Fredy  Larson

Fredy Larson

1610078198

A Complete Beginners Guide to Regular Expressions in R

Learn to Match Any Pattern. It is Easier Than You Think.

The regular expression is nothing but a sequence of characters that matches a pattern in a piece of text or a text file. It is used in text mining in a lot of programming languages. The characters of the regular expression are pretty similar in all the languages. But the functions of extracting, locating, detecting, and replacing can be different in different languages.

In this article, I will use R. But you can learn how to use the regular expression from this article even if you wish to use some other language. It may look too complicated when you do not know it. But as I mentioned at the top it is easier than you think it is. I will try to explain it as much as I can. You are welcome to ask me questions in the comment section if you did not understand any part.

Here we will learn by doing. I will start with very basic ideas and slowly move towards more complicated patterns.

I used RStudio for all the exercises in this article.

#artificial-intelligence #data-science #programming #r #r-programming

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A Complete Beginners Guide to Regular Expressions in R
Marcus  Flatley

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

A Gentle Introduction to Regular Expressions with R

We live in a data-centric age. Data has been described as the new oil. But just like oil, data isn’t always useful in its raw form. One form of data that is particularly hard to use in its raw form is unstructured data.

A lot of data is unstructured data. Unstructured data doesn’t fit nicely into a format for analysis, like an Excel spreadsheet or a data frame. Text data is a common type of unstructured data and this makes it difficult to work with. Enter regular expressions, or regex for short. They may look a little intimidating at first, but once you get started, using them will be a picnic!

More comfortable with python? Try my tutorial for using regex with python instead:

A Gentle Introduction to Regular Expressions with Python

Regular expressions are the data scientist’s most formidable weapon against unstructured text

towardsdatascience.com

The stringr Library

We’ll use the stringr library. The stringr library is built off a C library, so all of its functions are very fast.

To install and load the stringr library in R, use the following commands:

## Install stringer
install.packages("stringr")

## Load stringr
library(stringr)

See how easy that is? To make things even easier, most function names in the stringr package start with str. Let’s take a look at a couple of the functions we have available to us in this module:

  1. str_extract_all(string, pattern): This function returns a list with a vector containing all instances of pattern in string
  2. str_replace_all(string, pattern, replacement): This function returns string with instances of pattern in string replaced with replacement

You may have already used these functions. They have pretty straightforward applications without adding regex. Think back to the times before social distancing and imagine a nice picnic in the park, like the image above. Here’s an example string with what everyone is bringing to the picnic. We can use it to demonstrate the basic usage of the regex functions:

basicString <- "Drew has 3 watermelons, Alex has 4 hamburgers, Karina has 12 tamales, and Anna has 6 soft pretzels"

If I want to pull every instance of one person’s name from this string, I would simply pass the name and basic_string to str_extract_all():

basicExtractAll <- str_extract_all(basicString, "Drew")
print(basicExtractAll)

The result will be a list with all occurrences of the pattern. Using this example, basicExtractAll will have the following list with 1 vector as output:

[[1]]
[1] "Drew"

Now let’s imagine that Alex left his 4 hamburgers unattended at the picnic and they were stolen by Shawn. str_replace_all can replace any instances of Alex with Shawn:

basicReplaceAll <- str_replace_all(basicString, "Alex", "Shawn")
print(basicReplaceAll)

The resulting string will show that Shawn now has 4 hamburgers. What a lucky guy 🍔.

"Drew has 3 watermelons, Shawn has 4 hamburgers, Karina has 12 tamales, and Anna has 6 soft pretzels"

The examples so far are pretty basic. There is a time and place for them, but what if we want to know how many total food items there are at the picnic? Who are all the people with items? What if we need this data in a data frame for further analysis? This is where you will start to see the benefits of regex.

#regex #regular-expressions #r #text-processing #unstructured-data #express

Fredy  Larson

Fredy Larson

1610078198

A Complete Beginners Guide to Regular Expressions in R

Learn to Match Any Pattern. It is Easier Than You Think.

The regular expression is nothing but a sequence of characters that matches a pattern in a piece of text or a text file. It is used in text mining in a lot of programming languages. The characters of the regular expression are pretty similar in all the languages. But the functions of extracting, locating, detecting, and replacing can be different in different languages.

In this article, I will use R. But you can learn how to use the regular expression from this article even if you wish to use some other language. It may look too complicated when you do not know it. But as I mentioned at the top it is easier than you think it is. I will try to explain it as much as I can. You are welcome to ask me questions in the comment section if you did not understand any part.

Here we will learn by doing. I will start with very basic ideas and slowly move towards more complicated patterns.

I used RStudio for all the exercises in this article.

#artificial-intelligence #data-science #programming #r #r-programming

Mad Libs: Using regular expressions

From Tiny Python Projects by Ken Youens-Clark

Everyone loves Mad Libs! And everyone loves Python. This article shows you how to have fun with both and learn some programming skills along the way.


Take 40% off Tiny Python Projects by entering fccclark into the discount code box at checkout at manning.com.


When I was a wee lad, we used to play at Mad Libs for hours and hours. This was before computers, mind you, before televisions or radio or even paper! No, scratch that, we had paper. Anyway, the point is we only had Mad Libs to play, and we loved it! And now you must play!

We’ll write a program called mad.py  which reads a file given as a positional argument and finds all the placeholders noted in angle brackets like <verb>  or <adjective> . For each placeholder, we’ll prompt the user for the part of speech being requested like “Give me a verb” and “Give me an adjective.” (Notice that you’ll need to use the correct article.) Each value from the user replaces the placeholder in the text, and if the user says “drive” for “verb,” then <verb>  in the text replaces with drive . When all the placeholders have been replaced with inputs from the user, print out the new text.

#python #regular-expressions #python-programming #python3 #mad libs: using regular expressions #using regular expressions

August  Larson

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