The sub() and gsub() function in R

You can replace the string or the characters in a vector or a data frame using the sub() and gsub() function in R.

Hello folks, we are going to focus on the most useful and beneficial functions in R, i.e. sub() and gsub() functions.

The sub() and gsub() functions in R, will replace the string with a specific string. You can even use regular expressions with the gsub() function. Col right?

Let’s move forward and explore these functions using relevant illustrations.

Syntax of sub() and gsub()

**sub() and gsub(): **The functions which are exclusively useful for string substitution operations in R. You can replace the string in a vector or a data frame and can substitute the specified string.

sub``(pattern, replacement, x)

gsub``(pattern, replacement, x)

Where,

Pattern = The pattern or the string which you want to be replaced.
Replacement = A input string to replace the pattern string.
X = A vector or a data frame to replace the strings.

The sub() function in R

The sub() function in R is used to replace the string in a vector or a data frame with the input or the specified string.

When you are dealing with large data sets, it’s impossible to look at each line to find and replace the target words or strings.

In this case, the sub() function will replace string.

But, the disadvantage of the sub() function is that the function replaces only the first occurrence by leaving all other similarities.

Complicated? Don’t worry. Let’s illustrate this using a simple example.

#r programming #function

What is GEEK

Buddha Community

Vincent Lab

1605017502

The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences

this.name = "Bob";
const person = {

name: “Jon”,
<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);
},

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);
}

}
person.func1(); // Call the Regular function

// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}
person.func2(); // Call the Arrow function

// Output: {name:“Bob”}

The new keyword with an arrow function

const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

#arrow functions #javascript #regular functions #arrow functions vs normal functions #difference between functions and arrow functions

Noah Rowe

1598762400

The sub() and gsub() function in R

You can replace the string or the characters in a vector or a data frame using the sub() and gsub() function in R.

Hello folks, we are going to focus on the most useful and beneficial functions in R, i.e. sub() and gsub() functions.

The sub() and gsub() functions in R, will replace the string with a specific string. You can even use regular expressions with the gsub() function. Col right?

Let’s move forward and explore these functions using relevant illustrations.

Syntax of sub() and gsub()

**sub() and gsub(): **The functions which are exclusively useful for string substitution operations in R. You can replace the string in a vector or a data frame and can substitute the specified string.

sub``(pattern, replacement, x)

gsub``(pattern, replacement, x)

Where,

Pattern = The pattern or the string which you want to be replaced.
Replacement = A input string to replace the pattern string.
X = A vector or a data frame to replace the strings.

The sub() function in R

The sub() function in R is used to replace the string in a vector or a data frame with the input or the specified string.

When you are dealing with large data sets, it’s impossible to look at each line to find and replace the target words or strings.

In this case, the sub() function will replace string.

But, the disadvantage of the sub() function is that the function replaces only the first occurrence by leaving all other similarities.

Complicated? Don’t worry. Let’s illustrate this using a simple example.

#r programming #function

Marcus Flatley

1594399440

Getting Started with R Markdown — Guide and Cheatsheet

In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!

We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!

R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, results, plots, and writing in one place. R Markdown is particularly useful when you are producing a document for an audience that is interested in the results from your analysis, but not your code.

R Markdown is powerful because it can be used for data analysis and data science, collaborating with others, and communicating results to decision makers. With R Markdown, you have the option to export your work to numerous formats including PDF, Microsoft Word, a slideshow, or an HTML document for use in a website.

r markdown tips, tricks, and shortcuts

Turn your data analysis into pretty documents with R Markdown.

We’ll use the RStudio integrated development environment (IDE) to produce our R Markdown reference guide. If you’d like to learn more about RStudio, check out our list of 23 awesome RStudio tips and tricks!

Here at Dataquest, we love using R Markdown for coding in R and authoring content. In fact, we wrote this blog post in R Markdown! Also, learners on the Dataquest platform use R Markdown for completing their R projects.

We included fully-reproducible code examples in this blog post. When you’ve mastered the content in this post, check out our other blog post on R Markdown tips, tricks, and shortcuts.

Okay, let’s get started with building our very own R Markdown reference document!

R Markdown Guide and Cheatsheet: Quick Navigation

1. Install R Markdown

R Markdown is a free, open source tool that is installed like any other R package. Use the following command to install R Markdown:

install.packages("rmarkdown")

Now that R Markdown is installed, open a new R Markdown file in RStudio by navigating to File > New File > R Markdown…. R Markdown files have the file extension “.Rmd”.

2. Default Output Format

When you open a new R Markdown file in RStudio, a pop-up window appears that prompts you to select output format to use for the document.

New Document

The default output format is HTML. With HTML, you can easily view it in a web browser.

We recommend selecting the default HTML setting for now — it can save you time! Why? Because compiling an HTML document is generally faster than generating a PDF or other format. When you near a finished product, you change the output to the format of your choosing and then make the final touches.

One final thing to note is that the title you give your document in the pop-up above is not the file name! Navigate to File > Save As.. to name, and save, the document.

#data science tutorials #beginner #r #r markdown #r tutorial #r tutorials #rstats #rstudio #tutorial #tutorials

August Larson

1624422360

R vs Python: What Should Beginners Learn?

Let go of any doubts or confusion, make the right choice and then focus and thrive as a data scientist.

I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.

I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.

Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).

Therefore, the short answer on whether you should learn Python or R is: it depends.

The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.

#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r

Tia Gottlieb

1598258520

Activation Functions, Optimization Techniques, and Loss Functions

Activation Functions:

A significant piece of a neural system Activation function is numerical conditions that decide the yield of a neural system. The capacity is joined to every neuron in the system and decides if it ought to be initiated (“fired”) or not, founded on whether every neuron’s info is applicable for the model’s expectation. Initiation works likewise help standardize the yield of every neuron to a range somewhere in the range of 1 and 0 or between — 1 and 1.

Progressively, neural systems use linear and non-linear activation functions, which can enable the system to learn complex information, figure and adapt practically any capacity speaking to an inquiry, and give precise forecasts.

Linear Activation Functions:

**Step-Up: **Activation functions are dynamic units of neural systems. They figure the net yield of a neural node. In this, Heaviside step work is one of the most widely recognized initiation work in neural systems. The capacity produces paired yield. That is the motivation behind why it is additionally called paired advanced capacity.

The capacity produces 1 (or valid) when info passes edge limit though it produces 0 (or bogus) when information doesn’t pass edge. That is the reason, they are extremely valuable for paired order studies. Every rationale capacity can be actualized by neural systems. In this way, step work is usually utilized in crude neural systems without concealed layer or generally referred to name as single-layer perceptions.

#machine-learning #activation-functions #loss-function #optimization-algorithms #towards-data-science #function