Package 'jjb' reference manual

Title:	Balamuta Miscellaneous
Description:	Set of common functions used for manipulating colors, detecting and interacting with 'RStudio', modeling, formatting, determining users' operating system, feature scaling, and more!
Authors:	James Balamuta [aut, cre, cph]
Maintainer:	James Balamuta <[email protected]>
License:	GPL (>= 2)
Version:	0.1.2
Built:	2024-11-08 02:40:09 UTC
Source:	https://github.com/coatless-rpkg/jjb

jjb: Balamuta Miscellaneous

Description

Set of common functions used for manipulating colors, detecting and interacting with 'RStudio', modeling, formatting, determining users' operating system, feature scaling, and more!

Author(s)

Maintainer: James Balamuta [email protected] (ORCID) [copyright holder]

Accuracy of the Model

Description

Calculates the accuracy of the model by taking the mean of the number of times the truth, $y$ , equals the predicted, $\hat{y}$ .

Usage

acc(y, yhat)
acc(y, yhat)

Arguments

`y`	A `vector` of the true $y$ values
`yhat`	A `vector` of predicted $\hat{y}$ values.

Value

The accuracy of the classification in numeric form.

Examples

# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = round(runif(n))
yhat = round(runif(n))

# Compute
o = acc(y, yhat)
# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = round(runif(n))
yhat = round(runif(n))

# Compute
o = acc(y, yhat)

Celsius to Fahrenheit Conversion

Description

Converts temperature recorded in Celsius to Fahrenheit.

Usage

celsius_to_fahrenheit(t_celsius)
celsius_to_fahrenheit(t_celsius)

Arguments

t_celsius

Temperature recorded in Celsius.

Value

A numeric vector.

Examples


celsius_to_fahrenheit(33)

celsius_to_fahrenheit(0)
celsius_to_fahrenheit(33)

celsius_to_fahrenheit(0)

Celsius to Kelvin Conversion

Description

Converts temperature recorded in Celsius to Kelvin.

Usage

celsius_to_kelvin(t_celsius)
celsius_to_kelvin(t_celsius)

Arguments

t_celsius

Temperature recorded in Celsius.

Value

A numeric vector.

Examples

celsius_to_kelvin(92)

celsius_to_kelvin(32)
celsius_to_kelvin(92)

celsius_to_kelvin(32)

Character at Position i

Description

Returns the character at location i inside the string.

Usage

char_at(x, index)
char_at(x, index)

Arguments

`x`	A `⁠character vector⁠` to extract position from.
`index`	An `integer` between 1 and length n.

Value

A ⁠character vector⁠ of length index.

Author(s)

James J Balamuta

Examples

# Example string
s = "statistics"

# Single character
char_at(s, 1)

# Vectorized position
char_at(s, c(2, 3))
# Example string
s = "statistics"

# Single character
char_at(s, 1)

# Vectorized position
char_at(s, c(2, 3))

Create a circle pattern within a matrix

Description

Takes a default matrix and embeds circles within the matrix.

Usage

circle_matrix(m, n, x.center, y.center, r, f = 1)
circle_matrix(m, n, x.center, y.center, r, f = 1)

Arguments

`m`	A `int` that is the number of rows of the matrix
`n`	A `int` that is the number of the columns of the matrix.
`x.center`	A `vector` of x coordinate center position of the circle.
`y.center`	A `vector` of y coordinate center position of the circle.
`r`	A `vector` of integers denoting the different circle radii.
`f`	A `vector` of values that specify what the inside of the circles should be.

Value

A matrix with circles imprinted within its dimensions.

Author(s)

James Balamuta

Examples

# Generate a basic circle matrix
circle_matrix(10, 10, 3, 4, 2)

# Generate two circles within the matrix
circle_matrix(10, 20, c(3,6), c(4,6), c(2,2))

# Different fills
circle_matrix(10, 20, c(3,6), c(4,6), c(2,2), f = c(1,2))
# Generate a basic circle matrix
circle_matrix(10, 10, 3, 4, 2)

# Generate two circles within the matrix
circle_matrix(10, 20, c(3,6), c(4,6), c(2,2))

# Different fills
circle_matrix(10, 20, c(3,6), c(4,6), c(2,2), f = c(1,2))

Convert Multiple Columns of a `data.frame` All at once conversion of a `data.frame` from current column types to alternates.

Description

Convert Multiple Columns of a data.frame

All at once conversion of a data.frame from current column types to alternates.

Usage

convert_cols(d, cast)
convert_cols(d, cast)

Arguments

`d`	A `data.frame` that needs to have specific columns converted.
`cast`	A `⁠string vector⁠` containing either: `"n"` (numeric), `"c"` (character), or `"f"` (factor).

Value

A data.frame with converted column types.

Examples


n = 100

st = sample(LETTERS, n, replace = TRUE)
sr = sample(letters, n, replace = TRUE)
num = rnorm(n)

d = data.frame(x = st, y = num, z = sr, stringsAsFactors = FALSE)

# Convert all columns

o = convert_cols(d,c("f", "c", "f"))

# Convert a subset
d[, c(1, 3)] = convert_cols(d[, c(1, 3)], c("f", "f"))
n = 100

st = sample(LETTERS, n, replace = TRUE)
sr = sample(letters, n, replace = TRUE)
num = rnorm(n)

d = data.frame(x = st, y = num, z = sr, stringsAsFactors = FALSE)

# Convert all columns

o = convert_cols(d,c("f", "c", "f"))

# Convert a subset
d[, c(1, 3)] = convert_cols(d[, c(1, 3)], c("f", "f"))

Change Default Graphing Device from RStudio

Description

Checks to see if the user is in RStudio. If so, then it changes the device to a popup window.

Usage

external_graphs(ext = TRUE)
external_graphs(ext = TRUE)

Arguments

ext

A logical indicating whether the graph should be done externally or internally in RStudio.

Details

Depending on the operating system, the default drivers attempted to be used are:

OS X: quartz()
Linux: x11()
Windows: windows()

Note, this setting is not permanent. Thus, the behavioral change will last until the end of the session.

Also, the active graphing environment will be killed. As a result, any graphs that are open will be deleted. You will have to regraph them.

Value

There is no return value. Instead, once finished, the function will cause a side effect to occur. See details for more.

Author(s)

James Balamuta

Examples


# Turn on external graphs
external_graphs()

# Turn off external graphs
external_graphs(FALSE)

# Turn on external graphs
external_graphs()

# Turn off external graphs
external_graphs(FALSE)

Fahrenheit to Celsius Conversion

Description

Converts temperature recorded in Fahrenheit to Celsius.

Usage

fahrenheit_to_celsius(t_fahrenheit)
fahrenheit_to_celsius(t_fahrenheit)

Arguments

t_fahrenheit

Temperature recorded in Fahrenheit.

Value

A numeric vector.

Examples


fahrenheit_to_celsius(92)

fahrenheit_to_celsius(32)
fahrenheit_to_celsius(92)

fahrenheit_to_celsius(32)

Fahrenheit to Kelvin to Conversion

Description

Converts temperature recorded in Fahrenheit to Kelvin.

Usage

fahrenheit_to_kelvin(t_fahrenheit)
fahrenheit_to_kelvin(t_fahrenheit)

Arguments

t_fahrenheit

Temperature recorded in Fahrenheit.

Value

A numeric vector.

Examples

fahrenheit_to_kelvin(92)

fahrenheit_to_kelvin(32)
fahrenheit_to_kelvin(92)

fahrenheit_to_kelvin(32)

Feature Scaling

Description

Scale features in a datasets.

Usage

feature_rescale(x, x_min = NULL, x_max = NULL)

feature_derescale(x_rescaled, x_min, x_max)

feature_norm(x, x_norm = NULL)

feature_denorm(x_norm_std, x_norm = NULL)

feature_standardize(x, x_mean = NULL, x_sd = NULL)

feature_destandardize(x_std, x_mean = NULL, x_sd = NULL)
feature_rescale(x, x_min = NULL, x_max = NULL)

feature_derescale(x_rescaled, x_min, x_max)

feature_norm(x, x_norm = NULL)

feature_denorm(x_norm_std, x_norm = NULL)

feature_standardize(x, x_mean = NULL, x_sd = NULL)

feature_destandardize(x_std, x_mean = NULL, x_sd = NULL)

Arguments

`x`	Numeric values
`x_min`	Minimum non-normalized numeric value
`x_max`	Maximum non-normalized numeric value
`x_rescaled`	Rescaled values of `x`.
`x_norm`	Euclidean norm of x
`x_norm_std`	Euclidean vector of normalized `x` values.
`x_mean`	Mean of `x` values
`x_sd`	Standard Deviation of `x` values
`x_std`	Z-transformed `x` values

Details

The following functions provide a means to either scale features or to descale the features and return them to normal. These functions are ideal for working with optimizers.

Feature Scale	Feature Descale
feature_rescale	feature_derescale
feature_norm	feature_denorm
feature_standardize	feature_destandardize

Value

A numeric vector.

Feature Rescaling

Convert the original data $x$ to $x_{scaled}$ :

$x[scaled] = (x-x[min])/(x[max]-x[min])$

To move from the rescaled value $x_{scaled}$ to the original value $x$ use:

$x = x[scaled] * (x[max] - x[min]) + x[min]$

Feature Standardization

Convert the original data $x$ to $x_{std}$ :

$x[std] = (x-avg[x])/(sigma[x])$

To move from the standardized value $x_{std}$ to the original value $x$ use:

$x = x[std] * sigma[x] + avg[x]$

Feature Normalization

Convert the original data $x$ to $x_{norm}$ :

$x[norm] = (x)/||x||$

To move from the normalized value $x_{norm}$ to the original value $x$ use:

$x = x[norm] * ||x||$

Author(s)

James Balamuta

Examples


# Rescaling Features
temperatures = c(94.2, 88.1, 32, 0)

temp_min = min(temperatures)
temp_max = max(temperatures)

temperatures_norm   = feature_rescale(temp_min, temp_max)
temperatures_denorm = feature_derescale(temperatures_norm, temp_min, temp_max)

all.equal(temperatures, temperatures_denorm)
 
# Norming Features
x = 1:10

x_norm = sqrt(sum(x^2))

x_norm_std = feature_norm(x, x_norm)

x_recover = feature_denorm(x_norm_std, x_norm)
all.equal(x, x_recover)

# Standardizing Features
x      = 1:10

x_mean = mean(x)
x_sd   = sd(x)

x_std  = feature_standardize(x, x_mean, x_sd)
x_recovery = feature_destandardize(x, x_mean, x_sd)

all.equal(x, x_recovery)
# Rescaling Features
temperatures = c(94.2, 88.1, 32, 0)

temp_min = min(temperatures)
temp_max = max(temperatures)

temperatures_norm   = feature_rescale(temp_min, temp_max)
temperatures_denorm = feature_derescale(temperatures_norm, temp_min, temp_max)

all.equal(temperatures, temperatures_denorm)
 
# Norming Features
x = 1:10

x_norm = sqrt(sum(x^2))

x_norm_std = feature_norm(x, x_norm)

x_recover = feature_denorm(x_norm_std, x_norm)
all.equal(x, x_recover)

# Standardizing Features
x      = 1:10

x_mean = mean(x)
x_sd   = sd(x)

x_std  = feature_standardize(x, x_mean, x_sd)
x_recovery = feature_destandardize(x, x_mean, x_sd)

all.equal(x, x_recovery)

Floor and Cap a Numeric Variable

Description

Determine the floor and cap of a numeric variable by taking quantiles. Using the quantiles, values in the data found to be lower or higher than the floor or cap are replaced.

Usage

floor_and_cap(x, probs = c(0.025, 0.975))
floor_and_cap(x, probs = c(0.025, 0.975))

Arguments

`x`	A `vector` that has length $N$ .
`probs`	A `vector` containing two values between 0 and 1, with the first being less than the second.

Value

A vector with the values floored and capped.

Examples


# One case version
n = 100

x = rnorm(n)

x[n - 1] = -99999
x[n] = 10000

y = floor_and_cap(x)

# Dataset example

d = data.frame(x, y = rnorm(n))

o = sapply(d, floor_and_cap)
# One case version
n = 100

x = rnorm(n)

x[n - 1] = -99999
x[n] = 10000

y = floor_and_cap(x)

# Dataset example

d = data.frame(x, y = rnorm(n))

o = sapply(d, floor_and_cap)

Convert 0-255 to a Hex number

Description

This is a helper function for rgb_to_hex. This function takes a single R, G, or B numeric value and converts it to hex.

Usage

int_to_hex(n)
int_to_hex(n)

Arguments

n

An int

Value

A string of length 2.

Examples

int_to_hex(22)
int_to_hex(22)

Is R Open in RStudio?

Description

Detects whether R is open in RStudio.

Usage

is_rstudio()
is_rstudio()

Value

A logical value that indicates whether R is open in RStudio.

Author(s)

James Balamuta

Examples

is_rstudio()
is_rstudio()

Integer Check

Description

Checks whether the submitted value is an integer

Usage

is_whole(x)
is_whole(x)

Arguments

`x`	A `numeric` value to check to see if it is an `integer`.

Value

A boolean value indicating whether the value is an integer or not.

Author(s)

James Balamuta

Examples

is_whole(2.3)
is_whole(4)
is_whole(c(1,2,3))
is_whole(c(.4,.5,.6))
is_whole(c(7,.8,9))
is_whole(2.3)
is_whole(4)
is_whole(c(1,2,3))
is_whole(c(.4,.5,.6))
is_whole(c(7,.8,9))

Check for an Operating System

Description

Performs a check to determine the OS

Usage

is_windows()

is_macos()

is_linux()

is_sun()
is_windows()

is_macos()

is_linux()

is_sun()

Value

Either TRUE or FALSE

Author(s)

James Joseph Balamuta

Kelvin to Celsius Conversion

Description

Converts temperature recorded in Kelvin to Celsius.

Usage

kelvin_to_celsius(t_kelvin)
kelvin_to_celsius(t_kelvin)

Arguments

t_kelvin

Temperature recorded in Kelvin.

Value

A numeric vector.

Examples

kelvin_to_celsius(92)

kelvin_to_celsius(32)
kelvin_to_celsius(92)

kelvin_to_celsius(32)

Kelvin to Fahrenheit Conversion

Description

Converts temperature recorded in Celsius to Kelvin.

Usage

kelvin_to_fahrenheit(t_kelvin)
kelvin_to_fahrenheit(t_kelvin)

Arguments

t_kelvin

Temperature recorded in Kelvin.

Value

A numeric vector.

Examples

kelvin_to_fahrenheit(92)

kelvin_to_fahrenheit(32)
kelvin_to_fahrenheit(92)

kelvin_to_fahrenheit(32)

Lag Vector Values

Description

Provides a lagging mechanism for vector data.

Usage

lagged(x, lag = 1)
lagged(x, lag = 1)

Arguments

`x`	A `vec` of data.
`lag`	An `integer` value.

Value

A vector with lagged values and NAs.

Author(s)

James Balamuta

Examples

x = rnorm(10)

lagged(x, 2)
x = rnorm(10)

lagged(x, 2)

Maxima and Minima n elements

Description

Obtain the Maximum or Minimum n elements from a vector.

Usage

max_n(x, n = 1L)

min_n(x, n = 1)
max_n(x, n = 1L)

min_n(x, n = 1)

Arguments

`x`	Data vector
`n`	Number of observations to select

Details

The underlying function sorts the data using base::sort() and then extracts out the appropriate n-back or n-forward values.

As a result of the sorting procedure, this is an inefficient function.

Value

A vector containing the maximum/minimum of $n$ elements.

Examples


x = 1:10

# Defaults to traditional max
# This is more costly to compute than using the regular max function.
max_n(x) 

# Retrieve top two observations (highest first)
max_n(x, 2)

# Missing values have no effect on the sorting procedure
x[9] = NA
max_n(x, 3)

# Defaults to traditional min.
# This is more costly to compute than using the regular min function.
min_n(x)
min(x)

# Retrieve bottom two observations (lowest first)
min_n(x, 2)

# Missing values have no effect on the sorting procedure
x[2] = NA
min_n(x, 3)
x = 1:10

# Defaults to traditional max
# This is more costly to compute than using the regular max function.
max_n(x) 

# Retrieve top two observations (highest first)
max_n(x, 2)

# Missing values have no effect on the sorting procedure
x[9] = NA
max_n(x, 3)

# Defaults to traditional min.
# This is more costly to compute than using the regular min function.
min_n(x)
min(x)

# Retrieve bottom two observations (lowest first)
min_n(x, 2)

# Missing values have no effect on the sorting procedure
x[2] = NA
min_n(x, 3)

Make Directory

Description

Create a directory using either a relative path or an absolute path.

Usage

mkdir(dir, r = TRUE)
mkdir(dir, r = TRUE)

Arguments

`dir`	A `string` indicating the directory to make.
`r`	A `boolean` that indicates whether the directories should be made recursively

Value

New directory on file system

Author(s)

James Balamuta

Examples


# Make directory from working directory
mkdir("toad")

## This assumes the computer is on Windows and the C drive exists.
# Make directory from absolute path
mkdir("C:/path/to/dir/toad")

# Make directory from working directory
mkdir("toad")

## This assumes the computer is on Windows and the C drive exists.
# Make directory from absolute path
mkdir("C:/path/to/dir/toad")

Mean Squared Error (MSE)

Description

Calculates the mean square of the model by taking the mean of the sum of squares between the truth, $y$ , and the predicted, $\hat{y}$ at each observation $i$ .

Usage

mse(y, yhat)
mse(y, yhat)

Arguments

`y`	A `vector` of the true $y$ values
`yhat`	A `vector` of predicted $\hat{y}$ values.

Details

The equation for MSE is:

$\frac{1}{n}\sum\limits_{i = 1}^n {{{\left( {{y_i} - {{\hat y}_i}} \right)}^2}}$

Value

The MSE in numeric form.

Examples

# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = rnorm(n)
yhat = rnorm(n, 0.5)

# Compute
o = mse(y, yhat)
# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = rnorm(n)
yhat = rnorm(n, 0.5)

# Compute
o = mse(y, yhat)

Determine Amount of Missing Values in Data

Description

Computes the proportion of data that is missing in a given data set.

Usage

na_prop_overall(x)

na_prop_by_variable(x)

na_prop_by_observation(x)

na_count_overall(x)

na_count_by_variable(x)

na_count_by_observation(x)
na_prop_overall(x)

na_prop_by_variable(x)

na_prop_by_observation(x)

na_count_overall(x)

na_count_by_variable(x)

na_count_by_observation(x)

Arguments

`x`	A vector of length $N$ or a matrix with dimensions $N \times P$ .

Value

Overall: a single numeric value between ⁠[0, 1]⁠ or a count between ⁠[0, N]⁠.
Variable: $P$ different numeric values between ⁠[0, 1]⁠ or counts between ⁠[0, N]⁠.
Observation: $N$ different numeric values between ⁠[0, 1]⁠ or counts between ⁠[0, P]⁠.

Examples

# By vector
x = c(1, 2, NA, 4)
na_prop_overall(x)
na_count_overall(x)

# By Data Frame
missing_df = data.frame(
 a = c(1, 2, NA, 4),
 b = c(3, NA, 2, NA)
)

# Proportion
na_prop_overall(missing_df)
na_prop_by_variable(missing_df)
na_prop_by_observation(missing_df)

# Counts
na_count_overall(missing_df)
na_count_by_variable(missing_df)
na_count_by_observation(missing_df)

# By vector
x = c(1, 2, NA, 4)
na_prop_overall(x)
na_count_overall(x)

# By Data Frame
missing_df = data.frame(
 a = c(1, 2, NA, 4),
 b = c(3, NA, 2, NA)
)

# Proportion
na_prop_overall(missing_df)
na_prop_by_variable(missing_df)
na_prop_by_observation(missing_df)

# Counts
na_count_overall(missing_df)
na_count_by_variable(missing_df)
na_count_by_observation(missing_df)

Pad Numeric Numbers

Description

Add zeros before start of the number

Usage

pad_number(x)
pad_number(x)

Arguments

`x`	A `vector`

Value

A ⁠character vector⁠ that is padded to the length of the maximum entry.

Author(s)

James Balamuta

Examples

# Padding applied
pad_number(8:10)

# No padding applied
pad_number(2:3)

# Pads non-negative number with 0.
# This needs to be improved slightly...
pad_number(-1:1)
# Padding applied
pad_number(8:10)

# No padding applied
pad_number(2:3)

# Pads non-negative number with 0.
# This needs to be improved slightly...
pad_number(-1:1)

Require a Specific Operating System

Description

Mandates the presence of an operating system

Usage

require_linux()

require_windows()

require_macos()

require_sun()
require_linux()

require_windows()

require_macos()

require_sun()

Details

If any of these functions are called on the wrong operating system. A stop error is triggered and the function will fail.

Author(s)

James Joseph Balamuta

Convert RGB Value to Hexadecimal

Description

This function converts an RGB value to the hexadecimal numbering system.

Usage

rgb_to_hex(R, G, B, pound = TRUE)
rgb_to_hex(R, G, B, pound = TRUE)

Arguments

`R`	A `int` that is between 0 and 255 for the Red value.
`G`	A `int` that is between 0 and 255 for the Green value.
`B`	A `int` that is between 0 and 255 for the Blue value.
`pound`	A `bool` that indicates whether a pound sign should be prepended to the hexadecimal.

Value

A string containing the hexadecimal information.

Examples

# Hexadecimal with pound sign
rgb_to_hex(255,255,255)

# Heaxadecimal without pound sign
rgb_to_hex(255,255,255,FALSE)
# Hexadecimal with pound sign
rgb_to_hex(255,255,255)

# Heaxadecimal without pound sign
rgb_to_hex(255,255,255,FALSE)

Root Mean Squared Error (RMSE)

Description

Calculates the root mean square of the model by taking the square root of mean of the sum of squares between the truth, $y$ , and the predicted, $\hat{y}$ at each observation $i$ .

Usage

rmse(y, yhat)
rmse(y, yhat)

Arguments

`y`	A `vector` of the true $y$ values
`yhat`	A `vector` of predicted $\hat{y}$ values.

Details

The formula for RMSE is:

$\sqrt {\frac{1}{n}\sum\limits_{i = 1}^n {{{\left( {{y_i} - {{\hat y}_i}} \right)}^2}} }$

Value

The RMSE in numeric form

Examples

# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = rnorm(n)
yhat = rnorm(n, 0.5)

# Compute
o = mse(y, yhat)
# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = rnorm(n)
yhat = rnorm(n, 0.5)

# Compute
o = mse(y, yhat)

Shade an RGB value

Description

The function shades or darkens an RGB value by adding black to the values.

Usage

shade(rgb_value, shade_factor = 0.1)
shade(rgb_value, shade_factor = 0.1)

Arguments

`rgb_value`	A `vector` with length $3 \times 1$ .
`shade_factor`	A `double` that ranges between $[0, 1]$ .

Value

A matrix with dimensions $3 \times 1$ .

Examples

shade(c(22, 150, 230), shade_factor = 0.5)
shade(c(22, 150, 230), shade_factor = 0.5)

System Architecture

Description

System Architecture

Usage

system_arch()
system_arch()

Value

Either "x64" or "x32"

Natural Graphics Driver for Operating System

Description

Provides the default operating system graphics utility

Usage

system_graphic_driver()
system_graphic_driver()

Value

A string that is either:

"quartz": if on MacOS
"windows": if on Windows
"x11": if on Linux or Solaris

Author(s)

James Balamuta

Examples

# Returns a string depending on test platform
system_graphic_driver()
# Returns a string depending on test platform
system_graphic_driver()

Tint an RGB value

Description

The function tints or lightens an RGB value by adding white to the values.

Usage

tint(rgb_value, tint_factor = 0.2)
tint(rgb_value, tint_factor = 0.2)

Arguments

`rgb_value`	A `vector` with length $3 \times 1$ .
`tint_factor`	A `double` that ranges between $[0, 1]$ .

Value

A matrix with dimensions $3 \times 1$ .

Examples

tint(c(22, 150, 230), tint_factor = 0.5)
tint(c(22, 150, 230), tint_factor = 0.5)

Obtain the Trace of a Square Matrix

Description

Calculates and returns the trace of a square matrix.

Usage

tr(x)
tr(x)

Arguments

`x`	A `matrix` that is square e.g. $N \times N$

Value

A matrix with circles imprinted within its dimensions.

Author(s)

James Balamuta

Examples

# I_2 matrix
tr(diag(2))
# I_2 matrix
tr(diag(2))

Create a "safe" url title

Description

Takes a string, forces characters to lower case, then removes punctuation and switch spaces to - instead of _

Usage

url_title(st)
url_title(st)

Arguments

`st`	A `string` that needs to be a title in a url

Value

A string with the aforementioned modifications.

Author(s)

James Balamuta

Examples

url_title("My Name is Jaime!")
url_title("My Name is Jaime!")

Package 'jjb'

Help Index

jjb: Balamuta Miscellaneous

Description

Author(s)

See Also

Accuracy of the Model

Description

Usage

Arguments

Value

Examples

Celsius to Fahrenheit Conversion

Description

Usage

Arguments

Value

Examples

Celsius to Kelvin Conversion

Description

Usage

Arguments

Value

Examples

Character at Position i

Description

Usage

Arguments

Value

Author(s)

Examples

Create a circle pattern within a matrix

Description

Usage

Arguments

Value

Author(s)

Examples

Convert Multiple Columns of a data.frame All at once conversion of a data.frame from current column types to alternates.

Description

Usage

Arguments

Value

Examples

Change Default Graphing Device from RStudio

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Fahrenheit to Celsius Conversion

Description

Usage

Arguments

Value

Examples

Fahrenheit to Kelvin to Conversion

Description

Usage

Arguments

Value

Examples

Feature Scaling

Description

Usage

Arguments

Details

Value

Feature Rescaling

Feature Standardization

Feature Normalization

Author(s)

Examples

Floor and Cap a Numeric Variable

Description

Usage

Arguments

Value

Convert Multiple Columns of a `data.frame` All at once conversion of a `data.frame` from current column types to alternates.