Package 'UAHDataScienceSF'

Description

This function calculates the absolute accumulated frequency of a value in a numeric vector.

Usage

absolute_acum_frequency(v = NULL, x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The absolute accumulated frequency of x in v (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12,1,2,3,12)
value <- 12

# Simple calculation
absolute_acum_frequency(data, value)

# Learning mode
absolute_acum_frequency(data, value, learn = TRUE)

# Interactive mode
if(interactive()){
absolute_acum_frequency(interactive = TRUE)
}

Description

This function calculates the absolute frequency of a value in a numeric vector.

Usage

absolute_frequency(v = NULL, x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The absolute frequency of x in v (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12,1,2,3,12)
value <- 12

# Simple calculation
absolute_frequency(data, value)

# Learning mode
absolute_frequency(data, value, learn = TRUE)

# Interactive mode
if(interactive()){
absolute_frequency(interactive = TRUE)
}

Description

This function calculates the average absolute deviation of a numbers vector.

Usage

average_deviation(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The average absolute deviation of the vector (for non-interactive mode)

Examples

data <- c(7,2,5,7,1,4,12)

# Simple calculation
average_deviation(data)

# Learning mode
average_deviation(data, learn = TRUE)

# Interactive mode
if(interactive()){
average_deviation(interactive = TRUE)
}

Description

This function calculates the binomial distribution probability.

Usage

binomial_(n = NULL, x = NULL, p = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The binomial probability (for non-interactive mode)

Examples

n <- 3
x <- 2
p <- 0.7

# Simple calculation
binomial_(n, x, p)

# Learning mode
binomial_(n, x, p, learn = TRUE)

# Interactive mode
if(interactive()){
binomial_(interactive = TRUE)
}

Description

This function calculates the chi-squared statistic between two groups.

Usage

chisquared(x = NULL, y = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The chi-squared statistic (for non-interactive mode)

Examples

x <- c(70,75,74,72,68,59)
y <- c(74,77,70,80,72,76)

# Simple calculation
chisquared(x, y)

# Learning mode
chisquared(x, y, learn = TRUE)

# Interactive mode
if(interactive()){
chisquared(interactive = TRUE)
}

Description

This function calculates the covariance between two vectors of numbers.

Usage

covariance(x = NULL, y = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The covariance between the two vectors (for non-interactive mode)

Examples

data <- c(10,4,5,7,3,4,1)
data2 <- c(1,8,3,4,4,5,7)

# Simple calculation
covariance(data, data2)

# Learning mode
covariance(data, data2, learn = TRUE)

# Interactive mode
if(interactive()){
covariance(interactive = TRUE)
}

Description

This function calculates the coefficient of variation of a numbers vector.

Usage

cv(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The coefficient of variation of the vector (for non-interactive mode)

Examples

data <- c(10,4,5,7,3,4,1)

# Simple calculation
cv(data)

# Learning mode
cv(data, learn = TRUE)

# Interactive mode
if(interactive()){
cv(interactive = TRUE)
}

Description

This function prints all the elements of a vector

Usage

drawVector(buffer)

Arguments

Value

No return value, prints to screen

Examples

## Not run: 
data <- c(1:12)
drawVector(data)

## End(Not run)

Description

This function calculates the F statistic between two groups.

Usage

fisher(x = NULL, y = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The F statistic (for non-interactive mode)

Examples

x <- c(70,75,74,72,68,59)
y <- c(74,77,70,80,72,76)

# Simple calculation
fisher(x, y)

# Learning mode
fisher(x, y, learn = TRUE)

# Interactive mode
if(interactive()){
fisher(interactive = TRUE)
}

Description

This function calculates the geometric mean of a numeric vector. Can be used in three modes: simple calculation, learning mode with step-by-step explanation, or interactive mode for practice.

Usage

geometric_mean(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The geometric mean of the vector (for non-interactive mode)

Examples

data <- c(5,21,12,7,3,9,1)
# Simple calculation
geometric_mean(data)

# Learning mode
geometric_mean(data, learn = TRUE)

# Interactive mode
if(interactive()){
geometric_mean(interactive = TRUE)
}

Description

This function gets the buffer introduced by the user. Typically a numerical vector.

Usage

getUserAction()

Value

A vector entered by the user

Examples

## Not run: 
vector <- getUserAction()

## End(Not run)

Description

This function calculates the harmonic mean of a numbers vector.

Usage

harmonic_mean(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The harmonic mean of the vector (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12,1,2,3,12)

# Simple calculation
harmonic_mean(data)

# Learning mode
harmonic_mean(data, learn = TRUE)

# Interactive mode
if(interactive()){
harmonic_mean(interactive = TRUE)
}

Description

This function displays an image from the package resources.

Usage

initImages(image_name)

Arguments

Value

No return value

Description

This function calculates Laplace's Rule for a probability experiment.

Usage

laplace(x = NULL, y = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The probability according to Laplace's Rule (for non-interactive mode)

Examples

data <- 3
data2 <- c(1,2,3,4,5,6)

# Simple calculation
laplace(data, data2)

# Learning mode
laplace(data, data2, learn = TRUE)

# Interactive mode
if(interactive()){
laplace(interactive = TRUE)
}

Description

This function calculates the arithmetic mean of a numeric vector. Can be used in three modes: simple calculation, learning mode with step-by-step explanation, or interactive mode for practice.

Usage

mean_(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The arithmetic mean of the vector

Examples

# Simple calculation
data <- c(1,2,2,5,10,4,2)
mean_(data)

# Learning mode
mean_(data, learn = TRUE)

# Interactive mode
if(interactive()){
mean_(interactive = TRUE)
}

Description

This function calculates the median of a numbers vector.

Usage

median_(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The median of the vector (for non-interactive mode)

Examples

data <- c(1,3,2,5,12,4,4,2,9)

# Simple calculation
median_(data)

# Learning mode
median_(data, learn = TRUE)

# Interactive mode
if(interactive()){
median_(interactive = TRUE)
}

Description

This function calculates the mode of a numbers vector.

Usage

mode_(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The mode of the vector (for non-interactive mode)

Examples

data <- c(5,21,12,7,3,9,1)

# Simple calculation
mode_(data)

# Learning mode
mode_(data, learn = TRUE)

# Interactive mode
if(interactive()){
mode_(interactive = TRUE)
}

Description

This function calculates the normal distribution probability density.

Usage

normal(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The normal probability density (for non-interactive mode)

Examples

x <- 0.1

# Simple calculation
normal(x)

# Learning mode
normal(x, learn = TRUE)

# Interactive mode
if(interactive()){
normal(interactive = TRUE)
}

Description

This function calculates the Pearson correlation coefficient between two vectors of numbers.

Usage

pearson(x = NULL, y = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The Pearson correlation coefficient between the two vectors (for non-interactive mode)

Examples

data <- c(10,4,5,7,3,4,1)
data2 <- c(1,8,3,4,4,5,7)

# Simple calculation
pearson(data, data2)

# Learning mode
pearson(data, data2, learn = TRUE)

# Interactive mode
if(interactive()){
pearson(interactive = TRUE)
}

Description

This function calculates the percentiles of a numeric vector.

Usage

percentile(x = NULL, p = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The percentile value (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12)

# Simple calculation
percentile(data, 0.3)

percentile(data, 0.3, learn = TRUE)

if(interactive()){
percentile(interactive = TRUE)
}

Description

This function calculates the Poisson distribution probability.

Usage

poisson_(k = NULL, lam = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The Poisson probability (for non-interactive mode)

Examples

lam <- 2
k <- 3

# Simple calculation
poisson_(k, lam)

# Learning mode
poisson_(k, lam, learn = TRUE)

# Interactive mode
if(interactive()){
poisson_(interactive = TRUE)
}

Description

This function calculates the quartiles of a numeric vector.

Usage

quartile(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The quartiles of the vector (for non-interactive mode)

Examples

data <- c(1,2,2,5,10,4,2)

# Simple calculation
quartile(data)

# Learning mode
quartile(data, learn = TRUE)

# Interactive mode
if(interactive()){
quartile(interactive = TRUE)
}

Description

This function calculates the relative accumulated frequency of a value in a numeric vector.

Usage

relative_acum_frequency(v = NULL, x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The relative accumulated frequency of x in v (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12,1,2,3,12)
value <- 12

# Simple calculation
relative_acum_frequency(data, value)

# Learning mode
relative_acum_frequency(data, value, learn = TRUE)

# Interactive mode
if(interactive()){
relative_acum_frequency(interactive = TRUE)
}

Description

This function calculates the relative frequency of a value in a numeric vector.

Usage

relative_frequency(v = NULL, x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The relative frequency of x in v (for non-interactive mode)

Examples

data <- c(1,4,3,3,2,5,7,12,1,2,3,12)
value <- 12

# Simple calculation
relative_frequency(data, value)

# Learning mode
relative_frequency(data, value, learn = TRUE)

# Interactive mode
if(interactive()){
relative_frequency(interactive = TRUE)
}

Description

This function calculates the standard deviation of a numbers vector.

Usage

standard_deviation(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The standard deviation of the vector (for non-interactive mode)

Examples

data <- c(1,5,3,7,10,4,2)

# Simple calculation
standard_deviation(data)

# Learning mode
standard_deviation(data, learn = TRUE)

# Interactive mode
if(interactive()){
standard_deviation(interactive = TRUE)
}

Description

This function calculates the t-statistic for sample data.

Usage

tstudent(
  x = NULL,
  u = NULL,
  s = NULL,
  n = NULL,
  learn = FALSE,
  interactive = FALSE
)

Arguments

Value

The t-statistic (for non-interactive mode)

Examples

x <- 52.9
u <- 50
s <- 3
n <- 10

# Simple calculation
tstudent(x, u, s, n)

# Learning mode
tstudent(x, u, s, n, learn = TRUE)

# Interactive mode
if(interactive()){
tstudent(interactive = TRUE)
}

Description

This function calculates the variance of a numbers vector.

Usage

variance(x = NULL, learn = FALSE, interactive = FALSE)

Arguments

Value

The variance of the vector (for non-interactive mode)

Examples

data <- c(10,4,5,7,3,4,1)

# Simple calculation
variance(data)

# Learning mode
variance(data, learn = TRUE)

# Interactive mode
if(interactive()){
variance(interactive = TRUE)
}