", "university")) |>
 barplot(xang = 15, posx2 = -0.2)
# Example 4 - Coerced to histogram
rnorm(100, 10, 2) |>
  new_leem(variable = 2) |>
  barplot(barcol = heat.colors(10))

Box plot

Description

Produce box-and-whisker plot(s) of leem class object and computes the necessary values for the development of the plot.

Usage

## S3 method for class 'leem'
boxplot(
  x,
  type = "rawdata",
  details = FALSE,
  horizontal = FALSE,
  coef = 1.5,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  col = rgb(0, 175, 239, maxColorValue = 255),
  ...
)

Arguments

Examples

library(leem)
# Example 1
x <- rnorm(30, 100, 2) |>
  new_leem(variable = 2) |>
  tabfreq()
boxplot(x, details = TRUE)
# Example 2
boxplot(x, type = "classes")

Plot of cumulative distribution function of any discrete variable

Description

Help in building the plot of the cumulative distribution function of any discrete variable

Usage

cdfd(x, fda, main = NULL, xlab = NULL, ylab = NULL)

Arguments

Details

Consider the X distribution:

where p_X(x) and x are probability function and values of X. Consider also the X distribution function:

F_X(x) = \left\{\begin{array}{ll} 0, & \textrm{if } x < 1;\\ 0.23, & \textrm{if } 1 \leq x < 2;\\ 0.50, & \textrm{if } 2 \leq x < 3;\\ 0.80, & \textrm{if } 3 \leq x < 4;\\ 0.92, & \textrm{if } 4 \leq x < 5;\\ 1.00 & \textrm{if } x \geq 5.\\ \end{array}\right.

This way, the cdfd function needs to consider only the vectors x <- 1:5 and fda <- c(0.23, 0.50, 0.80, 0.92, 1), that is, only the equality conditions for x. See Example 1.

Value

The output is plot of distribution function. See Example 1.

Examples

# Example 1
x <- 1:5
fda <- c(0.23, 0.5, 0.8, 0.92, 1)
cdfd(x, fda)

Coefficient of variation

Description

Compute the sample coeffient of variation

Usage

cv(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  cv()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  cv(grouped = FALSE)

Histogram graph

Description

Class method leem for generic hist

Usage

## S3 method for class 'leem'
hist(
  x,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  ...
)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> hist()

# Example 2
library(leem)
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
 new_leem() |>
 tabfreq(ordered = c("basic", "high", "university"))

Insert measures of position in plot

Description

Generic function that allows inserting measures of position in plots

Usage

insert(x, ...)

Arguments

Examples

# Example 1
library(leem)
set.seed(10)
rnorm(36, 100, 50) |>
 new_leem(variable = "continuous") |>
 tabfreq() |>
 hist() |>
 insert(
  lcol = "black",
  tcol = "purple",
  acol = "brown",
  parrow = 0.6,
  larrow = 0.6,
  ptext = 0.4,
  side = "left",
  lwd = 2,
  lwdarrow = 4
 )

Insert measures of position in plot

Description

Method of insert function

Usage

## S3 method for class 'leem'
insert(
  x,
  type = "mean",
  lty = 1,
  lcol = "black",
  tcol = lcol,
  acol = lcol,
  parrow = 0.5,
  larrow = 0.6,
  ptext = 0.06,
  side = "right",
  lwd = 2,
  lwdarrow = lwd,
  ...
)

Arguments

Value

No return value. This function adds elements to an existing plot.

Examples

# Example 1
library(leem)
set.seed(10)
rnorm(36, 100, 50) |>
 new_leem(variable = "continuous") |>
 tabfreq() |>
 hist() |>
 insert(
  lcol = "black",
  tcol = "purple",
  acol = "brown",
  parrow = 0.6,
  larrow = 0.6,
  ptext = 0.4,
  side = "left",
  lwd = 2,
  lwdarrow = 4
 )

Graphical User Interface for leem package

Description

leem A Graphical User Interface (GUI) for the leem package

Usage

leem(gui = TRUE)

Arguments

Value

leem presents GUI with various problems for the teaching of statistics and mathematics. The idea is to use this package to learn these subjects without necessarily programming in R

Examples

# Loading package
library(leem)
if (interactive()) {
  leem(gui = FALSE)
}

Mean absolute deviation

Description

Compute the sample mean absolute deviation

Usage

madev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  madev(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  madev()

Arithmetic mean Class method leem for the generic mean function

Description

Arithmetic mean Class method leem for the generic mean function

Usage

## S3 method for class 'leem'
mean(
  x,
  trim = 0,
  na.rm = FALSE,
  rounding = 2,
  grouped = TRUE,
  details = FALSE,
  ...
)

Arguments

Examples

# Example 1
set.seed(10)
x <- rnorm(36, 100, 50)
y <- rbinom(36, 10, 0.8)
y |> new_leem(variable = "discrete") |> tabfreq() |> mean()
x |> new_leem(variable = "continuous") |> tabfreq() |> mean()

Median absolute deviation

Description

Compute the sample median absolute deviation

Usage

medev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  medev(grouped = FALSE)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  medev()

Median value

Description

Class method leem for the generic median function

Usage

## S3 method for class 'leem'
median(x, na.rm = FALSE, rounding = 2, grouped = TRUE, details = FALSE, ...)

Arguments

Examples

library(leem)
library(stats)
# Examples
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> median()

Mode value

Description

Compute the sample mode.

Usage

mfreq(x, na.rm = FALSE, rounding = 2, grouped = TRUE, details = FALSE)

Arguments

Examples

library(leem)
# set.seed(10)
x <- rnorm(36, 100, 50)
set.seed(10)
y <- rbinom(36, 10, 0.8)
w <- rep(letters[1:4], 1:4)
(tab1 <- y |> new_leem(variable = "discrete") |> tabfreq())
(tab2 <- x |> new_leem(variable = "continuous") |> tabfreq())
(tab3 <- w |> new_leem(variable = "discrete") |> tabfreq())
y |> new_leem(variable = "discrete") |> tabfreq() |> mfreq()
x |> new_leem(variable = "continuous") |> tabfreq() |> mfreq()
w |> new_leem(variable = "discrete") |> tabfreq() |> mfreq()

Measures of position

Description

Compute all measures of position

Usage

mpos(
  x,
  trim = 0,
  na.rm = FALSE,
  rounding = 2,
  grouped = TRUE,
  details = FALSE,
  ...
)

Arguments

Details

The measures of position are: average, median and mode.

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  mpos()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  mpos(grouped = FALSE)

Mean standard error

Description

Compute the sample mean standard error

Usage

mstde(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
set.seed(10)
rpois(30, 2.5) |>
  new_leem() |>
  mstde(rounding = 4)
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  mstde(grouped = FALSE)

Constructor of object of leem class

Description

Function that assists other functions of leem package

Usage

new_leem(x, variable = "discrete")

Arguments

Value

The variable argument also allows using variable = 1 for categorical variable and variable = 2 for continuous variable.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
new_leem(x, variable = 1)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |> new_leem(variable = 2)

Ogive chart

Description

Generic function that plots the culmulative frequency curve.

Usage

ogive(x, ...)

## S3 method for class 'leem'
ogive(
  x,
  freq = "a",
  decreasing = FALSE,
  both = FALSE,
  bars = FALSE,
  histogram = FALSE,
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  histcol = barcol,
  barborder = "gray",
  histborder = barborder,
  type = "b",
  lpcol = "black",
  lwd = 2,
  pch = 19,
  lty = 2,
  ...
)

Arguments

Value

Ogive plot.

Examples

library(leem)
# Example 1 - Both ogives
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE)

# Example 2 - Insert barplot
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE, bars = TRUE)
# Example 3 - Insert histogram
rnorm(36, 100, 50) |> new_leem(variable = 2) |> tabfreq() |> ogive(both = TRUE, histogram = TRUE)

Pie Chart

Description

Draw a pie chart.

Usage

piechart(
  x,
  labels = NULL,
  col = heat.colors(5, 1),
  border = FALSE,
  main = NULL,
  ...
)

Arguments

Examples

library(leem)
# Example 1
school <- rep(c("high", "university", "basic"), 3:5)
x <- sample(school, 30, TRUE) |>
  new_leem() |>
  tabfreq(ordered = c("basic", "high", "university"))
# Example 2
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
x <- tabfreq(x)
piechart(x)

Frequency polygon Graph

Description

Generic function that plots the frequency polygon curve.

Usage

polyfreq(x, ...)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> polyfreq()

Frequency polygon Graph

Description

Plot the frequency polygon curve.

Usage

## S3 method for class 'leem'
polyfreq(
  x,
  freq = "a",
  type = "b",
  bars = TRUE,
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  barcol = "yellow",
  barborder = "gray",
  lpcol = "black",
  lwd = 2,
  pch = 19,
  lty = 2,
  ...
)

Arguments

Examples

# Example 1
library(leem)
rnorm(36, 100, 50) |> new_leem(variable = "continuous") |> tabfreq() |> polyfreq()

Undertanding the probability of the normal distribution

Description

Using a graphical visualization, it is possible to understand the probabilities involved in a normal distribution.

Usage

probnormal(
  a = 1,
  b = 2,
  col = "lightblue",
  mean = 0,
  sd = 1,
  type = 1,
  rounding = 4,
  zang = 0,
  xang = 0
)

Arguments

Details

• ⁠type = 1,2⁠: a and b must be greater than mean;

• ⁠type = 3,4⁠: a and b must be less than mean;

• ⁠type = 5,6⁠: a and b can be any real value.

Examples

## Not run: 
probnormal(type = 2)
probnormal(-1, 0, type = 3)
probnormal(-1, 0, type = 4)
probnormal(-1, 0, type = 5)
probnormal(-1, 2, type = 5)
probnormal(1, 2, type = 5)
probnormal(1, 2, type = 6)

## End(Not run)

Properties of the normal distribution

Description

Graphically it is possible to observe some properties of the normal distribution

Usage

propofnormal(col = "lightblue2", type = 1)

Arguments

Standard Deviation

Description

Compute the sample standard deviation

Usage

sdev(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  sdev()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  sdev(grouped = FALSE)

Undertanding a box plot

Description

Detailing of a box plot, showing the main information contained in this type of graph

Usage

showboxplot(horizontal = FALSE, col = rgb(0, 175, 239, maxColorValue = 255))

Arguments

Examples

library(leem)
# Example 1
showboxplot()

Distribution Function Properties

Description

Graphic presentation of properties for distribution function

Usage

showcdf(variable = "discrete", prop = NULL)

Arguments

Details

• prop = 1: \lim_{x\rightarrow\infty}F_X(x)=1 and \lim_{x\rightarrow -\infty}F_X(x)=0;

• prop = 2: F_X(x)\leq F_X(y), ~ x\leq y~\forall x,y \in \mathbb{R};

• prop = 3: \lim_{x_n\downarrow x}F_X(x_n)\downarrow F_X(x).

Examples

library(leem)
# Example 1
showcdf()

Understanding the Confiance Indice

Description

Detailing the confiance indice plot, showing the main information contained in this type of graph.

Usage

showci(dist = "normal", ci = "two.sided", main = NULL)

Arguments

Examples

library(leem)
# Example 1
showci()

Plot of interpretation about Kurtosis

Description

showkur Interpretation of kutosis

Usage

showkur()

Value

showkur returns a plot with the kurtosis characteristics.

Examples

# Loading package
library(leem)
## Not run: 
showkur()

## End(Not run)

Interpretation of location and scale parameters

Description

showpar Function that exemplifies the interpretation of location and scale parameters

Usage

showpar(gui = "rstudio")

Arguments

Details

The result of the showpar() call will interactively present a plot of the normal distribution showing the behavior of the location and scale parameters via RStudio. For showpar(gui = "tcltk") the result will be displayed in a tcltk interface.

Value

showpar returns an interactive plot.

Examples

# Loading package
library(leem)
## Not run: 
showpar()

## End(Not run)

Plot of interpretation about skewsness

Description

showskew Interpretation of asymmetry based on frequency distributions

Usage

showskew(mpos = FALSE)

Arguments

Examples

# Loading package
library(leem)
## Not run: 
showskew()

## End(Not run)

showtabnormal

Description

Detailing of the Ztable, showing the main information contained in this type of table.

Usage

showtabnormal(z)

Arguments

Examples

library(leem)
# Example 1
showtabnormal(1)

Skewness value

Description

Compute the skewness

Usage

skewness(
  x,
  type = "pearson",
  rounding = 2,
  na.rm = FALSE,
  details = FALSE,
  grouped = TRUE
)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  skewness()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  skewness(grouped = TRUE)

Stick chart

Description

Stick chart for discrete data

Usage

stickchart(
  x,
  freq = "a",
  bg = TRUE,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  grids = grid(col = "white"),
  bgcol = "gray",
  bgborder = NA,
  posx1 = 0,
  posx2 = 0,
  xang = 0,
  labels = NULL,
  lcol = "black",
  pcol = lcol,
  pty = 19,
  pwd = 3,
  lty = 1,
  lwd = 2,
  ...
)

Arguments

Value

The result of stickchart() is x object.

Examples

library(leem)
# Example 1
rbinom(30, 10, 0.4) |>
  new_leem() |>
  tabfreq() |>
  stickchart()
# Example 2
school <- rep(c("high", "university", "basic"), 3:5)
sample(school, 30, TRUE) |>
  new_leem() |>
  tabfreq(ordered = c("basic", "high", "university")) |>
  stickchart(xang = 15, posx2 = -0.5)

Frequency distribution table

Description

Generic function that allows you to tabulate continuous and categorical data (quantitative or qualitative) in frequency distribution. Depending on the nature of the data, they can be grouped into class ranges or not.

Usage

tabfreq(data, ...)

Arguments

Value

The result of tabfreq() is a list. This list has two elements: table and statistics. The first is the data frequency table, and the second represents some useful statistics for methods of leem class.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
tabfreq(x)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |>
  new_leem(variable = "continuous") |> tabfreq()

# Example 3
x <- rbinom(36, 10, 0.6)
# Constructor (object of leem class)
x <- new_leem(x, variable = "discrete")
tab <- tabfreq(x)
# Details
tab$table
tab$statistics

# Example 3 - ordered categories ("d","a", "b", "c")
w <- rep(letters[1:4], 1:4)
w |> new_leem(variable = "discrete") |> tabfreq(ordered = c("d","a", "b", "c"))

Frequency distribution table

Description

Allows you to tabulate continuous and categorical data (quantitative or qualitative) in frequency distribution. Depending on the nature of the data, they can be grouped into class ranges or not.

Usage

## S3 method for class 'leem'
tabfreq(
  data,
  k = NULL,
  na.rm = FALSE,
  ordered = NULL,
  namereduction = TRUE,
  ...
)

Arguments

Value

The result of tabfreq() is a list. This list has two elements: table and statistics. The first is the data frequency table, and the second represents some useful statistics for methods of leem class.

Examples

# Example 1
library(leem)
x <- rbinom(36, 10, 0.6)
x <- new_leem(x, variable = "discrete")
tabfreq(x)

# Example 2 (Pipe operator)
rnorm(36, 100, 4) |>
  new_leem(variable = "continuous") |> tabfreq()

# Example 3
x <- rbinom(36, 10, 0.6)
# Constructor (object of leem class)
x <- new_leem(x, variable = "discrete")
tab <- tabfreq(x)
# Details
tab$table
tab$statistics

# Example 3 - ordered categories ("d","a", "b", "c")
w <- rep(letters[1:4], 1:4)
w |> new_leem(variable = "discrete") |> tabfreq(ordered = c("d","a", "b", "c"))

Variance value

Description

Compute the sample variance

Usage

variance(x, rounding = 2, na.rm = FALSE, details = FALSE, grouped = TRUE)

Arguments

Examples

# Example 1: Poisson data
rpois(30, 2.5) |>
  new_leem() |>
  variance()
# Example 2: Normal data
rnorm(50, 100, 2.5) |>
  new_leem(variable = 2) |>
  variance(grouped = FALSE)