R Programming – Hacks and Automation

R Programming – Hacks and Automation, available at $59.99, has an average rating of 4.35, with 37 lectures, based on 51 reviews, and has 407 subscribers.

You will learn about Up to date R programming How to loop with the maps family of functions How to do file management operation like copying and pasting files, deleting them, creating new directories and more and combining these operations with loops How to use Git and Github combined with R Studio Create a repository in github and push scripts to it directly from R Studio How to use the rvest package to scrape valuable data from websites Download files with R APIs with R Get data with a REST API using R Post data with a REST API using R Use R to AUTOMATE repetitive task such as scraping dynamic data at a set frequency This course is ideal for individuals who are Beginner or intermediate R users or Those who are curious about the R language and what it can do or Aspiring data scientist and data analysts It is particularly useful for Beginner or intermediate R users or Those who are curious about the R language and what it can do or Aspiring data scientist and data analysts.

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Summary

Title: R Programming – Hacks and Automation

Price: $59.99

Average Rating: 4.35

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Up to date R programming
How to loop with the maps family of functions
How to do file management operation like copying and pasting files, deleting them, creating new directories and more and combining these operations with loops
How to use Git and Github combined with R Studio
Create a repository in github and push scripts to it directly from R Studio
How to use the rvest package to scrape valuable data from websites
Download files with R
APIs with R
Get data with a REST API using R
Post data with a REST API using R
Use R to AUTOMATE repetitive task such as scraping dynamic data at a set frequency

Who Should Attend

Beginner or intermediate R users
Those who are curious about the R language and what it can do
Aspiring data scientist and data analysts

Target Audiences

Beginner or intermediate R users
Those who are curious about the R language and what it can do
Aspiring data scientist and data analysts

You just downloaded datasets online. They came in a zip file. The first thing you do after downloading it, you extract the data with your favorite unzipping software such as WinZip or WinRar. Then you proceed with reading the data in with your analysis software (hopefully R). OK! Where is he going with this, you may wonder.

You are starting a project in R and realize your files are scattered in different paths on your computer. Your immediate reflex is to open the folders involved (say with windows explorer) and proceed to gather those files in one place before starting R. Wait, what’s wrong with that? Hold that thought.

You visit a website frequently. This website is full of data—numbers, downloadable documents, and pictures alike. It may or may not have occurred to you that you can access the data programmatically and visualize it differently. Perhaps you had ideas about it but didn’t know how to get it done. Hold this thought also.

There is nothing wrong with unzipping files with a WinZip or WinRar. Still, it can be beneficial to do unzip files within R. After downloading a dataset or any zip files; you can go directly into R and manage your files there before your analysis. You ever thought about unzipping, copying and pasting, deleting files within R? This course will show you examples of that.

One of the goals of this course is to implant in you the thought of scraping data with ease. I want you to think you can scrape data and visualize it differently and doing so promptly. I will show you the commonly used web scraping techniques in R.

With APIs, you go a step further than scraping. In this course, I teach you how to retrieve data using HTTR and jsonlite packages. Specifically, use the GET function to retrieve data and the POST function to update your account. All this without logging onto your account. I use the peer-to-peer lending platform Lending Club to showcase the use of an API. The API, therefore, allows you to interact with your account programmatically. Combining this with a scheduler can prove highly efficient. A well-thought-out algorithm can be automated and handle repetitive tasks that would otherwise be routine.

This course will also introduce you to the version control system Git. You will learn the power of R Studio combined with Git and GitHub. I teach how to keep different versions of your script with Git and push files, including R scripts, datasets, and other files to the GitHub platform. You will also learn how to revert to previous versions of your code if you make mistakes in later versions. When you master this, you will no longer have to save different versions of your scripts in your directory.

To become an efficient data analyst, you have to be skilled at one or more programming languages. Why not R? This course should also serve as a barometer. If you feel comfortable with the material in this course, you should understand most R scripts you will encounter.

This course will not teach you how to hack into servers. The intent here is not to sway you towards criminal activities.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Resources

Chapter 2: Install R, R Studio, Git, and GitHub

Lecture 1: Install R and R Studio

Lecture 2: About Git and GitHub

Lecture 3: Commit Files

Lecture 4: GitHub

Lecture 5: Exercise 1

Lecture 6: Solution to Exercise 1

Chapter 3: File Handling and Maps Functional Loops

Lecture 1: Loops Lecture

Lecture 2: For Loops to Unzip

Lecture 3: Walk to Unzip

Lecture 4: Exercise 2

Lecture 5: Solution to Exercise 2

Lecture 6: Copy Files

Lecture 7: How I Created the Weather Folder

Lecture 8: Exercise 3

Lecture 9: Solution to Exercise 3

Lecture 10: Map Functions Part 1

Lecture 11: Map Functions Part 2

Chapter 4: Web Scraping Using the rvest Package

Lecture 1: Web Scraping Intro

Lecture 2: First Extraction

Lecture 3: Selector Gadget More Scraping

Lecture 4: Scraping Links and Creating a Dataset

Lecture 5: Intro to Zillow

Lecture 6: Zillow Continued

Lecture 7: Loop Through Pages

Lecture 8: About Exercise 4

Lecture 9: Exercise 4

Lecture 10: Solution to Exercise 4

Lecture 11: Creating a dot R File for the ScheduleR

Lecture 12: The ScheduleR

Lecture 13: Issues with Web Scraping

Chapter 5: APIs with httr and jsonlite Packages

Lecture 1: Intro to Lending Club

Lecture 2: API Documentation

Lecture 3: GET Function

Lecture 4: GET Data Frame or Tibble

Lecture 5: POST Function

Instructors

Robert Jeutong
Statistician

Rating Distribution

1 stars: 0 votes
2 stars: 2 votes
3 stars: 1 votes
4 stars: 12 votes
5 stars: 36 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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