simple function chaining routine

Description

chain multiple function to a call

Usage

obj %.% funcs

chain_sep(sep = "\\.\\.")

Arguments

Value

the result of applying the chained functions to the data object

Note

chain_sep allows the user to preset the separator for the function chaining

e.g. you can call the function to set sep = "__" before using the

Examples

#use default sep ".."
1:3%.%unique..length
sample(1:1000,10,replace=TRUE) %.%unique..length
sample(1:10,10,replace=TRUE) %.%unique..cumsum

# set sep before function chaining
chain_sep("__")
sample(1:10,10,replace=TRUE) %.%unique__cumsum
sample(1:10,10,replace=TRUE) %.%unique__cumsum__length


# set sep before function chaining
chain_sep("X")
sample(1:10,10,replace=TRUE) %.%uniqueXcumsum

Not in vector or array

Description

Check if entry is in vector

Usage

x %nin% table

Arguments

Value

a boolean value to indicate if entry is present

Examples

5 %nin% c(1:10) #FALSE
5 %nin% c(11:20) #TRUE

x = "a"
if(x %nin% letters) x

# let's say we are trying to exclude numbers from a vector
vector_num1 <- number(9, max.digits = 5, seed = 1) #simulate 9 numbers
vector_num1 #values
vector_num1[vector_num1 %nin% c(83615,85229)]#return values not 83615 or 85229

Addin snippet function to add header comment to a current opened file

Description

Shorthand to add header comment

Usage

add.header()

Value

Inserts header content for file

Examples

if(interactive())
add.header()

Addin snippet function to custom section comment

Description

Shorthand to add section comment to current file

Usage

add.sect.comment()

Value

Inserts section comment content for file

Examples

if(interactive())
add.sect.comment()

Snippet R function to clear console and set directory

Description

Shorthand to add clear console code to current file

Usage

add.snippet.clear()

Value

Inserts code to clear console

Examples

if(interactive())
add.snippet.clear()

Add index keys to a vector or data frame or list or matrix

Description

Index a vector or lists and convert to a list of objects

Usage

add_key(vector)

indexed(vector, key = key, value = value)

Arguments

Details

This function takes a vector and turns it into a list containing 'key' and 'value' for each vector. This allows the output to be used in loops such as for loops or lapply or other functions to track the index of the list content e.g. 1,2,3...

This function also contains a validator to ensure that a vector had not been previously 'keyed', which prevents the user from inadvertently calling the function twice on a vector. Helps especially because the function keys the vector, and sets the new list to the variable name of the original vector.

This function takes a vector and turns it into a list containing 'key' and 'value' for each vector. This allows the output to be used in loops such as for loops or lapply or other functions to track the index of the list content e.g. 1,2,3...

This function also contains a validator to ensure that a vector had not been previously 'keyed', which prevents the user from inadvertently calling the function twice on a vector. Helps especially because the function keys the vector, and sets the new list to the variable name of the original vector.

Value

a transformed list containing keys along with vector values

Use case

Efficient for loops and for tracking various steps through a vector contents

Note

add_key - resaves the keys and value pairs to original variable

indexed - return the keys and value pairs

Examples

# EXAMPLES for add_key()


#ex1 simple conversion of a vector
rti2 <- c("rpkg","obinna", "obianom")
add_key(rti2)
rti2

#ex2 add keys to a vector content for use in downstream processes
ver1 <- c("Test 1","Test 2","Test 3")
add_key(ver1)

#ex3 use keyed ver1 in for loop
for(i in ver1){
  message(sprintf("%s is the key for this %s", i$key, i$value))
}

#ex4 use keyed ver1 in lapply loop
xl1 <- lapply(ver1,function(i){
  message(sprintf("lapply - %s is the key for this %s", i$key, i$value))
})


# EXAMPLES for indexed()

#ex1 simple conversion of a vector
rti2 <- c("rpkg","obinna", "obianom")
indexed(rti2)

#ex2 add keys to a vector content for use in downstream processes
ver1 <- c("Test 1","Test 2","Test 3")


#ex3 use keyed ver1 in for loop
for(i in indexed(ver1)){
  message(sprintf("%s is the key for this %s", i$key, i$value))
}

#ex4 use keyed ver1 in for loop
#specify name for key and value
for(i in indexed(ver1,k,v)){
  message(
  sprintf("%s is the new key for this value %s",
  i$k, i$v))
}

#ex5 use keyed ver1 in lapply loop
xl1 <- lapply(indexed(ver1),function(i){
  message(sprintf("lapply - %s is the key for this %s", i$key, i$value))
})

Prompt guided duplication and editing of files

Description

AI like duplication and editing of files

Usage

ai.duplicate(file = NULL, new.name = NULL, open = TRUE)

Arguments

Value

duplicated files with edited texts

Examples

if(interactive()){
file1s <- paste0(tempfile(),".R")
writeLines("message(
'Sample items: farm, shinyappstore, rpkg'
)", file1s)
ai.duplicate(file1s,'file2.R')
}

Listing of all CRAN archived R packages

Description

Retrieve a list of all currently archived R packages and their archive date

Usage

archivedPkg(
  startsWith = c("all", letters),
  after = NULL,
  inc.date = TRUE,
  as = c("data.frame", "list")
)

Arguments

Value

a data frame or list containing listing of all archived R packages

Use case

This function allows the retrieval of various R packages archived by CRAN along with the respective latest archive date. The packages retrieved include both active and inactive R projects submitted to CRAN. When a new version of an active R package is published, the older versions of the package gets archived. In the same way, when a package is decommissioned from CRAN active projects for one reason or another, it gets archived.

Note

* The "startsWith" argument should be one letter and should be in lowercase
* If no argument is provided for "startsWith", all the packages will be retrieved
* The format of the "after" argument must be YYYY-MM-DD e.g. 2022-04-11

Examples

# Task 1: get archived R packages with names beginning with C or All
head(archivedPkg(startsWith = "all"), n= 10) #retrieves all packages
head(archivedPkg(startsWith = "c"), n= 10) #retrieves only packages beginning with a

# Task 2: return the packages from Task 1 without including latest archive date
res.dt2 <- archivedPkg(startsWith = "b", inc.date = FALSE)
res.dt2[1:10,]

# Task 3: return the results from Task 2 as a list
res.dt3 <- archivedPkg(startsWith = "c", inc.date = FALSE, as = "list")
res.dt3$name[1:10]

res.dt3 <- archivedPkg(startsWith = "e", as = "list")
res.dt3$name[1:10]

# Task 4: return the archived packages beginning with Y archived after 2022-08-12
# Note that startsWith should be lowercase

#without archive date
yRPkg <- archivedPkg(startsWith = "y", after= NULL)
nrow(yRPkg) #number of rows returned
head(yRPkg, n = 15) #show first 15 rows

#with archive date
yRPkg2 <- archivedPkg(startsWith = "y", after= "2022-08-12")
nrow(yRPkg2) #number of rows returned
head(yRPkg2, n = 15) #show first 15 rows, notice no archive date before 2022-08-12

Convert boolean values between formats

Description

Convert Yes/No to 1/0 or to TRUE/FALSE or vice versa

Usage

as.boolean(ds, type = 3)

Arguments

Details

Output various format of booleans into a specified format. Below are the options for the type argument.

type: options are as follows -

1 - Yes/No
2 - TRUE/FALSE
3 - 1/0

Value

output adhering to the format of the type provided

Examples

# Task: convert "yes" or "no" to format of TRUE or FALSE
as.boolean("yes",2)
as.boolean("no",2)
as.boolean("YES",2)
as.boolean("NO",2)

# Task: convert "yes" or "no" to format of 1 or 0
as.boolean("yes",3)
as.boolean("no",3)
as.boolean("YES",3)
as.boolean("NO",3)


# Task: convert 1 to format of Yes or No
as.boolean(1,1)

# Task: convert "T" to format of Yes or No
as.boolean("T",1)


# Task: convert "f" to format of TRUE or FALSE
as.boolean("f",2)


# Task: convert 1 to format of TRUE or FALSE
as.boolean(1,2)


# Task: convert "Y" or "y" to format of Yes or No
as.boolean("Y",1) #uppercase Y
as.boolean("y",1) #lowercase y


# Task: convert TRUE/FALSE to format of 1 or 0
as.boolean(TRUE,3)
as.boolean(FALSE,3)


# Task: convert TRUE/FALSE to format of Yes or No
as.boolean(TRUE,1)
as.boolean(FALSE,1)



# In case of error in argument
# as.boolean("tr",3) #NA
# as.boolean("ye",3) #NA

# vector of mixed boolean to TRUE/FALSE or 1/0
multv <- c(TRUE,"y","n","YES","yes",FALSE,"f","F","T","t")
as.boolean(multv,1) # return vector as Yes/No
as.boolean(multv,2) # return vector as TRUE/FALSE
as.boolean(multv,3) # return vector as 1/0

Generate a bionic text

Description

This function serves as a mechanism enabling the conversion of provided text into a bionic form. Users input the text, and the function, in turn, delivers the text transformed into a bionic format.

Usage

bionic_txt(text)

Arguments

Details

A bionic text refers to a transformed version of a given text achieved through a specialized function designed to incorporate elements of advanced technology, enhancing both the form and content of the original input. This function operates by infusing the text with a fusion of various elements, resulting in a synthesis that transcends traditional linguistic boundaries. The function augments the text with dynamic visual representations that adapt to the reader's preferences. The goal is to create a text that not only conveys information but also engages the audience in a more immersive and interactive manner, harnessing the capabilities of modern technology to redefine the traditional concept of textual communication. An example of a bionic text could be a news article that dynamically updates with real-time data, incorporates multimedia elements, and adjusts its presentation style based on the reader's preferences, thereby offering a more enriched and personalized reading experience.

Value

bionic text

References

This idea stems from a blog article published at https://www.r-bloggers.com/2023/10/little-useless-useful-r-functions-function-for-faster-reading-with-bionic-reading/ and the original source for bionic texts may be found at https://bionic-reading.com/

Examples

# simple example to show a text
# transformation to bionic text

# text to transform
text1 <- "A tool for nonparametric
estimation and inference
of a non-decreasing
monotone hazard\nratio
from a right censored survival dataset."

# transform text
genbt <- bionic_txt(text1)

# print bionic text as message or cat
message(genbt)
cat(genbt)

Convert Categorical Values to Numeric Improvement to seq_along()

Description

Converts categorical values into numeric by sorting unique values alphabetically/numerically and assigning sequential numbers starting from 1

Usage

cat_to_num(x, decreasing = FALSE)

cat_to_num2(x, decreasing = FALSE)

Arguments

Value

A numeric vector where each category is mapped to a number based on its sorted position. NA values in the input remain NA in the output.

Examples

# Example 1: Basic character vector
fruits <- c("apple", "banana", "apple", "cherry", "banana")
cat_to_num(fruits, decreasing = FALSE)  # Returns: 1, 2, 1, 3, 2

# Example 2: Vector with NAs
grades <- c("A", NA, "C", "B", "A", NA)
cat_to_num(grades)  # Returns: 1, NA, 3, 2, 1, NA

# Example 3: Factor input
animals <- factor(c("dog", "cat", "bird", "cat", "dog"))
cat_to_num(animals)  # Returns: 3, 2, 1, 2, 3

# Example 4: Numeric categories
sizes <- c("XL", "S", "M", "XS", "L")
cat_to_num(sizes)  # Returns: 5, 2, 3, 1, 4

# Example 5: Single category
status <- c("active", "active", "active")
cat_to_num(status)  # Returns: 1, 1, 1

# Examples for cat_to_num2

# Assign numeric values to a set of categorical values
# Call the value assignment to assign
# a numeric value to a new categorical value

cat_to_num2(letters[1:7])("b")
cat_to_num2(letters[1:7], decreasing = FALSE)("d")
# the above assigns numeric values to letters a-g
# and then calls the number for "a"

cat_vals_5 <- c("apple","orange","guava")
num_equiv <- cat_to_num2(cat_vals_5)

applenum = num_equiv("apple")

Combine specific functions as store as one function

Description

Use many functions in one call using predeclared calls

Usage

chain_func(..., otherargs = list())

Arguments

Value

returns a function that combines multiple function

Examples

# Example 1 with base functions
combf1 <- chain_func(unique, cumsum, print)
result <- combf1(sample(1:5,10,replace = TRUE))
#or
u = sample(1:5,10,replace = TRUE)
result <- combf1(u)

# Example 2 with base functions with arguments
combf2 <- chain_func(unique, print, otherargs = list(.= c(FALSE),.=c(2)))
result <- combf2(sample(1:3,10,replace = TRUE))

# Example 3 with custom functions
r = function(a,b,c){
  if(!missing(a))print(a)
  if(!missing(b))print(b)
  if(!missing(c))print(c)
  return(a)
}

r2 = function(a,b,c){
  if(!missing(a))message(a)
  if(!missing(b))message(b)
  if(!missing(c))message(c)
  return(a)
}

combf3 <- chain_func(r,r2, otherargs =list(.=c("apple","cat"),.=c("rice")))
res <- combf3(head(mtcars))

Clear environment, clear console, set work directory and load files

Description

Shorthand to quickly clear console, clear environment, set working directory, load files

Usage

clean(setwd = NULL, source = c(), load = c(), clearPkgs = FALSE)

clear(setwd = NULL, source = c(), load = c(), clearPkgs = FALSE)

Arguments

Details

The purpose of this function is provide a one-line code to clear the console, clear the environment, set working directory to a specified path, source in various files into the current file, and load RData files into the current environment. The first process in the sequence of events is to clear the environment. Then the working directory is set, prior to inclusion of various files and RData. With the directory being set first, the path to the sourced in or RData files will not need to be appended to the file name. See examples.

Value

cleared environment and set directory

Examples

if(interactive()){
#simply clear environment, clear console and devices
quickcode::clean()

#clear combined with additional arguments
quickcode::clean(
  clearPkgs = FALSE
) #also clear all previously loaded packages if set to true

quickcode::clean(
  setwd = "/home/"
) #clear env and also set working directory


quickcode::clean(
  source = c("/home/file1.R","file2")
) #clear environment and source two files into current document


quickcode::clean(
  setwd = "/home/",
  source = c("file1","file2")
) #clear environment, set working directory and source 2 files into environment


quickcode::clean(
  setwd = "/home/",
  source="file1.R",
  load="obi.RData"
) #clear environment, set working directory, source files and load RData
}

Replicate Columns in a Data Frame

Description

This function replicates each column in a data frame a specified number of times.

Usage

cols.rep(data, n)

Arguments

Value

A data frame with each column replicated 'n' times. If 'n' is less than or equal to 0, an empty data frame is returned.

Examples

# Example with a simple data frame
df <- data.frame(A = c(1, 2), B = c(3, 4))
cols.rep(df, 3)

# Example with no replication (n = 0)
cols.rep(df, 0)

Compare histograms of two distributions

Description

For comparing histograms of two data distributions. Simply input the two distributions, and it generates a clear and informative histogram that illustrates the differences between the data.

Usage

compHist(
  x1,
  x2,
  title,
  col1 = "red",
  col2 = "yellow",
  xlab = "",
  ylab = "Frequency",
  separate = FALSE
)

Arguments

Details

Users have the option to view individual histograms for each distribution before initiating the comparison, allowing for a detailed examination of each dataset's characteristics. This feature ensures a comprehensive understanding of the data and enhances the user's ability to interpret the results of the distribution comparison provided by this function.

Value

return histogram comparison using basic histogram plot

Some recommended color pairs

col1 = 'dodgerblue4' (and) col2 = 'darksalmon'
col1 = 'brown' (and) col2 = 'beige'
col1 = 'pink' (and) col2 = 'royalblue4'
col1 = 'red' (and) col2 = 'yellow'
col1 = 'limegreen' (and) col2 = 'blue'
col1 = 'darkred' (and) col2 = 'aquamarine4'
col1 = 'purple' (and) col2 = 'yellow'

Note

- Hexadecimal values can also be passed
in for col1 and col2, see the example section - For best visual results,
col1 should be a dark color and col2 should be passed as a light color.
For example, col1 = "black", col2 = "yellow"

Examples

# compare two normal distributions with means that differ a lot
# in this case, the overlap will not be observed
set.seed(123)
compHist(
  x1 = rnorm(1000, mean = 3),
  x2 = rnorm(1000, mean = 10),
  title = "Histogram of Distributions With Means 3 & 10",
  col1 = "yellow", col2 = "violet"
)


# compare two normal distributions with means that are close
# in this case, the overlap between the histograms will be observed
set.seed(123)
compHist(
  x1 = rnorm(1000, mean = 0),
  x2 = rnorm(1000, mean = 2),
  title = "Histogram of rnorm Distributions With Means 0 & 2",
  col1 = "lightslateblue", col2 = "salmon"
)

set.seed(123)
# separate the plots for preview
compHist(
  x1 = rnorm(1000, mean = 0),
  x2 = rnorm(1000, mean = 2),
  title = c("Plot Means 0", "Plot Means 2"),
  col1 = "#F96167", col2 = "#CCF381",
  separate = TRUE
)

Mathematical constants

Description

List of mathematical functions

Usage

const

Format

An object of class list of length 2.

Details

const list contains a list of mathematical constants below

- pi
- e: Euler's number
- golden_ratio
- euler_mascheroni: Euler-Mascheroni constant
- feigenbaum: Feigenbaum constant
- champernowne: Champernowne constant
- apery: Roger Apery's constant
- gelfond_schneider: Gelfond-Schneider constant
- khinchin: Aleksandr Khinchin constant
- ramanujan_soldner: Ramanujan-Soldner constant

Examples

# definition of pi
const$defs$pi

# value of pi
const$values$pi

Create geometric shapes with optional text

Description

This function creates various geometric shapes using base R graphics. It supports multiple shape types including circle, square, rectangle, triangle, hexagon, star, ellipse, and regular polygons with custom sides.

Usage

create_shape(
  shape = c("circle", "square", "rectangle", "triangle", "hexagon", "star", "ellipse",
    "polygon"),
  x_center = 0,
  y_center = 0,
  size = 1,
  width = size,
  height = size,
  n_sides = 5,
  rotation = 0,
  fill_color = NA,
  border_color = "black",
  line_width = 1,
  title = "Geometric Shape",
  text = NULL,
  text_color = "black",
  text_size = 1,
  text_font = 1,
  text_angle = 0,
  show_axes = FALSE,
  show_ticks = FALSE,
  show_axis_labels = FALSE
)

Arguments

Value

A list containing:

• plot: The ggplot object

• coordinates: Data frame with x and y coordinates of the shape's vertices

• center: Vector with x and y coordinates of the shape's center

• size: Primary size parameter used

• shape: Shape type

• text: Text displayed inside the shape (if any)

Examples

## Not run: 
# Example 1: Basic circle
create_shape(shape = "circle", size = 2, fill_color = "lightblue")

# Example 2: Square with rotation and text
create_shape(
  shape = "square",
  size = 3,
  rotation = 45,
  fill_color = "salmon",
  border_color = "darkred",
  line_width = 2,
  text = "Rotated Square",
  text_color = "darkblue",
  text_size = 3
)

# Example 3: Hexagon with custom title
create_shape(
  shape = "hexagon",
  size = 2.5,
  fill_color = "lightgreen",
  border_color = "darkgreen",
  title = "My Hexagon"
)

# Example 4: Star with custom styling
create_shape(
  shape = "star",
  size = 3,
  rotation = 15,
  fill_color = "gold",
  border_color = "orange",
  line_width = 2,
  title = "Gold Star"
)

# Example 5: Rectangle with custom dimensions
create_shape(
  shape = "rectangle",
  width = 4,
  height = 2,
  fill_color = "lavender",
  text = "Rectangle",
  text_size = 2.5
)

# Example 6: Triangle with rotation and styling
create_shape(
  shape = "triangle",
  size = 3,
  rotation = 180,
  fill_color = "pink",
  border_color = "purple",
  line_width = 2,
  text = "rpkg",
  text_color = "black",
  text_size = 4
)

# Example 7: Ellipse with custom dimensions
create_shape(
  shape = "ellipse",
  width = 5,
  height = 2,
  rotation = 30,
  fill_color = "lightcyan",
  border_color = "steelblue",
  title = "Rotated Ellipse"
)

# Example 8: Custom polygon (octagon)
create_shape(
  shape = "polygon",
  n_sides = 8,
  size = 2,
  fill_color = "thistle",
  border_color = "purple",
  title = "Octagon",
  text = "8",
  text_color = "darkblue",
  text_size = 3
)

# Example 9: Overlapping shapes (need to call par() between shapes)
create_shape(
  shape = "circle",
  x_center = 0,
  y_center = 0,
  size = 3,
  fill_color = rgb(1, 0, 0, 0.5),
  title = "Overlapping Shapes"
)

par(new = TRUE)  # Allow adding to the existing plot
create_shape(
  shape = "circle",
  x_center = 1,
  y_center = 1,
  size = 3,
  fill_color = rgb(0, 0, 1, 0.5),
  title = ""  # Empty title to avoid overwriting the previous title
)
# Create a star with text
create_shape("star", size = 2,
                       fill_color = "gold",
                       text = "*",
                       text_size = 8)

## End(Not run)

Remove last n rows or column or specified elements from a data frame like array_pop in PHP

Description

Shorthand to remove elements from a data frame and save as the same name

Usage

data_pop(., n = 1, which = c("rows", "cols"), ret = FALSE)

Arguments

Value

data with elements removed

Examples

#basic example: pop off 1 row from sample data
data.frame(ID=1:10,N=number(10)) #before
data_pop(data.frame(ID=1:10,N=number(10))) #after

#using data objects
data.01 <- mtcars[1:7,]

#task: remove 1 element from the end of the data and set it to the data name
data.01 #data.01 data before pop
data_pop(data.01) #does not return anything
data.01 #data.01 data updated after pop

#task: remove 3 columns from the end of the data and set it to the data name
data.01 #data.01 data before pop
data_pop(data.01, n = 3, which = "cols") #does not return anything, but updates data
data.01 #data.01 data updated after pop

#task: remove 5 elements from the end, but do not set it to the data name
data.01 #data.01 data before pop
data_pop(data.01,5, ret = TRUE) #return modified data
data.01 #data.01 data remains the same after pop

Remove elements from a data matching filter

Description

Shorthand to remove elements from a data frame based on filter and save as the same name

Usage

data_pop_filter(., remove)

Arguments

Value

data filtered out based on the expression

Examples

# this function removes rows matching the filter expression
data.01 <- mtcars
data.02 <- airquality

#task: remove all mpg > 20
data.01 #data.01 data before pop
data_pop_filter(data.01,mpg > 15) #computes and resaves to variable
#note: this is different from subset(data.01,data.01$mpg > 15)
data.01 #modified data after pop based on filter

#task: remove all multiple. remove all elements where Month   == 5 or Solar.R > 50
data.02 #data.02 data before pop
data_pop_filter(data.02,Month   == 5 | Solar.R > 50) #computes and resaves to variable
data.02 #modified data after pop based on filter

Add data to another data like array_push in PHP

Description

Shorthand to add data to a dataset and save as the same name

Usage

data_push(., add, which = c("rows", "cols"))

Arguments

Value

the combined dataset store to a variable with the name of the first

Examples

# initialize p1 and p2
init(p1,p2)
p1
p2

# declare p1 and p2 as data frame
p1 <- data.frame(PK=1:10,ID2=1:10)
p2 <- data.frame(PK=11:20,ID2=21:30)

p1
p2

#add p1 to p2 by row, and resave as p1
data_push(p1,p2,"rows")
# p2 # p2 remains the same
p1 #p1 has been updated

# declare a new data frame called p3
p3 <- data.frame(Hindex=number(20),Rindex=number(20,seed=20))

# add p3 to p1 as column, and resave as p1
data_push(p1,p3,"cols")
p1 # p1 has been updated

Duplicate a data rows or columns X times

Description

Add a data to itself X times by rows or columns

Usage

data_rep(., n, which = c("rows", "cols"))

Arguments

Value

the duplicated dataset store to a variable with the name of the first

Examples

# initialize p1 and p2
init(p1,p2)
p1
p2

# declare p1 and p2 as data frame
p1 <- data.frame(PK=1:10,ID2=1:10)
p2 <- data.frame(PK=11:20,ID2=21:30)

p1
p2

#add p1  twice by row, and resave as p1
data_rep(p1,n=2,"rows")
p1 #p1 has been updated


#add p2  3 times by col, and resave as p2
data_rep(p2,n=3,"cols")
p2 #p2 has been updated

Shuffle a data frame just like shuffle in PHP

Description

Shorthand to shuffle a data frame and save

Usage

data_shuffle(., which = c("rows", "cols"), seed = NULL)

Arguments

Value

shuffled data frame of items store to the data frame name

Examples

#basic example
data.frame(ID=46:55,PK=c(rep("Treatment",5),rep("Placebo",5))) #before
data_shuffle(
  data.frame(ID=46:55,PK=c(rep("Treatment",5),rep("Placebo",5)))
) #after shuffle row
data_shuffle(
  data.frame(ID=46:55,PK=c(rep("Treatment",5),rep("Placebo",5))),
  which = "cols"
) #after shuffle column


# examples using object
df1<-data.frame(ID=46:55,PK=c(rep("Treatment",5),rep("Placebo",5)))

#illustrate basic functionality
data_shuffle(df1)
df1 #shuffle and resaved to variable

data.f2<-df1
data_shuffle(data.f2)
data.f2 #first output

data.f2<-df1
data_shuffle(data.f2)
data.f2 # different output from first output top

data.f2<-df1
data_shuffle(data.f2,seed = 344L)
data.f2 #second output

data.f2<-df1
data_shuffle(data.f2,seed = 344L)
data.f2 #the same output as second output top

Combine vector to create Date, or split Date into vector

Description

Combine or split Date into a specified format

Usage

date3to1(data, out.format = "%Y-%m-%d", col.YMD = 1:3, as.vector = FALSE)

date1to3(
  data,
  in.format = "%Y-%m-%d",
  date.col = 1,
  out.cols = c("%Y", "%m", "%d")
)

Arguments

Details

NOTE for date3to1

The three input columns corresponding to "Year Month Day" must be numeric values.

For example, Do not provide the month variable as non-numeric such as "Mar", "Jul", or "Jan".

If the values of the columns are non-numeric, the results will return an "NA" in the output.date column.

Value

date derived from combining values from three columns of a data frame

Note

DATE FORMATS IN R

References

Adapted from Ecfun R package

Examples

# EXAMPLES FOR date3to1

data0 <- data.frame(y=c(NA, -1, 2001:2009),
m=c(1:2, -1, NA, 13, 2, 12, 6:9),
d=c(0, 0:6, NA, -1, 32) )
head(data0)

# combine and convert to date
# return as data frame
date3to1(data0)

# combine and convert to date
# return as vector
date3to1(data0, as.vector = TRUE) #eg. 2004-02-04


# combine and convert to date in the format DD_MM_YYYY
date3to1(data0, out.format = "%d_%m_%Y") #eg. 04_02_1974


# combine and convert to date in the format MM_DD_YY
date3to1(data0, out.format = "%m_%d_%y") #eg. 02_04_74

# combine and convert to date in the various date formats
date3to1(data0, out.format = "%B %d, %y") #eg. February 04, 74
date3to1(data0, out.format = "%a, %b %d, %Y") #eg. Mon, Feb 04, 1974
date3to1(data0, out.format = "%A, %B %d, %Y") #eg. Monday, February 04, 1974
date3to1(data0, out.format = "Day %j in Year %Y") #eg. Day 035 in Year 1974
date3to1(data0, out.format = "Week %U in %Y") #eg. Week 05 in 1974
date3to1(data0, out.format = "Numeric month %m in Year %Y") #eg. Numeric month 02 in Year 1974





# EXAMPLES FOR date1to3

data1 <- data.frame(Full.Dates =
                      c("2023-02-14",NA,NA,
                        "2002-12-04","1974-08-04",
                        "2008-11-10"))
head(data1)

# split date with default settings
# return as data frame with columns
# for day(d), month(m) and year(Y)
date1to3(data1)


# split date in the format and only return year in YYYY
date1to3(data1, out.cols = "%Y") #eg. 2002, 2023


# split date in the format and only return month in m
date1to3(data1, out.cols = "%m") #eg. 02, 12, 08

# split date in the format and return multiple date formats colums
date1to3(data1, out.cols = c("%B","%d") )
date1to3(data1, out.cols = c("%a","%b","%y") )
date1to3(data1, out.cols = c("%A","%B","%Y","%y") )
date1to3(data1, out.cols = c("%j","%Y","%y","%m") )
date1to3(data1, out.cols = c("%U","%Y","%y","%x") )
date1to3(data1, out.cols = c("%m","%Y","%y","%C") )

Detect Outliers in a Numeric Vector

Description

This function identifies outliers in a numeric vector using either the interquartile range (IQR) method or the z-score method. The IQR method defines outliers as values below Q1 - multiplier * IQR or above Q3 + multiplier * IQR, where Q1 and Q3 are the first and third quartiles. The z-score method identifies outliers as values with an absolute z-score exceeding a specified threshold.

Usage

detect_outlier(
  x,
  method = "iqr",
  multiplier = 1.5,
  z_threshold = 3,
  na.rm = TRUE,
  groups = NULL,
  summary = FALSE
)

iqr_outlier(x, multiplier)

zscore_outlier(x, z_threshold)

zscore_outlier2(x, z_threshold)

Arguments

Details

The function returns a list containing the outliers, their indices, detection bounds or thresholds, and a logical vector indicating which elements are outliers. If a grouping vector is provided via 'groups', outlier detection is performed separately for each group, and results are returned as a nested list by group. If 'na.rm = TRUE' (default), missing values ('NA') are removed before computation. If 'na.rm = FALSE' and 'NA' values are present, the function stops with an error. The function also stops for non-numeric input, insufficient valid data, or mismatched group lengths.

The function requires at least two non-'NA' values per group (if 'groups' is provided) or overall (if 'groups = NULL') to compute meaningful statistics when 'na.rm = TRUE'. If 'na.rm = FALSE', the presence of 'NA' values triggers an error. If all values in a group are identical or there are insufficient data points, an error is thrown for that group. The IQR method is robust to non-normal data, while the z-score method assumes approximate normality and is sensitive to extreme values.

Value

If 'groups = NULL' (default), a list with the following components: - 'outliers': A numeric vector of the outlier values. - 'indices': An integer vector of the indices where outliers occur in the input vector. - 'bounds' (if 'method = "iqr"'): A named numeric vector with the 'lower' and 'upper' bounds for outliers. - 'threshold' (if 'method = "zscore"'): A named numeric vector with the 'lower' and 'upper' z-score thresholds. - 'is_outlier': A logical vector of the same length as 'x', where 'TRUE' indicates an outlier. - 'summary' (if 'summary = TRUE'): A list with summary statistics including the mean, median, standard deviation (for z-score), quartiles (for IQR), and number of outliers.

If 'groups' is provided, a named list where each element corresponds to a unique group, containing the same components as above but computed separately for that group’s values.

Examples

# Example 1: Basic IQR method without groups
x <- c(1, 2, 3, 4, 100)
detect_outlier(x)

# IQR method with summary
detect_outlier(x, summary = TRUE)

# Z-score method with custom threshold
y <- c(-10, 1, 2, 3, 4, 5, 20)
detect_outlier(y, method = "zscore", z_threshold = 2.5)

# Handling missing values
z <- c(1, 2, NA, 4, 5, 100)
detect_outlier(z, method = "iqr", multiplier = 3)

# Example 2: IQR method with groups
x2 <- c(1, 2, 3, 100, 5, 6, 7, 200)
groups2 <- c("A", "A", "A", "A", "B", "B", "B", "B")
detect_outlier(x2, groups = groups2)

# Example 3: Z-score method with groups and summary
x3 <- c(-10, 1, 2, 20, 3, 4, 5, 50)
groups3 <- c("X", "X", "X", "X", "Y", "Y", "Y", "Y")
detect_outlier(x3, method = "zscore", z_threshold = 2, groups = groups3, summary = TRUE)

# Example 4: IQR method with groups and NA values
x4 <- c(1, 2, NA, 100, 5, 6, 7, 200,1000)
groups4 <- c("G1", "G1", "G1", "G1", "G2", "G2", "G2", "G2","G1")
detect_outlier(x4, groups = groups4)

# Error cases
## Not run: 
detect_outlier(c("a", "b"))  # Non-numeric input
detect_outlier(c(1), groups = c("A"))  # Insufficient data
detect_outlier(c(1, 2), groups = c("A"))  # Mismatched group length
detect_outlier(c(1, NA, 3), na.rm = FALSE)  # NA with na.rm = FALSE

## End(Not run)

Advanced Outlier Detection in Numeric Data with Optional Grouping

Description

Detects outliers in numeric data using multiple statistical methods and provides comprehensive analysis including visualizations and summary statistics. Supports grouped analysis when group vector is provided. The function supports three detection methods: IQR, Z-score, and Modified Z-score.

Usage

detect_outlier2(
  x,
  groups = NULL,
  method = c("iqr", "zscore", "modified_zscore", "all"),
  multiplier = 1.5,
  z_threshold = 3,
  modified_z_threshold = 3.5,
  plot = TRUE,
  plot_groups = TRUE
)

Arguments

Value

A list of class "outlier_analysis" containing:

• overall: Overall analysis results

• by_group: Group-specific results (if groups provided)

• plots: List of generated plots

Examples

# Example 1: Basic grouped analysis
set.seed(123)
data <- c(rnorm(50), rnorm(50, 2), rnorm(50, 4))
groups <- rep(c("A", "B", "C"), each = 50)
resultA <- detect_outlier2(data) # no groups
resultB <- detect_outlier2(data, groups = groups) # with groups

# Example 2: Custom thresholds by group
test_scores <- c(65, 70, 75, 72, 68, 73, 78, 71, 69, 74,
                 90, 85, 92, 88, 95, 87, 91, 89, 86, 93)
class_groups <- rep(c("Morning", "Afternoon"), each = 10)
result <- detect_outlier2(test_scores,
                                   groups = class_groups,
                                   method = "all",
                                   z_threshold = 2)
result$overall
result$by_group
result$plots$overall$boxplot()
result$plots$overall$density()
result$plots$overall$comparison()
result$plots$by_group$Morning$boxplot()
result$plots$by_group$Morning$density()
result$plots$by_group$Afternoon$boxplot()
result$plots$by_group$Afternoon$density()

Duplicate a file with global text replacement

Description

Shorthand to return a re-sample number of rows in a data frame by unique column

Usage

duplicate(file, new.name, pattern = NULL, replacement = NULL, open = TRUE)

Arguments

Value

data frame containing re-sampled rows from an original data frame

Examples

if(interactive()){
# example to duplicate a file, and replace text1 within it
# NOTE that, by default, this function will also open the file within RStudio

#create sample file
file1s <- paste0(tempfile(),".R")
writeLines("message(
'Sample items: eggs, coke, fanta, book'
)", file1s)

file2s <- paste0(tempfile(),".R")
file3s <- paste0(tempfile(),".R")

duplicate(
  file = file1s,
  new.name = file2s,
  pattern = 'text1',
  replacement = 'replacement1'
 )

# duplicate the file, with multiple replacements
# replace 'book' with 'egg' and 'coke' with 'fanta'
duplicate(
  file1s, file2s,
  pattern = c('book','coke'),
  replacement = c('egg','fanta')
)



# duplicate the file with no replacement
duplicate(file1s,file3s) # this simply performs file.copy, and opens the new file



# duplicate the file but do not open for editing
duplicate(file1s,file3s, open = FALSE) # this does not open file after duplication
}

Error coalescing operator

Description

Alternative return for error statements.Return alternative if the value of expression is erroneous

Usage

error.out(test, alternative = "")

test %eo% alternative

Arguments

Value

value of test if error, else return value of alternative

Examples

# The following statement would produce
# error because 'stat1' does not exist

# stat1 + 1

# To prevent the statement from
# stopping the process, when can have alternative out
alt = 0
error.out(stats1 + 1, alt)

Extract all IP addresses from a string

Description

Parse a string and vectorize the IP addresses within it

Usage

extract_IP(input)

Arguments

Value

extract_IP returns a vector of IP addresses

Examples

# Extract all IP addresses from a string

# Example 1
# This example demonstrates the separate
# extraction of an IP address one per vector element:
str1 = c("Two IP(66.544.33.54) addresses
were discovered in the scan.
One was at 92.234.1.0.",
"The other IP was 62.3.45.255.")
extract_IP(str1)

# Example 2
# This example demonstrates the extraction
# of multiple IP addresses from a single vector element:
str2 = "Two IP addresses were discovered
in the scan. One was at 92.234.1.0.
The other IP was 62.3.45.255."
extract_IP(str2)

Extract all comments or functions from a file

Description

Vectorize all comments from the file

Usage

extract_comment(file)

remove_content_in_quotes(line)

remove_comment(line)

clean_file(file, output_file)

get_func_def(file)

Arguments

Value

vector of all comments within a file

Examples

## Not run: 
ex_file1 <- "path/file1.R"
# get all comments
cmmts <- extract_comment(ex_file1)
cmmts

## End(Not run)

## Not run: 
# Ex to clean out comments from file
file_path <- ".testR"
output_file_path <- ".cleaned_script.R"
clean_file(file_path, output_file_path)

## End(Not run)

# example code

## Not run: 
# Ex to get all defined functions
# within a file
file_path <- ".testR"
get_func_def(file_path)

## End(Not run)

Append date to filename

Description

Add today's date to the filename

Usage

fAddDate(..., format = "%d-%b-%Y")

Arguments

Details

The present function enables users to add the current date to the file name, facilitating the straightforward saving of files with their respective dates. It accepts different file paths and names as arguments, as demonstrated in the example section. This functionality simplifies the process of associating a file's creation date with its name, aiding users in recalling when a file was saved. Moreover, it serves as a preventive measure against unintentional overwriting of files created on different dates.

Value

file name with the current date added as suffix

Examples

# Task 1
fAddDate("path1/","path2/filepre","filemid","fileend.png")

# Task 2
fAddDate(c("path1/","path2/"),"filepre","filemid","fileend.png")

# Task 3
fAddDate("one_file_name_fileend.pdf")

# Task 4
fAddDate(c("path1/","path2/"),"file1-","filemid",c("fileend.png",".pdf"))

# Task 5
data("USArrests")
USArrests$fn = paste0(row.names(USArrests), ".txt")
head(fAddDate(USArrests$fn),10)

# Task 6: format date - month.day.year
fAddDate("sample_file_name.pdf", format = "%B.%d.%YYYY")

# Task 7: format date - year_month
fAddDate("sample_file_name.pdf", format = "%YYYY_%m")

Fetch R package based on keyword

Description

This function gets R packages based on a keyword in their description.

Usage

find_packages(keyword)

Arguments

Value

character vector of matching package names or NULL if no matches.

Note

the function in its current form only searches available.packages()

Examples

# find the list of R packages for data or machine learning

matched_pkgs <- suppressWarnings(find_packages("plotting"))
matched_pkgs

matched_pkgs <- suppressWarnings(find_packages("machine learning"))
matched_pkgs

Create Tensor-Like Slices from a Data Frame or Matrix

Description

This function converts a data frame or matrix into row-wise slices, similar to TensorFlow's 'from_tensor_slices()'.

Usage

from_tensor_slices(data)

Arguments

Value

A list of slices, each corresponding to a row of the input data.

Examples

df <- data.frame(A = 1:3, B = c("x", "y", "z"))
from_tensor_slices(df)

Calculate geometric coefficient of variation, mean, or SD and round

Description

Calculate the coefficient of variation

Calculate the geometric mean

Calculate the geometric standard deviation

Usage

geo.cv(num, round = 2, na.rm = TRUE, neg.rm = TRUE, pct = TRUE)

geo.mean(num, round = 2, na.rm = TRUE, neg.rm = TRUE)

geo.sd(num, round = 2, na.rm = TRUE, neg.rm = TRUE)

Arguments

Value

the geometric cv of a set of numbers

the geometric mean of a set of numbers

the geometric standard deviation of a set of numbers

Examples

#simulate numbers using a fixed seed
num1 <- number(n = 1115,max.digits = 4, seed = 10)

#get geometric CV, represent as percent and round to 2 decimal places
geo.cv(num1,round = 2) # result: 60.61%

#or round to 3 decimal places
geo.cv(num1,round = 3) # result: 60.609%

#by default, the above examples return a CV%
#if you do not want the result as percentage, specify "pct"
geo.cv(num1,pct = FALSE) # result: 0.61

num1 <- sample(300:3000,10)

#get the geometric mean, excluding all negatives and round to 2
geo.mean(num1)

#or
geo.mean(num1)


#get geometric mean, but round the final value to 5 decimal places
geo.mean(num1, round = 5)

num1 <- sample(330:400,20)

#get geometric SD remove negative values and round to 2 decimal places
geo.sd(num1)

#get geometric SD, DON'T remove negative values and round to 2 decimal places
geo.sd(num1,na.rm=FALSE)

#get geometric SD, remove negative values and round to 3 decimal places
geo.sd(num1,round = 3)

Extract all dates from a string

Description

Extract all dates from a string

Usage

getDate(str1, out.format = "%Y-%m-%d")

Arguments

Note

DATE FORMATS IN R

Examples

str1 <- "The video was recorded on July 19, 2023."
str2 <- "The video was recorded over a 4 hour period
starting on July 19, 2023."
str3 <- "The first batch aug 12,2024 of aug 12,
2024 reports are due on July 12, 2024; the second
batch on 7/19/24."
str4 <- c("On 3.12.25, Jerry is taking one month
05.13.1895 of leave and is not scheduled to
return until around 4-9-2025.", "The staff
will be out on training on 10/11/24,
Oct 12, 2024, and 10-13-24.")
getDate(str1)
getDate(str2, out.format = "%Y-%m-%d")
getDate(str3, out.format = "%m-%d/%Y")
getDate(str4)

Fetch GitHub Repository Creation & Last Updated Date

Description

The GitHub REST API is a powerful tool that allows developers to interact with GitHub programmatically. It provides a set of endpoints that allows a user to create integration, retrieve data, and automate workflows related to GitHub repositories. It is a means by which users can interact with GitHub without directly using a web interface.

Usage

getGitRepoStart(repo_name, out.format = "%Y-%m-%d")

getGitRepoChange(repo_name, out.format = "%Y-%m-%d")

Arguments

Details

The two functions utilize the GitHub REST API to extract important temporal information about a GitHub repository.

- the getGitRepoStart function is used to retrieve the date a GitHub repository was first created.

- the getGitRepoChange function retrieves the date a GitHub repository was last updated.

Value

date of creation of repository as a character

date of the last update of repository as a character

Examples

# Use default date format
getGitRepoStart(repo_name = "oobianom/quickcode")

# Specify date format
getGitRepoStart(repo_name = "oobianom/quickcode", out.format = "%j|%Y")
getGitRepoStart(repo_name = "oobianom/quickcode", out.format = "%D|%j")



getGitRepoChange(repo_name = "oobianom/shinyStorePlus", out.format = "%d-%b-%Y")
getGitRepoChange(repo_name = "oobianom/r2social", out.format = "%Y/%m/%d")

Convert Dates into Numeric Week Counts

Description

Convert the range of date to number of weeks

Usage

getWeekSeq(start_date, end_date, dates, in.format = "%m/%d/%y")

is.Date(x)

not.Date(x)

is.leap(yyyy)

Arguments

Value

data frame of the dates along with their corresponding week

Examples

# simple example with start and end date
getWeekSeq(start_date="12/29/25",end_date="1/8/26")

# enter specific dates instead
# specify format
getWeekSeq(
dates = c(
  '2025-12-29',
  '2025-12-30',
  '2025-12-31',
  '2026-01-01',
  '2026-01-04',
  '2026-01-05',
  '2026-01-06',
  '2026-01-07',
  '2026-01-08'),
  in.format = "%Y-%m-%d"
)

getWeekSeq(
dates = c(
  '12/29/25',
  '12/30/25',
  '12/31/25',
  '01/01/26',
  '01/02/26',
  '01/03/26',
  '01/06/26',
  '01/07/26',
  '01/08/26'
  ),
  in.format = "%m/%d/%y"
)

Check if a call or expression produces errors

Description

Whether a function or series of calls results in error

Usage

has.error(...)

Arguments

Value

boolean value to indicate if the expression produces errors

Note

More information, check: https://rpkg.net/package/quickcode

Examples

# this should not produce error
# so the function result should be FALSE
has.error({
  x = 8
  y = number(10)
  res = x + y
})

# this should produce the following error
# Error in x + y : non-numeric argument to binary operator
# so the function result should be TRUE
has.error({
  x = 8
  y = "random"
  res = x + y
})

# this should result in error because
# the dataset does not contain a "rpkg.net" column
# the result should be TRUE
df1 = mtcars
has.error(df1[,"rpkg.net"])

Snippet function to add header to a current Rmd opened file

Description

Shorthand to add Rmd header

Usage

header.rmd()

Value

Inserts header content for Rmd file

Examples

if(interactive())
header.rmd()

If number falls within a range of values and get closest values

Description

With a defined range of values, the function systematically examines each provided number to determine if it falls within the specified range. It may also provide the values with the range that are closest to a desired number.

Usage

in.range(
  value,
  range.min,
  range.max,
  range.vec = NULL,
  closest = FALSE,
  rm.na = FALSE
)

Arguments

Details

The described function serves the purpose of checking whether a given number or set of numbers falls within a specified range. It operates by taking a range of values as input and then systematically evaluates each provided number to determine if it lies within the defined range. This function proves particularly useful for scenarios where there is a need to assess numeric values against predefined boundaries, ensuring they meet specific criteria or constraints. In the same manner, this function allows the user to also retrieve values within the range that are closest to each provided number.

Value

boolean to indicate if the value or set of values are within the range

Note

The argument range.vec is utilized when users opt not to employ the range.min or range.max arguments. If range.vec is specified, range.min and range.max are disregarded. It's important to note that the use of range.vec is optional.

Examples

# Task 1: Check if a number is within specified range
in.range(5, range.min = 3, range.max = 10) # TRUE
in.range(25, range.min = 12, range.max = 20) # FALSE

# Task 2: Check if a set of values are within a specified range
in.range(1:5, range.min = 2, range.max = 7) #
in.range(50:60, range.min = 16, range.max = 27) #


# Task 3: Check if a number is within the range of a set of numbers
in.range(5, range.vec = 1:10) # TRUE
in.range(345, range.vec = c(1001,1002,1003,1004,1005,
1006,1007,1008,1009,1010,1011,1012,1013,1014)) # FALSE

# Task 4: Check if a set of values are within the range of a set of numbers
in.range(1:5, range.vec = 4:19) #
in.range(50:60, range.vec = c(55,33,22,56,75,213,120)) #

# Task 5: remove NAs prior to processing
in.range(c(1,3,NA,3,4,NA,8), range.min = 4, range.max = 6, rm.na = FALSE) # do not remove NA
in.range(c(1,3,NA,3,4,NA,8), range.min = 4, range.max = 6, rm.na = TRUE) # remove NA
#in.range(c(NA), range.min = 4, range.max = 6, rm.na = TRUE) #This will return error

# Task 6: return the closest number to the value
in.range(5:23, range.vec = 7:19, closest = TRUE)
in.range(-5:10, range.vec = -2:19, closest = TRUE)
in.range(c(1:5,NA,6:9), range.vec = 4:19, closest = TRUE)
in.range(c(1:5,NA,6:9), range.vec = 4:19, closest = TRUE, rm.na = TRUE)

Increment vector by value

Description

Increment the content of a vector and re-save as the vector

Usage

inc(., add = 1L)

Arguments

Details

This function is very useful when writing complex codes involving loops. Apart from the for loop, this can be useful to quickly increment a variable located outside the loop by simply incrementing the variable by 1 or other numbers. Check in the example section for a specific use. Nonetheless, one may also choose to use this function in any other instance, as it's simple purpose is to increase the value of a variable by a number and then re-save the new value to that variable.

Value

a vector incremented by a number

Examples

num1 <- sample(330:400,10)
num1#before increment

# increment num1 by 1
inc(num1)
num1 #after increment

# increment num1 by 5
num1 #before increment
inc(num1, add= 10)
num1 #after increment

#when used in loops

#add and compare directly
rnum = 10
inc(rnum) == 11 #returns TRUE
rnum #the variable was also updated

# use in a for loop
ynum = 1
for( i in c("scientist","dancer","handyman","pharmacist")){
message("This is the item number ")
message(ynum)
message(". For this item, I am a ")
message(i)

#decrement easily at each turn
plus(ynum)
}


#use in a repeat loop
xnum = 1
repeat{ #repeat until xnum is 15
message(xnum)
if(inc(xnum) == 15) break
}

Initialize new variables and objects

Description

Shorthand to initialize one or more objects

Usage

init(..., value = NULL)

Arguments

Value

initialized objects set to the value specified

Examples

init(t,u,v)
message(t) # t = NULL
message(u) # u = NULL
message(v) # v = NULL
init(j,k,m,value = 7)
message(j) # j = 7
message(k) # k = 7
message(m) # m = 7

Shiny app function to insert string to current file in RStudio

Description

Shorthand to insert content to opened file

Usage

insertInText(string)

Arguments

Value

Inserts into current position on opened file

Examples

if(interactive()){
insertInText('hello rpkg.net')
insertInText('hello world')
}

Is file name extension(s) an image

Description

Check if one or multiple file name entry is an image

Usage

is.image(x)

Arguments

Details

This current function tests if the extension of the file name provided belongs to any of the image extensions listed below
AI - Adobe Illustrator
BMP - Bitmap Image
CDR - Corel Draw Picture
CGM - Computer Graphics Metafile
CR2 - Canon Raw Version 2
CRW - Canon Raw
CUR - Cursor Image
DNG - Digital Negative
EPS - Encapsulated PostScript
FPX - FlashPix
GIF - Graphics Interchange Format
HEIC - High-Efficiency Image File Format
HEIF - High-Efficiency Image File Format
ICO - Icon Image
IMG - GEM Raster Graphics
JFIF - JPEG File Interchange Format
JPEG - Joint Photographic Experts Group
JPG - Joint Photographic Experts Group
MAC - MacPaint Image
NEF - Nikon Electronic Format
ORF - Olympus Raw Format
PCD - Photo CD
PCX - Paintbrush Bitmap Image
PNG - Portable Network Graphics
PSD - Adobe Photoshop Document
SR2 - Sony Raw Version 2
SVG - Scalable Vector Graphics
TIF - Tagged Image File
TIFF - Tagged Image File Format
WebP - Web Picture Format
WMF - Windows Metafile
WPG - WordPerfect Graphics

Value

a boolean value to indicate if entry is an image

Examples

img.1 <- "fjk.jpg"
is.image(img.1)

img.0 <- "fjk.bbVG"
is.image(img.0)

img.2 <- "fjk.bmp"
is.image(img.2)

img.3 <- "fjk.SVG"
is.image(img.3)

# a vector of file names
v <- c("logo.png", "business process.pdf",
"front_cover.jpg", "intro.docx",
"financial_future.doc", "2022 buybacks.xlsx")

is.image(v)


# when the file name has no extension
# the function returns NA
v2 <- c("img2.jpg","northbound.xlsx","landimg",NA)
is.image(v2)

Check is numbers in a vector are decreasing or increasing

Description

Test if numeric values of a vector are decreasing or increasing using the radix method

Usage

is.increasing(., na.last = TRUE)

is.decreasing(., na.last = TRUE)

Arguments

Value

boolean value to indicate if the values are increasing or decreasing

Examples

# example code
doy1 <- rnorm(1e3)
doy2 <- sort(doy1, decreasing = FALSE)
doy3 <- sort(doy1, decreasing = TRUE)

is.increasing(doy1)
is.decreasing(doy1)

is.increasing(doy2)
is.decreasing(doy2)

is.increasing(doy3)
is.decreasing(doy3)

Check if a data fits the distribution

Description

Check whether a vector of data contains values that fit a distribution

Usage

is.lognormal(values, alpha = 0.05, method = 1)

is.normal(values, alpha = 0.05, method = 1)

is.uniform(values, alpha = 0.05)

is.poisson(values, alpha = 0.05)

is.gamma(values, alpha = 0.05)

is.logistic(values, alpha = 0.05)

is.weibull(values, alpha = 0.05)

is.cauchy(values, alpha = 0.05)

setDisAlpha(alpha = 0.05)

unsetDisAlpha()

getDistribution(data, alpha = 0.05)

Arguments

Details

This function takes a numeric vector as its input. This vector contains the dataset that will be analyzed.

For Normal and LogNormal:

- Method 1: we perform the Shapiro-Wilk test on the (log-transformed) data to test for normality. The null hypothesis of the Shapiro-Wilk test is that the data are normally distributed. If the p-value is greater than the chosen significance level (typically 0.05), we fail to reject the null hypothesis, indicating that the data may follow a log-normal distribution.

- Method 2: we perform the Kolmogorov-Smirnov test on the log-transformed data, comparing it to a normal distribution with the same mean and standard deviation. Again, if the p-value is greater than the chosen significance level, it suggests that the data may follow a log-normal distribution. These tests provide a statistical assessment of whether your data follows a log-normal distribution.

Value

boolean value if lognormal distributed

boolean value if normal distributed

boolean value if uniform distributed

boolean value if poisson distributed

boolean value if gamma distributed

boolean value if logistic distributed

boolean value if logistic distributed

boolean value if cauchy distributed

setDisAlpha sets global significance level for testing of distribution

unsetDisAlpha removes global significance level for testing of distribution

Note

getDistribution() checks if data fits multiple distributions

Examples

# Set global alpha for testing significance
setDisAlpha(alpha = 0.05)

# Prepare all data to test
# Set the seed for reproducibility
set.seed(13200323)
lognormal_data <- stats::rlnorm(n = 4000, meanlog = 1, sdlog = 1) #lognormal data
normal_data <- stats::rnorm(n = 4000, mean = 10, sd = 3) #normal data
uniform_data <- stats::runif(4000,min=0,max=10) #uniform data
poisson_data <- stats::rpois(4000, lambda = 5) #poisson data
gamma_data <- stats::rgamma(4000,shape = 5, rate = 2) #gamma data
logis_data <- stats::rlogis(4000, location = 4, scale = 2)#logistic values
weibull_data <- stats::rweibull(4000, shape = 4, scale = 2) #weibull data
cauchy_data <- stats::rcauchy(4000, location = 8, scale = 5) #cauchy data

# EXAMPLE FOR is.lognormal

# Test if the data is lognormal
is.lognormal(lognormal_data)
is.lognormal(normal_data)
is.lognormal(uniform_data)
is.lognormal(poisson_data)
is.lognormal(gamma_data)
is.lognormal(logis_data)
is.lognormal(weibull_data)
is.lognormal(cauchy_data)
is.lognormal(1:4000)

# EXAMPLE FOR is.normal

# Test if the data fits a normal distribution
is.normal(lognormal_data)
is.normal(normal_data)
is.normal(uniform_data)
is.normal(poisson_data)
is.normal(gamma_data)
is.normal(logis_data)
is.normal(weibull_data)
is.normal(cauchy_data)
is.normal(1:4000)

## Not run: 
# EXAMPLES for is.uniform

# Test if the data fits a uniform distribution
is.uniform(lognormal_data)
is.uniform(normal_data)
is.uniform(uniform_data)
is.uniform(poisson_data)
is.uniform(gamma_data)
is.uniform(logis_data)
is.uniform(weibull_data)
is.uniform(cauchy_data)
is.uniform(1:4000)

## End(Not run)
## Not run: 
# EXAMPLE for is.poisson

# Test if the data fits a poisson distribution
is.poisson(lognormal_data)
is.poisson(normal_data)
is.poisson(uniform_data)
is.poisson(poisson_data)
is.poisson(gamma_data)
is.poisson(logis_data)
is.poisson(weibull_data)
is.poisson(cauchy_data)
is.poisson(1:4000)

## End(Not run)
## Not run: 
# EXAMPLE for is.gamma

# Test if the data fits a gamma distribution
is.gamma(lognormal_data)
is.gamma(normal_data)
is.gamma(uniform_data)
is.gamma(poisson_data)
is.gamma(gamma_data)
is.gamma(logis_data)
is.gamma(weibull_data)
is.gamma(cauchy_data)
is.gamma(1:4000)

## End(Not run)
## Not run: 
# EXAMPLE for is.logistic

# Test if the data fits a logistic distribution
is.logistic(lognormal_data)
is.logistic(normal_data)
is.logistic(uniform_data)
is.logistic(poisson_data)
is.logistic(gamma_data)
is.logistic(logis_data)
is.logistic(weibull_data)
is.logistic(cauchy_data)
is.logistic(1:4000)

## End(Not run)
## Not run: 
# Test if the data fits a weibull distribution
is.weibull(lognormal_data)
is.weibull(normal_data)
is.weibull(uniform_data)
is.weibull(poisson_data)
is.weibull(gamma_data)
is.weibull(logis_data)
is.weibull(weibull_data)
is.weibull(cauchy_data)
is.weibull(1:4000)

## End(Not run)
## Not run: 
# EXAMPLES for is.cauchy

# Test if the data fits a cauchy distribution
is.cauchy(lognormal_data)
is.cauchy(normal_data)
is.cauchy(uniform_data)
is.cauchy(poisson_data)
is.cauchy(gamma_data)
is.cauchy(logis_data)
is.cauchy(weibull_data)
is.cauchy(cauchy_data)
is.cauchy(1:4000)

## End(Not run)
## Not run: 
# set global distribution alpha

# default setting
setDisAlpha()

# set to 0.001
setDisAlpha(alpha = 0.01)

## End(Not run)
## Not run: 
# unset global distribution alpha

unsetDisAlpha()

## End(Not run)

Go back to previous directory

Description

Navigate to previous working directory if the setwd was called by clean() or refresh() function

Usage

lastwd()

Value

the previous directory

Note

In order to use this function to retrieve the last/previous working directory, you must use the quickcode::clean() or quickcode::refresh() function to set the working directory.

Examples

lastwd()

Learning Rate Scheduler

Description

This function adjusts the learning rate at each epoch according to a custom schedule.

Usage

learn_rate_scheduler(initial_lr, schedule, epochs)

Arguments

Value

A numeric vector representing the learning rate for each epoch.

Examples

schedule <- function(epoch) { 0.01 * (0.9 ^ epoch) }
learn_rate_scheduler(0.01, schedule, epochs = 10)

Load specific R libraries and clear environment

Description

Load specific packages, print a list of the loaded packages along with versions. Only include libraries, don't install if library doesn't exist

Usage

libraryAll(
  ...,
  lib.loc = NULL,
  quietly = FALSE,
  clear = TRUE,
  clearPkgs = FALSE
)

Arguments

Value

loaded libraries and clear environment

Examples

# load packages and print their versions to the console
libraryAll(base) #one package

libraryAll(
  base,
  tools,
  stats
) #multiple packages

libraryAll("grDevices") #with quotes

libraryAll(
  stats,
  utils,
  quietly = TRUE
) #load quietly

libraryAll(
  base,
clear = FALSE) #do not clear console after load

# clear previously loaded packages, then load r2resize and r2social
libraryAll(
  stats,
  utils,
  clearPkgs = TRUE
)

Add elements to a list like array_push in PHP

Description

Shorthand to add elements to a vector and save as the same name

Usage

list_push(., add)

Arguments

Value

vector combining fist and second vector, but have name set to the first

Examples

num1 <- list(sample(330:400,10))
num2 <-list("rpkg.net")
list_push(num1, add= num2)

Shuffle a list object just like shuffle in PHP

Description

Shorthand to shuffle a list and save

Usage

list_shuffle(., seed = NULL)

Arguments

Value

shuffled list of items store to the list name

Examples

list001 <- list("a" = 1:5,
           "b" = letters[1:5],
           c = LETTERS[1:10],
           "2" = number(5,5),
           "e" = randString(5,5))
list001 #show initial list

#illustrate basic functionality
list_shuffle(list001)
list001 #shuffle and resaved to variable

list.f2<-list001
list_shuffle(list.f2)
list.f2 #first output

list.f2<-list001
list_shuffle(list.f2)
list.f2 # different output from first output top

list.f2<-list001
list_shuffle(list.f2,seed = 344L)
list.f2 #second output

list.f2<-list001
list_shuffle(list.f2,seed = 344L)
list.f2 #the same output as second output top

Miscellaneous math computations: Corresponding m-m and quantile for confident intervals

Description

Compute the corresponding quantile given confident interval bounds

Usage

math.qt(ci = 0.9)

math.mm(Vmax, S, Km, V, round = NULL)

Arguments

Value

vector of two numeric values for the quantile based on the confident interval chosen

result of calculation of Michaelis-Menten equation

Examples

# Get the bounds for 90% confident intervals
math.qt(0.9)

# Get the bounds for 95% confident intervals
# use the bounds to obtain quartile
values = number(100)
values
ci = math.qt(0.95)
getquart = quantile(values, probs = ci)
getquart

math.mm(3,500,0.5)

Decrease vector by value

Description

decrease the content of a vector and re-save as the vector

Usage

minus(., minus = 1L)

Arguments

Details

Similar to the inc and plus functions, the minus function is very useful when writing complex codes involving loops. Apart from the for loop, minus can be useful to quickly decrease the value of a variable located outside the loop by simply decreement the variable by 1 or other numbers. Check in the example section for a specific use. Given the scope, one may also choose to use this function in any other instances, as it's simple purpose is to decrease the value of a variable by a number and then re-save the new value to that variable.

Value

a vector decreased by a number

Examples

num1 <- sample(5:150,10)
num1

# decrease num1 by 1
num1 #before decrease
minus(num1)
num1 #after decrease

# decrease num1 by 5
num1 #before decrease
minus(num1, minus = 5)
num1 #after decrease



#when used in loops

#add and compare directly
rnum = 23
minus(rnum) == 220 #returns FALSE
rnum #the variable was also updated


# use in a for loop
ynum = 100

for( i in c("teacher","student","lawyer","pharmacist")){
message("This is the item number ")
message(ynum)
message(". For this item, I am a ")
message(i)

#decrement easily at each turn
minus(ynum,3)
}

#use in a repeat loop
xnum = 100
repeat{ #repeat until xnum is 85
message(xnum)
if(minus(xnum) == 85) break
}

Mix or Blend two or more colors

Description

Combine colors to generate a new color

Usage

mix.color(color, type = 2, alpha = 1)

Arguments

Value

hex for the combined color

Examples

# color vector
colvec <- c("red", "blue", "violet", "green", "#ff0066")

# just one color
mix.color(colvec[1], type = 1, alpha = 1)

# add two colors
mix.color(colvec[1:2], type = 1, alpha = 1)

# add three colors
mix.color(colvec[1:3], type = 1, alpha = 1)


# return type = 2

# just one color
mix.color(colvec[1], type = 2, alpha = 1)

# add two colors
mix.color(colvec[1:2], type = 2, alpha = 1)

# add three colors
mix.color(colvec[1:3], type = 2, alpha = 1)


# opacity or alpha  0.5

# just one color
mix.color(colvec[1], type = 1, alpha = 0.5)

# add two colors
mix.color(colvec[1:2], type = 1, alpha = 0.5)

# add three colors
mix.color(colvec[1:3], type = 1, alpha = 0.5)

# add all colors
mix.color(colvec, type = 1, alpha = 0.5)

Mix or Blend colors between two or more colors

Description

Mix or blend multiple colors between two colors

Usage

mix.cols.btw(colors, max = 20, alpha = 1, preview = FALSE)

Arguments

Value

color hex for all generated colors

Examples

# simply mix/blend two colors
mix.cols.btw(c("red","brown"))

# simply mix/blend two colors, maximum number of colors at the end
mix.cols.btw(c("red","brown"), max = 8)

# simply mix/blend two colors with alpha=0.2 (opacity=0.2)
mix.cols.btw(c("yellow","green"),alpha = 0.2)

# also preview after mixing the two colors
mix.cols.btw(c("red","green"), preview = TRUE)
mix.cols.btw(c("blue","violet"),alpha = 0.2, preview = TRUE)

mix.cols.btw(c("red","purple","yellow","gray"), preview = TRUE)

mix.cols.btw(c("red","purple","yellow","gray"),alpha = 0.2, preview = TRUE)

Calculate the Mode of a Numeric or Character Vector

Description

This function calculates the mode (most frequently occurring value(s)) of a numeric or character vector.

Usage

mode.calc(x)

Arguments

Value

The mode(s) of the input vector. If multiple values have the same highest frequency, all modes are returned. Returns 'NA' if the input vector is empty.

Examples

# Example with a numeric vector
numeric_vector <- c(1, 2, 2, 3, 3, 3, 4, 5)
mode.calc(numeric_vector)

# Example with a character vector
character_vector <- c("apple", "banana", "apple", "orange", "banana", "banana")
mode.calc(character_vector)

Multi-Head Attention

Description

This function implements a simplified version of multi-head attention for sequence data.

Usage

multihead_att(query, key, value, num_heads)

Arguments

Value

A numeric matrix representing the attention output.

Examples

query <- matrix(rnorm(12), nrow = 3)
key <- matrix(rnorm(12), nrow = 3)
value <- matrix(rnorm(12), nrow = 3)
multihead_att(query, key, value, num_heads = 2)

Mutate only a subset of dataset intact

Description

Extension of the dplyr::mutate function that allows the user to mutate only a specific filtered subset of a data, while leaving the other parts of the data intact

Usage

mutate_filter(., sub.set, ...)

Arguments

Value

data frame containing original data, but with a subset mutated

Examples

#mutate a subsection filter of mtcars
dt = mtcars
names(dt)
head(dt)
mutate_filter(dt,mpg == 21.0 & cyl == 6, cyl=1000,hp=2000,vs=hp*2)


dt2 = beaver1
names(dt2)
head(dt2)
mutate_filter(dt2, day == 346 & time < 1200, activ = 12, temp = round(temp*10,1))

Cumulative Sum with NA Removal

Description

This function calculates the cumulative sum of a numeric vector, removing any 'NA' values before computation.

Usage

na.cumsum(x)

Arguments

Value

A numeric vector representing the cumulative sum with 'NA' values removed. Returns an empty vector if the input is empty or contains only 'NA' values.

Examples

# Example with numeric vector
numeric_vector <- c(1, 2, NA, 4, 5)
na.cumsum(numeric_vector)

# Example with all NAs
na.cumsum(c(NA, NA))

Count the number of decimal places

Description

Count the number of decimal places

Usage

ndecimal(num)

Arguments

Examples

#example 1
ndecimal(9.000322)

#example 2
ndecimal(34)

#example 3
ndecimal(45.)

Create and use a super variable with unique capabilities

Description

Create a variable that supersedes other variables and has various functionalities

Usage

newSuperVar(variable, value = 0L, lock = FALSE, editn = NULL)

Arguments

Value

no visible return, but variable is created and stored with various functionalities

Note

What you should know about the functionality:

This function ensures that a variable is created and may not easily be altered. It helps preserve the original variable by providing only limited access to the variable.

Creation of this super variable automatically attached some key functions to it, such that the user is able to call the function like .set(), .rm().

Super variable value may be set from any scope using the .set() function, which means that it is granted global variable features without being present within the global environment of the current section.

The variable name of the super variable may be overwritten in the local environment, but this would not alter the super variable. It means that once the local variable is removed, the super variable remains available for use.

Use cases:

- Preserve originality of variable within an R session. Avoid inadvertent deletion.

- Widely accessible from any scope e.g functions, lapply, loops, local environment etc

- Restricted mutability of variable using set function e.g varname.set()

- Variable with easy function calls by attaching '.'

- Variable with un-mutable class when changing its value

- Variable with restricted number of times it can be changed

Examples

# Task: create a super variable to
# store dataset that should not be altered
newSuperVar(mtdf, value = austres) # create a super variable
head(mtdf) # view it
mtdf.class # view the store class of the variable, it cannot be changed
# it means that when the super variable is edited, the new value MUST have the same class "ts"

# create and lock super variable by default
# extra security to prevent changing
newSuperVar(mtdf3, value = beaver1, lock = TRUE)
head(mtdf3) # view
mtdf3.round(1) # round to 1 decimal places
head(mtdf3) # view
mtdf3.signif(2) # round to 2 significant digits
head(mtdf3) # view

# Task: create a new super variable to store numbers
# edit the numbers from various scopes
newSuperVar(edtvec, value = number(5))
edtvec # view content of the vector

# edtvec.set(letters) #ERROR: Cannot set to value with different class than initial value

edtvec.set(number(20)) # set to new numbers
edtvec # view output

for (pu in 1:8) {
  print(edtvec) # view output within loop
  edtvec.set(number(pu)) # set to new numbers within for loop
}

lc <- lapply(1:8, function(pu) {
  print(edtvec) # view output within loop
  edtvec.set(number(pu)) # set to new numbers within lapply loop
})

# see that the above changed the super variable easily.
# local variable will not be altered by the loop
# example
bim <- 198
lc <- lapply(1:8, function(j) {
  print(bim)
  bim <- j # will not alter the value of bim in next round
})


# Task: create and search data.frame
# create a new super variable with value as mtcars
# search if it contains the numeric value 21
newSuperVar(lon2, value = mtcars) # declares lon2
lon2 # view content of lon2
lon2.contains("21.0") # WRONG - since df.col is not specified,
# only the first column is search for the character "21.0"
lon2.contains("21.0", df.col = "mpg") # WRONG - searches mpg column
# for the character "21.0"
lon2.contains(21.0, df.col = "mpg") # CORRECT - search mpg column for the
# numeric value 21.0

# remove lon2 as a super variable
exists("lon2") # before removal
lon2.rm()
exists("lon2") # after removal

# Task: create and search vector
# create a new super variable with value as 10 random numbers
# search if it contains the numeric value 72
newSuperVar(lon3, value = number(10, seed = 12)) # declares lon3
lon3 # view content of lon3
lon3.contains(72) # should give TRUE or false if the vector contains the value 45
lon3.contains(72, fixed = TRUE) # should give TRUE or false if the vector contains the value 45

# remove lon3 as a super variable
lon3.rm()


#Task: create a super variable that can only be edited 3 times
newSuperVar(man1, value = number(5), editn = 3)
man1 # view value

man1.set(number(10)) # change value first time
man1 # view value

man1.set(number(2)) # change value second time
man1 # view value

man1.set(number(1)) # change value third time
man1 # view value

man1.set(number(5)) # change value forth time,
# should not change because max change times exceeded
man1 # view value

Normalize a Numeric Vector to the Range [0, 1]

Description

This function normalizes a numeric vector so that all values are scaled to the range [0, 1].

Usage

normalize.vector(x)

Arguments

Value

A numeric vector with values scaled to the range [0, 1]. If all values are identical, all values are set to 0.

Examples

# Example with a numeric vector
normalize.vector(c(1, 2, 3, 4, 5))

# Example with a vector containing identical values
normalize.vector(c(3, 3, 3))

Not a data

Description

Opposite of is.data.frame(). Check if entry is not a data object

Usage

not.data(x)

Arguments

Value

a boolean value to indicate if entry is a data table

Examples

test.dt <- data.frame(ID=1:200,Type="RPKG.net")
test.notenv <- list(t=1)

is.data.frame(test.dt) # TRUE
not.data(test.dt) # FALSE

not.data(test.notenv) # TRUE
if(not.data(test.dt)) message("yes") # NULL

Not duplicated elements

Description

Opoosite of duplicated(). Checks which elements of a vector or data frame are NOT duplicates of elements with smaller subscripts

Usage

not.duplicated(x, incomparables = FALSE, ...)

Arguments

Value

elements of a vector or data frame that are NOT duplicates

Examples

set.seed(08082023)
dtf <- sample(1:10,15, replace = TRUE)
dtf # 3  9 10  3  8  9  6 10  5  1  2  2  2  9  8
dtf[ dtf > 4 & duplicated(dtf) ] # 9 10  9  8
dtf[ dtf > 4 & not.duplicated(dtf) ] # 9 10  8  6  5

Not empty

Description

Check if entry is not empty

Usage

not.empty(x)

is.empty(x)

Arguments

Value

a boolean value to indicate if entry is empty

Examples

not.empty("empty") # TRUE
not.empty('') # FALSE
not.empty(y<-NULL) # FALSE
if(not.empty('')) message("yes") # NULL

Not an environment

Description

Check if entry is not an environment object

Usage

not.environment(x)

Arguments

Value

a boolean value to indicate if entry is an environment

Examples

test.env <- new.env()
test.notenv <- list(t=1)
not.environment(test.env) # FALSE
not.environment(test.notenv) # TRUE
if(not.environment(test.notenv)) message("yes") # yes

Not exists

Description

Check if object does not exists

Usage

not.exists(x)

Arguments

Value

a boolean value to indicate if entry does not exists

Examples

go = 7
not.exists("exis") # TRUE
not.exists("go") # FALSE
if(not.exists('hallo')) message("yes") # NULL

File name extension(s) is Not an image

Description

Check if one or multiple file name entry is not an image

Usage

not.image(x)

Arguments

Details

This current function tests if the extension of the file name provided does NOT belongs to any of the image extensions listed below
AI - Adobe Illustrator
BMP - Bitmap Image
CDR - Corel Draw Picture
CGM - Computer Graphics Metafile
CR2 - Canon Raw Version 2
CRW - Canon Raw
CUR - Cursor Image
DNG - Digital Negative
EPS - Encapsulated PostScript
FPX - FlashPix
GIF - Graphics Interchange Format
HEIC - High-Efficiency Image File Format
HEIF - High-Efficiency Image File Format
ICO - Icon Image
IMG - GEM Raster Graphics
JFIF - JPEG File Interchange Format
JPEG - Joint Photographic Experts Group
JPG - Joint Photographic Experts Group
MAC - MacPaint Image
NEF - Nikon Electronic Format
ORF - Olympus Raw Format
PCD - Photo CD
PCX - Paintbrush Bitmap Image
PNG - Portable Network Graphics
PSD - Adobe Photoshop Document
SR2 - Sony Raw Version 2
SVG - Scalable Vector Graphics
TIF - Tagged Image File
TIFF - Tagged Image File Format
WebP - Web Picture Format
WMF - Windows Metafile
WPG - WordPerfect Graphics

Value

a boolean value to indicate if entry is not an image

Examples

img.1 <- "fjk.jpg"
not.image(img.1)

img.2 <- "fjk.bmp"
not.image(img.2)

img.3 <- "fjk.SVG"
not.image(img.3)

# a vector of file names
v <- c("logo.png", "business process.pdf",
"front_cover.jpg", "intro.docx",
"financial_future.doc", "2022 buybacks.xlsx")
not.image(v)

# when the file name has no extension
# the function returns NA
v2 <- c("img2.jpg",NA,"northbound.xlsx","landimg")
not.image(v2)

Not inherit from any of the classes specified

Description

Opposite of base::inherits(). Indicates whether its first argument inherits from any of the classes specified in the what argument. If which is TRUE then an integer vector of the same length as what is returned. Each element indicates the position in the class(x) matched by the element of what; zero indicates no match. If which is FALSE then TRUE is returned by inherits if any of the names in what match with any class.

Usage

not.inherits(x, what, which = FALSE)

Arguments

Value

a boolean value to indicate if !inherits

Examples

keep.cols = "a character"
class(keep.cols) # class is character
not.inherits(keep.cols,"character")

num.var = 1L
class(num.var) # class is integer
not.inherits(num.var,"double")

Not an integer

Description

Opposite of is.integer(). Check if entry is not an integer

Usage

not.integer(x)

Arguments

Value

a boolean value to indicate if entry is an integer

Examples

is.integer(78L) #TRUE
not.integer(78L) #FALSE

not.integer(23.43) # TRUE
not.integer(45L) # FALSE
if(not.integer(4L)) message("yes") # NULL

Not logical

Description

Opposite of is.logical(). Check if entry is a logical object

Usage

not.logical(x)

Arguments

Value

a boolean value to indicate if entry is logical

Examples

test.env <- TRUE
test.notenv <- 0
not.logical(test.env) # FALSE
not.logical(test.notenv) # TRUE
if(not.logical(test.notenv)) message("yes") # yes

Not NA

Description

Opposite of is.na(). Check if entry is not NA

Usage

not.na(x)

Arguments

Value

a boolean value to indicate if entry is NA

Examples

not.na(NA) # FALSE
not.na(NULL) # logical(0)
if(not.na(45)) message("something") # TRUE

Not NULL

Description

Opposite of is.null(). Check if entry is not NULL

Usage

not.null(x)

Arguments

Value

a boolean value to indicate if entry is NULL

Examples

is.null("") # FALSE
not.null("") # TRUE
not.null(NULL) # FALSE
if(not.null(45)) message("something") # yes

Not numeric

Description

Check if entry is not numeric

Usage

not.numeric(x)

Arguments

Value

a boolean value to indicate if entry is numeric

Examples

not.numeric("45") # TRUE
not.numeric(45) # FALSE
if(not.numeric(45)) message("yes") # yes

Not a vector

Description

Opposite of is.vector(). Check if entry is not vector

Usage

not.vector(x)

Arguments

Value

a boolean value to indicate if entry is vector

Examples

vect1 = list(r=1,t=3:10)
vect2 = LETTERS
is.vector(vect1) # TRUE
not.vector(vect1) # FALSE
not.vector(vect2) # FALSE
if(not.vector(vect1)) message("yes") # NULL

Generate a random number (integer)

Description

Fetch n random integers between 1 and 1,000,000,000

Usage

number(n, max.digits = 10, seed = NULL)

Arguments

Value

random numbers between 1 and 1 billion

Examples

number(1)
number(10)
paste0(number(2),LETTERS)

#set maximum number of digits
number(1,max.digits = 5)
number(10,max.digits = 4)

#set seed for reproducibility
#without seed
number(6) #result 1
number(6) #result 2, different from result 1
#with seed
number(6,seed=1)#result 3
number(6,seed=1)#result 4, same as result 3

Nullish coalescing operator

Description

Alternative return for empty, null or na statements.Return alternative if the value of expression is empty or NA or NULL

Usage

or(test, alternative)

test %or% alternative

Arguments

Value

value of test if not null or empty, else return value of alternative

Note

Equivalent to Nullish coalescing operator ?? in javascript or PHP like $Var = $operand1 ?? $operand2;

Examples

test1 <- c(4,NA,5,2,0,21)

test2 <- data.frame(ID = 1:10,ED = LETTERS[10:1])

# One may also choose to use

test2[,1] %or% "A"

test2[which(test2$ID == 323),2] %or% "CCCCC"

number(1) %or% "Placeholder"

number(10) %or% "Placeholder"

NA %or% "Random"

NULL %or% "Random"

"" %or% "Random"

or(test1[which(test1==4)],100)


or(test1[which(test1==43)],100)

or(test2[which(test2$ID == 10),2],"BBBBB")

or(test2[which(test2$ID == 323),2],"CCCCC")

Calculate the distance of points from the center of a cluster

Description

This function operates on multivariate data and calculates the distance of points from the centroid of one or more clusters.

Usage

pairDist(data, round)

Arguments

Value

a named vector consisting of a row number and a pair-distance value

Function utility

Used to generate the computations needed to model pair-distance measures in three dimensions

More information about this function

The pairDist function is used to quantify how far each data point (row) is from the overall mean across all columns. It’s commonly used in multivariate statistics, machine learning, and data analysis to assess the variability or similarity of data points relative to their mean. More specifically, the function is used in outlier detection and cluster analysis to evaluate the dispersion of data. Used in conjunction with other calculations, pairDist output can also be used to model data in three dimensions.

References

the current function was adapted from one of the examples in the svgViewR package,
https://cran.r-project.org/web/packages/svgViewR/svgViewR.pdf

Examples

data = attenu[,1:2]

# example 1: basic example using data.frame
pairDist(data)

# example 2: basic example using as.matrix
pairDist(as.matrix(data))

# example 3: round results to 2 decimal points
pairDist(data, 2)

# example 4
data = matrix(
c(1, 5, NA, 4, 5, 6, NA, 8, 9, NA, 12, 23, 6, 0, 3, 3, 8, 15),
ncol = 3,
byrow = TRUE)
x = pairDist(data, 3)
data = data.frame(data)
data = cbind(data, x)

Function to calculate the percentage of matching between two strings

Description

Function to calculate the percentage of matching between two strings

Usage

percent_match(
  string1,
  string2,
  case_sensitive = FALSE,
  ignore_whitespace = TRUE,
  frag_size = 2
)

string1 %match% string2

sound_match(string1, string2)

Arguments

Details

Case Sensitivity:

The function can optionally consider or ignore case sensitivity based on the case_sensitive argument.

Whitespace Handling:

With ignore_whitespace set to TRUE, the function removes all whitespaces before comparison. This can be useful for matching strings that may have inconsistent spacing.

Exact Character-by-Character Matching:

The function computes the percentage of matching characters in the same positions.

Substring Matching:

The function checks if one string is a substring of the other, awarding a full match if true.

Levenshtein Distance:

The function uses Levenshtein distance to calculate the similarity and integrates this into the overall match percentage.

Fragment Matching:

- A frag_size argument is introduced that compares fragments (substrings) of a given size (default is 3) from both strings.
- The function creates unique fragments from each string and compares them to find common fragments.
- The percentage match is calculated based on the ratio of common fragments to the total number of unique fragments.

Combining Metrics:

The overall match percentage is computed as the average of exact match, substring match, Levenshtein match, and fragment match percentages.

Value

numeric value of the match percent

match word sounds

Examples

# Example 1: simple match
string1 <- "Hello World"
string2 <- "helo world"

match_percent <- percent_match(string1, string2)
message("Percentage of matching: ", match_percent)


# Example 2: which date is closest
string0 <- "october 12,1898"
string1 <- "2018-10-12"
string2 <- "1898-10-12"
percent_match(string0, string1)
percent_match(string0, string2)
percent_match(string0, string2, frag_size = 4)
percent_match(string1, string2)

sound_match("Robert","rupert")
sound_match("rupert","Rubin")
sound_match("book","oops")

Increment vector by value

Description

Increment the content of a vector and re-save as the vector

Usage

plus(., add = 1L)

Arguments

Details

This function is very useful when writing complex codes involving loops. Apart from the for loop, this can be useful to quickly increment a variable located outside the loop by simply incrementing the variable by 1 or other numbers. Check in the example section for a specific use. Nonetheless, one may also choose to use this function in any other instance, as it's simple purpose is to increase the value of a variable by a number and then re-save the new value to that variable.

Value

a vector incremented by a number

Examples

num1 <- sample(330:400,10)
num1#before increment

# increment num1 by 1
inc(num1)
num1 #after increment

# increment num1 by 5
num1 #before increment
inc(num1, add= 10)
num1 #after increment

#when used in loops

#add and compare directly
rnum = 10
inc(rnum) == 11 #returns TRUE
rnum #the variable was also updated

# use in a for loop
ynum = 1
for( i in c("scientist","dancer","handyman","pharmacist")){
message("This is the item number ")
message(ynum)
message(". For this item, I am a ")
message(i)

#decrement easily at each turn
plus(ynum)
}


#use in a repeat loop
xnum = 1
repeat{ #repeat until xnum is 15
message(xnum)
if(inc(xnum) == 15) break
}

Check whether an R package has been decommissioned in CRAN

Description

Designed to assist users in checking the decommission status of an R package on CRAN. In the context of R language, CRAN stands for the Comprehensive R Archive Network.

Usage

rDecomPkg(package)

Arguments

Details

CRAN is a network of servers around the world that store R packages and their documentation, providing a centralized repository for the R community. With the current function, users can quickly and easily determine whether a specific R package has been decommissioned on CRAN, ensuring they stay informed about the availability and support status of the packages they rely on for their R programming projects. This tool simplifies the process of package management, helping users maintain up-to-date and reliable dependencies in their R code.

Value

the decommissioned status of a particular package based on the available packages using the utils package

Examples

## Not run: 
# check if cattonum package is decommissioned
# the current package is expected to be decommissioned
rDecomPkg("cattonum")

# check if dplyr is decommissioned
# the current package is expected NOT to be decommissioned
rDecomPkg("dplyr")

# when a package never existed in CRAN
# the result of the function call should be NA
rDecomPkg("printy")
rDecomPkg("package0002312122312")

## End(Not run)

Generate a random string

Description

Create a random string of specified length

Usage

randString(n, length)

Arguments

Value

one more random string of specific length

Examples

# Task 1: create 1 random string string of length 5
randString(n = 1, length = 5)

# Task 2: create 5 random string string of length 10
randString(n = 5, length = 10)


# Task 3: create 4 random string string of length 16
randString(n = 4, length = 16)

R Color Constant

Description

This function provides information that describes the color constants that exist in R

Usage

rcolorconst(title = "R Color Constants")

Arguments

Details

In addition to the color palette in R that can be represented as either color literals or hexadecimal values, numeric values can also be used to add colorization to a plot. Numeric values ranging from 1 to 8 provide 8 basic colors that can be deployed. The rcolorconst function returns both a Named Vector and a color palette plot that connects these numeric values with their corresponding color.

Value

returns color constant

Examples

# Without title
ex1 <- rcolorconst()

# With title
ex2 <- rcolorconst("My new color constant")

# More detailed example
set.seed(200)
x = data.frame(
  meas = rnorm(100),
  grp = sample(1:8, size = 100,
  replace = TRUE))
plot(x, pch = 16, col = x$grp)
colnums = rcolorconst()

Read a CSV and preview first X rows and columns

Description

The purpose of this function is combine the functionality of read.csv and print, which are often used together.

The purpose of this function is to read data from a file into a variable and simultaneously display a preview of the data, showing either the first few rows or columns based on the user's specification. It is important to emphasize that the function expects the user to assign the result of the read operation to a variable in order to achieve its intended purpose. eg. Use var1 = read.csv.print(file1) instead of read.csv.print(file1)

Usage

read.csv.print(
  file,
  header = TRUE,
  sep = ",",
  quote = "\"",
  dec = ".",
  fill = TRUE,
  comment.char = "",
  ...,
  dim = c(10L, 5L)
)

Arguments

Details

Read a dataset of type csv and show x rows and y columns with one function call

Value

read csv content and a print out of the data head

Examples

## Not run: 
# Example: read a csv file and print the first 10 lines
# declare file
new.file <- "test.csv"

# read file and preview default
dth3 <- read.csv.print(file = new.file)

# read file and preview 10 rows and all columns
dth1 <- read.csv.print(file = new.file, dim = 10)

# read file and preview 10 rows and 5 columns
dth2 <- read.csv.print(file = new.file, dim = c(10,5))

## End(Not run)

Read in a table and show first X rows and columns

Description

The purpose of this function is combine the functionality of read.table and print, which are often used together.

The purpose of this function is to read table from a file into a variable and simultaneously display a preview of the data, showing either the first few rows or columns based on the user's specification. It is important to emphasize that the function expects the user to assign the result of the read operation to a variable in order to achieve its intended purpose. eg. Use var1 = read.table.print(file1) instead of read.table.print(file1)

Usage

read.table.print(
  file,
  header = FALSE,
  sep = "",
  quote = "\"'",
  dec = ".",
  numerals = c("allow.loss", "warn.loss", "no.loss"),
  row.names,
  col.names,
  as.is = TRUE,
  na.strings = "NA",
  colClasses = NA,
  nrows = -1,
  skip = 0,
  check.names = TRUE,
  fill = NULL,
  strip.white = FALSE,
  blank.lines.skip = TRUE,
  comment.char = "#",
  allowEscapes = FALSE,
  flush = FALSE,
  stringsAsFactors = FALSE,
  fileEncoding = "",
  encoding = "unknown",
  skipNul = FALSE,
  dim = c(10L, 5L),
  ...
)

Arguments

Details

Read a dataset of type table and show x rows and y columns

Value

read table content and a print out of the data head

Examples

## Not run: 
# Example: read a table file and print the first 10 lines
# declare file
new.file <- "test.csv"

# read file and preview default
dth3 <- read.table.print(file = new.file, sep=",",quote = "\"",dec = ".",
fill = TRUE, comment.char = "", header = TRUE)

# read file and preview 10 rows and all columns
dth1 <- read.table.print(file = new.file, sep=",",quote = "\"",dec = ".",
fill = TRUE, comment.char = "", header = TRUE, dim = 10)

# read file and preview 10 rows and 5 columns
dth2 <- read.table.print(file = new.file, sep=",",quote = "\"",dec = ".",
fill = TRUE, comment.char = "", header = TRUE, dim = c(10,5))

## End(Not run)

Clear environment, clear console, set work directory and load files

Description

Shorthand to quickly clear console, clear environment, set working directory, load files

Usage

refresh(setwd = NULL, source = c(), load = c(), clearPkgs = FALSE)

Arguments

Details

The purpose of this function is provide a one-line code to clear the console, clear the environment, set working directory to a specified path, source in various files into the current file, and load RData files into the current environment. The first process in the sequence of events is to clear the environment. Then the working directory is set, prior to inclusion of various files and RData. With the directory being set first, the path to the sourced in or RData files will not need to be appended to the file name. See examples.

Value

cleared environment and set directory

Examples

if(interactive()){
#exactly like the clean function
#simply clear environment, clear console and devices
quickcode::refresh()

#clear combined with additional arguments
quickcode::refresh(
  clearPkgs = FALSE
) #also clear all previously loaded packages if set to TRUE

quickcode::refresh(
  setwd = "/home/"
) #clear env and also set working directory


quickcode::refresh(
  source = c("/home/file1.R","file2")
) #clear environment and source two files into current document


quickcode::refresh(
  setwd = "/home/",
  source = c("file1","file2")
) #clear environment, set working directory and source 2 files into environment


quickcode::refresh(
  setwd = "/home/",
  source="file1.R",
  load="obi.RData"
) #clear environment, set working directory, source files and load RData

}

Replicate Rows in a Data Frame

Description

This function replicates each row in a data frame a specified number of times.

Usage

rows.rep(data, n)

Arguments

Value

A data frame with each row replicated 'n' times. If 'n' is less than or equal to 0, an empty data frame is returned.

Examples

# Example with a simple data frame
df <- data.frame(A = c(1, 2), B = c("x", "y"))
rows.rep(df, 3)

# Example with no replication (n = 0)
rows.rep(df, 0)

Re-sample a dataset by column and return number of entry needed

Description

Shorthand to return a re-sample number of rows in a data frame by unique column

Usage

sample_by_column(.dt, col, n, seed = NULL, replace = FALSE)

Arguments

Value

data frame containing re-sampled rows from an original data frame

Examples

data1 <- data.frame(ID=1:10,MOT=11:20)
sample_by_column(data1,MOT,3)
sample_by_column(data1,ID,7)

Set a variable only once

Description

Facilitates the one-time setting of a variable in R, ensuring its immutability thereafter.

Usage

setOnce(., val = 1L, envir = NULL)

Arguments

Details

With this function, users can establish the change to the initial value of a variable, and it guarantees that any subsequent attempts to modify the variable are ignored. This feature ensures that the variable remains constant and immutable once it has been set, preventing unintentional changes and promoting code stability. This function simplifies the process of managing immutable variables in R, providing a reliable mechanism for enforcing consistency in data throughout the course of a program or script.

Value

the variable set to the new variable, along with a class of once added to the output

Examples

# set the value of vector_x1, vector_y1, vector_z1
init(vector_x1, vector_y1, vector_z1, value = 85)

# view the initial values of the variables
vector_x1
vector_y1
vector_z1

# task 1: change the value vector_x1 and prevent further changes
vector_x1 # check value of unchanged
vector_x1 * 0.56 # check value when x 0.56

setOnce(vector_x1, val = 4500) # set vector_x1
vector_x1 # check value
vector_x1 * 0.56 # check value when x 0.56

setOnce(vector_x1, val = 13) # set vector_x1 AGAIN, should not change
vector_x1 # check value
vector_x1 * 0.56 # check value when x 0.56

# task 2: In for loop, change vector_y1 and use later
vector_y1 # check value of unchanged

for(i in 1:20){
setOnce(vector_y1,as.numeric(Sys.time()))
# now let's see the difference between vector_y1
# and the current time as it changes
message("current vector_y1: ",vector_y1,"; subtraction res: ",as.numeric(Sys.time()) - vector_y1)
}

# task 3: In for lapply, change vector_z1 and use later
vector_z1 # check value of unchanged

invisible(
lapply(1:20, function(i){
setOnce(vector_z1,as.numeric(Sys.time()))
# now let's see the difference between vector_z1
# and the current time as it changes
message("current vector_z1: ",vector_z1,"; subtraction res: ",as.numeric(Sys.time()) - vector_z1)
})
)

# result of all the tasks
vector_x1
vector_y1
vector_z1

Sort vector by length or file types of its content

Description

Sort the length or file types of the content of a vector

Takes a vector of file names and sorts them by their file extensions (file type)

Usage

sort_length(vec, asc = TRUE)

sort_file_type(files, asc = TRUE)

Arguments

Value

vector of items sorted by length

A character vector of sorted file names

Note

This function removes all NAs prior to sorting the vector

Examples

# sort by length of content
x = c("acs","tt","jdssr","h","grab")
sort_length(vec = x) # ascending order of length
sort_length(vec = x, asc = FALSE) # descending order of length


files <- c("doc1.pdf",
  "image.jpg", "house.csv", "notes.txt",
  "patab","doc2.pdf", "data.csv", "pic.png","cotab")
sort_file_type(files)
sort_file_type(files, asc = FALSE)

Split a string of values and return as boolean vector

Description

The purpose of this function is combine the functionality of strsplit, unlist and as.logical, which are often used together.

Usage

strsplit.bool(
  x,
  split,
  fixed = FALSE,
  perl = FALSE,
  useBytes = FALSE,
  type = 2
)

Arguments

Details

Given a sting, split by a separator into boolean

Value

boolean values based on split string

Examples

# string of numbers
num.01 = "0 1 0 0 1 0 1 T F TRUE FALSE t f"

# split a string of numbers and return as boolean 1/0
strsplit.bool(num.01, split = " ", type = 3)

# split a string of numbers and return as boolean TRUE/FALSE
strsplit.bool(num.01, split = " ", type = 2)

# split a string of numbers and return as boolean Yes/No
strsplit.bool(num.01, split = " ", type = 1)


# string of numbers
num.02 = "0abc1abc0abc0abc1abc0abc1abcTabcFabcTRUEabcFALSEabcf"

# split a string of numbers and return as boolean 1/0
strsplit.bool(num.02, split = "abc", type = 3)

# split a string of numbers and return as boolean TRUE/FALSE
strsplit.bool(num.02, split = "abc", type = 2)

# split a string of numbers and return as boolean Yes/No
strsplit.bool(num.02, split = "abc", type = 1)

Split a string of numbers and return as numeric vector

Description

The purpose of this function is combine the functionality of strsplit, unlist and as.numeric, which are often used together.

Usage

strsplit.num(x, split, fixed = FALSE, perl = FALSE, useBytes = FALSE)

Arguments

Details

Given a sting, split by a separator into numbers

Value

numeric values based on split string

Examples

# Example 1
# string of numbers with separator " "
num.01 = "5 3 2 3 5 2 33 23 5 32 432 42 23 554"

# split a string of numbers and return as numeric
strsplit.num(num.01, split = " ")


# Example 2
# string of numbers with separator "|||"
num.02 = "0|||1|||4|||43|||6|||8|||00||| 1||| 0 1||| T |||F|||TRUE |||f"

# split a string of numbers and return as numeric
strsplit.num(num.02, split = "[|||]")

Calculate the Range Difference of a Numeric Vector

Description

This function calculates the difference between the maximum and minimum values of a numeric vector.

Usage

sub.range(x)

Arguments

Value

A numeric value representing the difference between the maximum and minimum values of the input vector. Returns 'NA' if the input is empty or contains only 'NA' values.

Examples

# Example with a numeric vector
numeric_vector <- c(1, 5, 3, 8, 2)
sub.range(numeric_vector)

# Example with missing values
sub.range(c(NA, 4, 7, NA, 10))

Get all the environment objects and their sizes

Description

Retrieve the size contribution of all the available objects in the environment

Usage

summarize.envobj(envir = parent.frame())

Arguments

Value

a dataframe of all the variables within the environment

Examples

# Get a data frame of all environment objects and their size
summarize.envobj()

Switch the index of two columns in a data set

Description

Allows the user to choose precisely which two columns they want to swap places, while optionally preventing some rows within the columns from being altered in the process. Excluded rows within the columns act as anchors that are immune from the switching operation on the selected columns.

Usage

switch_cols(data, col1, col2, keep.rows = NULL)

Arguments

Examples

# Example using mtcars
data101 <- mtcars[1:7,]

head(data101) # preview overall data

# task 1: basic result of switching columns 5 and 6
head(switch_cols(data101, 5, 6))

# task 1: basic result of switching columns number 5 and name "gear"
head(switch_cols(data101, 5, "gear"))

# task 1: basic result of switching columns "qsec" and "carb"
head(switch_cols(data101, "qsec", "carb"))


# task 2: switch columns, but retain some rows with the switched columns


# lets exchange some columns, but keep content of row 4, 5 intact
data101[1:6,4:7] # preview the portion that is to be changed
res1 <- switch_cols(data101, col1 = 5, col2 = 6, keep.rows = 4:5) # use column numbers
res1[1:6,4:7] # check result, pay attention to rows 4, 5 of columns 5, 6 as well

data101[1:6,6:11] # preview the portion that is to be changed
res2 <- switch_cols(data101,
col1 = "qsec",
col2 = "carb",
keep.rows = c(1,2,3)) # keep 1, 2, 3
res2[1:6,6:11] # check result

Switch the index of two rows in a data set

Description

Allows the user to choose precisely which two rows they want to swap places, while optionally preventing some columns from being altered in the process. Excluded columns within the rows act as anchors that are immune from the switching operation on the selected rows.

Usage

switch_rows(data, row1, row2, keep.cols = NULL)

Arguments

Examples

# Example using mtcars
data100 <- mtcars[1:7,]

head(data100) # preview overall data

# task 1: basic result of switching rows 5 and 6
head(switch_rows(data100, 5, 6))

# task 2: switch rows, but retain some columns
data100[5:6,2:10] # preview the portion that is to be changed

# lets switch 2 rows, but keep content of columns 7, 8, 9 10 within the changed rows
res1 <- switch_rows(data100, row1 = 5, row2 = 6, keep.cols = 7:10) # use column numbers
res1[5:6,] # check result, pay attention to columns 9 and 10 as well
res2 <- switch_rows(data100,
row1 = 5,
row2 = 6,
keep.cols = c("disp","cyl")) # use column names
res2[5:6,] # check result, pay attention to columns "disp","cyl" as well

Track Function Usage and Performance

Description

Up count the number of times a particular function is called. This function tracker is a higher-order function or decorator that wraps around other functions to monitor and record their execution patterns, including metrics like call frequency, execution time, argument patterns, and return values. It acts as a transparent layer that doesn't modify the original function's behavior but collects valuable metadata about its usage.

Usage

track_func(output.dest = "output_tracking.csv")

Arguments

Value

the numeric count of a function usage

Note

The usefulness of function tracking spans several critical areas:

Performance Optimization: By measuring execution times and frequency, developers can identify bottlenecks and frequently called functions that need optimization
Debugging: Tracking argument patterns and function call sequences helps pinpoint issues in complex applications
Usage Analytics: Understanding which features (functions) are most commonly used helps guide development priorities and API design decisions
Resource Management: Monitoring function behavior helps identify memory leaks, resource consumption patterns, and potential optimization opportunities
Testing: Usage patterns can inform test case design and coverage requirements, ensuring critical paths are well-tested
Documentation: Automatically gathering real-world usage examples helps maintain accurate and relevant documentation
Compliance: In regulated environments, function tracking can help maintain audit trails of system behavior

Examples

## Not run: 
library(quickcode)
# Track usage of type2 and type1 functions
store.usage.file <- tempfile()
type5 <- function(x) type2(x)
type4 <- function(x) type3(x)
type3 <- function(x) type1(x)
type1 <- function(x) {
  mean(x)
  sd(x)
  track_func(store.usage.file)
}
type2 <- function(x) {
  type1(x)
  track_func(store.usage.file)
}

# add usage counts to store.usage.file
type1(number(10))
type2(number(10))
type3(number(10))
type4(number(10))
type5(number(10))

# check the stored function usage file
print(read.csv(store.usage.file))

## End(Not run)

Remove Empty Lines from a File

Description

This function reads a text file, removes all empty lines, and saves the modified content back to the same file.

Usage

trim.file(file_path, resave = TRUE)

Arguments

Value

NULL This function modifies the file in place and does not return a value.

Examples

if(interactive()){
# Remove empty lines from a file
trim.file("path/to/your/file.txt")
}

Combine unique() and length()

Description

Combine two frequently used function together to return the length of the unique items of a vector

Usage

unique_len(.)

Arguments

Value

length of the unique items in a vector

Examples

frenchnames1 = c("Léa","Obinna","Bastien","Léa","Obinna", "Hugo", "Amélie","Louis")
unique_len(frenchnames1)

Remove last n elements or specified elements from a vector like array_pop in PHP

Description

Shorthand to remove elements from a vector and save as the same name

Usage

vector_pop(., n = 1, el = NULL, ret = FALSE)

Arguments

Value

vector with elements removed

Examples

# basic example: pop off the last 2 values from vector
c(0,3,"A","Apple", TRUE) #before
vector_pop(c(0,3,"A","Apple", TRUE)) #after 1 pop
vector_pop(c(0,3,"A","Apple", TRUE), n=3) #after 3 pop

# using objects
num1 <- sample(330:400,10)
name1 <- "ObinnaObianomObiObianom"

#task: remove 1 element from the end of the vector and set it to the vector name
num1 #num1 vector before pop
vector_pop(num1) #does not return anything
num1 #num1 vector updated after pop

#task: remove 5 elements from the end, but do not set it to the vector name
num1 #num1 vector before pop
vector_pop(num1,5, ret = TRUE) #return modified vector
num1 #num1 vector remains the same after pop


#task: remove 6 elements from a word, set it back to vector name
name1 #name1 before pop
vector_pop(name1,6) #does not return anything
name1 #name updated after pop

#task: remove 3 elements from a word, Do not set it back to vector name
name1 #name1 before pop
vector_pop(name1,3, ret = TRUE) #returns modified name1
name1 #name1 not updated after pop

#task: remove 4 elements from the end of a vector and return both the removed content and remaining
v_f_num <- paste0(number(20),c("TI")) #simulate 20 numbers and add TI suffix
v_f_num #show simulated numbers
vector_pop(v_f_num, n = 4, ret = TRUE) #get the modified vector
vector_pop(v_f_num, n = 4, ret = "removed") #get the content removed

#task: remove specific items from vector
#note that this aspect of the functionality ignores the 'n' argument
v_f_num_2 <- paste0(number(6, seed = 33),c("AB")) #simulate 6 numbers using seed and add AB suffix
v_f_num_2 #show numbers
vector_pop(v_f_num_2, el = c("403211378AB")) #remove 1 specific entries
v_f_num_2 #show results
vector_pop(v_f_num_2, el = c("803690460AB","66592309AB")) #remove 2 specific entries
v_f_num_2 #show results

Add elements to a vector like array_push in PHP

Description

Shorthand to add elements to a vector and save as the same name

Usage

vector_push(., add, unique = FALSE, rm.na = FALSE, rm.empty = FALSE)

Arguments

Details

Note that two vectors are required in order to use this function. Also, note that the final result replaces the content of the first vector. This means that the original content of the 'first vector' will no longer exist after this function executes.

Value

vector combining fist and second vector, but have name set to the first

Use case

This function allows the combination of two vectors in one short line of code. It allows specification of further downstream filtering of the resulting vector such as selecting only unique items, removing NA or empty values. It simplifies a code chunk with many lines of code to concatenate and filter various vectors.

Examples

num1 <- number(10, seed = 45)
num2 <-"rpkg.net"

num1
num2

#Task: add num2 to num1 and re-save as num1
vector_push(num1,num2)
num1 #updated with num2
num2 #not updated


#Task: concatenate two vectors and remove duplicates
vector1 = number(4,seed = 5)
vector2 = number(8,seed = 5)
vector3 = number(12,seed = 5)

vector1 #length is 4
vector2 #length is 8
vector3 #length is 12

# with duplicated
vector_push(vector1,vector2, unique = FALSE)
vector1 #return modified vector
length(vector1) #length is 12 because nothing was removed
#duplicates in vector1 is 886905927 100040083 293768998  54080431

# without duplicated
vector_push(vector2,vector3, unique = TRUE)
vector2 #return modified vector
length(vector2) #length is 12 instead of 20
#Total of 8 duplicated numbers were removed


#Task: concatenate two vector and remove NA values
vector1 = number(5)
vector2 = c(4,NA,5,NA)
vector3 = number(5)

# with NA
vector_push(vector1,vector2, rm.na = FALSE)
vector1 #return modified vector

# without NA
vector_push(vector3,vector2, rm.na = TRUE)
vector3 #return modified vector


#Task: concatenate two vector and remove empty values
vector1 = number(5)
vector2 = c(4,'',5,'',NULL,' ')
vector3 = number(5)

# with empty
vector_push(vector1,vector2, rm.empty = FALSE)
vector1 #return modified vector

# without empty
vector_push(vector3,vector2, rm.empty = TRUE)
vector3 #return modified vector

Shuffle a vector just like shuffle in PHP

Description

Shorthand to shuffle a vector and save

Usage

vector_shuffle(., replace = FALSE, prob = NULL, seed = NULL)

Arguments

Value

shuffled vector of items store to the vector name

Examples

#basic example
vector_shuffle(c(3,45,23,3,2,4,1))


#using objects
v1<-c(3,45,23,3,2,4,1)

#demonstrate vector_shuffle
vector_shuffle(v1)
v1 # show outputs

#demonstrate reproducibility in shuffle with seed
v0<-v1
vector_shuffle(v0)
v0 #first output

v0<-v1
vector_shuffle(v0)
v0 # different output from first output top

v0<-v1
vector_shuffle(v0,seed = 232L)
v0 #second output

v0<-v1
vector_shuffle(v0,seed = 232L)
v0 #the same output as second output top

Convert Yes/No to Binary or Logical

Description

Seamlessly convert a yes or no to either a binary or logical output

Usage

yesNoBool(
  table,
  fldname,
  out = c("change", "append", "vector"),
  type = c("bin", "log")
)

Arguments

Details

type - "bin" for binary, and "log" for logical

Value

converted Yes/No entries into 1/0 or TRUE/FALSE

Examples

# Declare data for example
usedata <- data.frame(ID = 1:32)
usedata #view the dataset

usedata$yess = rep(c("yes","n","no","YES","No","NO","yES","Y"),4) #create a new column
usedata #view the modified dataset

# Set all yess field as standardize boolean
# Task: convert the "yess" column content to 1/0 or TRUE/FALSE
# Notice that you have add the column name with or without quotes
yesNoBool(usedata,yess, type="bin") #set all as binary 1/0
yesNoBool(usedata,"yess", type="log") #set all as logical TRUE/FALSE


# Task: By default, the 'out' argument is set to "change"
# means that the original data field will be
# replaced with the results as above

# In this example, set the out variable to
# append data frame with a new column name containing the result

yesNoBool(usedata,yess,"append")
#or yesNoBool(usedata,"yess","append")

# In this example, return as vector
yesNoBool(usedata,yess,"vector")
#or yesNoBool(usedata,"yess","vector")

# Task: Return result as logical
yesNoBool(usedata,"yess",type = "log")

Calculates Z-Scores of a distribution

Description

Calculates Z-Scores based on data

Usage

zscore(.data, round, na.rm = TRUE)

zscoreGrowthCurve(Xi, Mi, Si, Li = !0)

Arguments

Value

zscore calculated based on data object or parameters

References

CDC growth chart Z score calculation: https://www.cdc.gov/growthcharts/cdc-data-files.htm

Examples

# Capture z-score from the following distribution x
x = c(6, 7, 7, 12, 13, 13, 15, 16, 19, 22)
z_scores = zscore(x, round = 2) # limit to 2 decimal place
z_scores = zscore(x) # no decimal place limit

df = data.frame(val = x, zscore = z_scores)
head(df)
#EXAMPLE for zscore based on CDC growth chart

# Calculate the zscore for a patient weighing 50kg
Li=-0.1600954
Mi=9.476500305
Si=0.11218624
Xi=50
zscoreGrowthCurve(Xi,Mi,Si,Li)