Introduction to R Programming A Modern Approach

Introduction to R Programming – A Modern Approach, available at $79.99, has an average rating of 4.75, with 42 lectures, based on 111 reviews, and has 468 subscribers.

You will learn about Basics of R programming Data wrangling manipulations Make use of the tidyverse packages which includes but not limited to purrr, dplyr, ggplot2, etc.. Create pipelines using the pipe operator to chain instruction and transform a data frame to another Transform data frames then pipe them to ggplot for EDA or professional looking graphs Showcase the importance of a working directory Teach the fundamentals of R – useful beyond this course Understanding functions and how to use existing ones or how to create your own Modern techniques used in R programming by data scientists Install and load packages such as lubridate, readxl, esquisse, etc Read and write different types of data Group and summarize data using the dplyr verbs Transpose data with dplyr pivoting functions or using the soon to be deprecated gather and spread functions This course is ideal for individuals who are Aspiring data scientists, statisticians, or data analyts or Beginner R programming developers curious about data science or Non computer programmers who are willing to learn a fun, useful, and intuitive coding language or Data scientists eager to learn R programming It is particularly useful for Aspiring data scientists, statisticians, or data analyts or Beginner R programming developers curious about data science or Non computer programmers who are willing to learn a fun, useful, and intuitive coding language or Data scientists eager to learn R programming.

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Summary

Title: Introduction to R Programming – A Modern Approach

Price: $79.99

Average Rating: 4.75

Number of Lectures: 42

Number of Published Lectures: 42

Number of Curriculum Items: 42

Number of Published Curriculum Objects: 42

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of R programming

Data wrangling manipulations

Make use of the tidyverse packages which includes but not limited to purrr, dplyr, ggplot2, etc..

Create pipelines using the pipe operator to chain instruction and transform a data frame to another

Transform data frames then pipe them to ggplot for EDA or professional looking graphs

Showcase the importance of a working directory

Teach the fundamentals of R – useful beyond this course

Understanding functions and how to use existing ones or how to create your own

Modern techniques used in R programming by data scientists

Install and load packages such as lubridate, readxl, esquisse, etc

Read and write different types of data

Group and summarize data using the dplyr verbs

Transpose data with dplyr pivoting functions or using the soon to be deprecated gather and spread functions

Who Should Attend

Aspiring data scientists, statisticians, or data analyts

Beginner R programming developers curious about data science

Non computer programmers who are willing to learn a fun, useful, and intuitive coding language

Data scientists eager to learn R programming

Target Audiences

Aspiring data scientists, statisticians, or data analyts

Beginner R programming developers curious about data science

Non computer programmers who are willing to learn a fun, useful, and intuitive coding language

Data scientists eager to learn R programming

Are you nervous or excited about learning how to code? Are you a beginner who wants to get better at learning R the right way? Would you like to learn how to make cool looking and insightful charts? If so, you are in the right place.

Learning how to code in R is an excellent way to start. R is one of the top languages used by data scientists, data analysts, statisticians, etc. The best thing about it is its simplicity.

R was introduced to me in the summer of 2008 as an intern at a marketing firm; since then, I have been a loyal user. Along with SAS, I use it daily to conduct data analysis and reporting. R is one of my top go-to tools. I start with the basics showing you how I learned it, and then I teach it at a pace comfortable for a beginner.

We are living in exciting times, and the future looks bright for those skilled in programming. Industries are using data more and more to make crucial decisions. They need experienced analysts to help design data collection processes and to analyze it. Where do you fit in this picture now and tomorrow? Learning R sets you now and will sustain you for the future.

R was designed mainly for statisticians or those who did not have a computer science background, hence its intuitiveness. R is a free and open-source programming language. It will not cost you anything to have R installed and running on your computer. R is open-source, meaning that contributors can improve its usability by creating packages. Packages contain functions to help users solve specific problems that R’s founders did not think of. It would be a pleasure to see you grow to become a contributor to R someday.

Although R itself is mighty, it is not the best place to write R codes. We will write R codes (or scripts) in R studio. R studio is a powerful editor for R. You will learn all about it in this course.

Here are some of the things you will learn in this course:

1. Download and install R and R studio

2. The different data structures, such as atomic vectors, lists, data frames, and tibbles. How to create and use them

3. How to import an excel or a CSV file into R

4. Create functions

5. How to execute chunks of code following an if-else logic

6. Lean R studio short cut keys to increase your efficiency and productivity

7. How to summarize data

8. How to transpose data from long format to wide format and backward

9. How to create powerful easy to read pipelines using purrr and dplyr packages

10. Introduction to base R plots

11. Ggplots

12. And more

Thanks for taking the time to check out my course. I cannot wait to help you get started with R and R studio. If you have any questions, please message me or check out the free preview lecture to learn more.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Installing R and R Studio Demo

Lecture 1: Installing R and R Studio

Lecture 2: R Studio Setup and Working Directory

Chapter 3: Base R Fundamentals – Base R Data Structure and Slicing

Lecture 1: Variables Lecture

Lecture 2: Variables Demo

Lecture 3: Vectors Lecture

Lecture 4: Vectors Demo

Lecture 5: Matrices and Arrays Lecture

Lecture 6: Matrices and Arrays Demo

Lecture 7: Lists Lecture

Lecture 8: Lists Demo

Lecture 9: Data Frames Lecture

Lecture 10: Data Frames Demo

Lecture 11: Slicing

Lecture 12: Vectors Slicing

Lecture 13: List Slicing

Lecture 14: Data Frames Slicing 1

Lecture 15: Data Frames Slicing 2

Chapter 4: Packages – Modernizing Your R Script with Tidyverse Packages and More

Lecture 1: What are Packages

Lecture 2: Tidyverse Pakages Explained

Lecture 3: Import and Export data

Lecture 4: Dplyr Verbs

Lecture 5: The Pipe Operator

Lecture 6: Summarize Data

Lecture 7: More on the Pipe Operator

Lecture 8: Pivoting

Lecture 9: Relational Data Lecture

Lecture 10: Relational Data Demo

Chapter 5: Base R Must – if(), loops, functions, and Base R plot

Lecture 1: If Else

Lecture 2: Loops – for and while loops

Lecture 3: Intro to Functions

Lecture 4: Create Your Own Function

Lecture 5: Intro to Base R Plots

Chapter 6: Plots with ggplot2 Package

Lecture 1: Intro to ggplot2

Lecture 2: Layers

Lecture 3: Bonus – How did I Transpose the Game Data Set

Lecture 4: Other Types of Charts

Lecture 5: Faceting

Lecture 6: ggplot Options

Lecture 7: The Esquisse Package

Chapter 7: Comprehensive Project

Lecture 1: The Comprehensive Project

Lecture 2: Solution to the Comprehensive Project

Instructors

Robert Jeutong
Statistician

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2 stars: 2 votes

3 stars: 3 votes

4 stars: 20 votes

5 stars: 86 votes

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