Data Analysis with R Programming

This course is part of Google Data Analytics Professional Certificate

Instructor: Google Career Certificates

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What you'll learn

Describe the R programming language and its programming environment.
Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors.
Describe the options for generating visualizations in R.
Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content.

Skills you'll gain

Data Analysis

Details to know

Earn a career certificate

Add to your LinkedIn profile

Assessments

37 quizzes, 1 assignment

Taught in English

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Build your Data Analysis expertise

This course is part of the Google Data Analytics Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 5 modules in this course

This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R, and the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course, learners will: - Examine the benefits of using the R programming language. - Discover how to use RStudio to apply R to your analysis. - Explore the fundamental concepts associated with programming in R. - Understand the contents and components of R packages including the Tidyverse package. - Gain an understanding of dataframes and their use in R. - Discover the options for generating visualizations in R. - Learn about R Markdown for documenting R programming.

R is a programming language that can help you in your data analysis process. In this part of the course, you’ll learn about R and RStudio, the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started.

What's included

6 videos8 readings7 quizzes1 plugin

6 videosTotal 35 minutes

Introduction to the exciting world of programming6 minutesPreview module
Fun with R5 minutes
Carrie: Getting started with R3 minutes
Programming languages5 minutes
Introduction to R 6 minutes
Intro to RStudio 7 minutes

8 readingsTotal 85 minutes

Course syllabus10 minutes
The R-versus-Python debate10 minutes
Learning Log: Get ready to explore R20 minutes
Ways to learn about programming10 minutes
From spreadsheets to SQL to R 10 minutes
When to use RStudio10 minutes
Connecting with other analysts in the R community10 minutes
Glossary: Terms and definitions5 minutes

7 quizzesTotal 292 minutes

Module 1 challenge40 minutes
Optional Hands-On Activity: Downloading and installing R60 minutes
Optional Hands-On Activity: R Console60 minutes
Test your knowledge on programming languages6 minutes
Hands-On Activity: Cloud access to RStudio60 minutes
Optional Hands-On Activity: Get started in RStudio Desktop60 minutes
Test your knowledge on programming with RStudio6 minutes

1 pluginTotal 10 minutes

Refresher: Your data analytics certificate roadmap10 minutes

Using R can help you complete your analysis efficiently and effectively. In this part of the course, you’ll explore the fundamental concepts associated with R. You’ll learn about functions and variables for calculations and other programming. In addition, you'll discover R packages, which are collections of R functions, code and sample data that you’ll use in RStudio.

What's included

8 videos8 readings7 quizzes1 plugin

8 videosTotal 47 minutes

Programming using RStudio2 minutesPreview module
Programming fundamentals8 minutes
Operators and calculations5 minutes
The gift that keeps on giving4 minutes
Welcome to the tidyverse6 minutes
More on the tidyverse 5 minutes
Use pipes to nest code9 minutes
Connor: Coding tips5 minutes

8 readingsTotal 75 minutes

Vectors and lists in R10 minutes
Dates and times in R10 minutes
Other common data structures10 minutes
Logical operators and conditional statements10 minutes
Guide: Keeping your code readable 10 minutes
Available R packages 10 minutes
R resources for more help10 minutes
Glossary: Terms and definitions5 minutes

7 quizzesTotal 186 minutes

Module 2 challenge40 minutes
Test your knowledge on programming concepts8 minutes
Hands-On Activity: R sandbox60 minutes
Test your knowledge on coding in R6 minutes
Hands-On Activity: Installing and loading tidyverse60 minutes
Test your knowledge on R packages 6 minutes
Test your knowledge on the tidyverse6 minutes

1 pluginTotal 10 minutes

Basic Concepts of R10 minutes

The R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you’ll examine how R can help you structure, organize, and clean your data using functions and other processes. You’ll learn about data frames and how to work with them in R. You’ll also revisit the issue of data bias and how R can help.

What's included

8 videos8 readings7 quizzes1 assignment1 plugin

8 videosTotal 40 minutes

Data in R 1 minutePreview module
R data frames 4 minutes
Working with data frames4 minutes
Cleaning up with the basics 7 minutes
Organize your data6 minutes
Transforming data5 minutes
Same data, different outcome5 minutes
The bias function5 minutes

8 readingsTotal 75 minutes

More about tibbles10 minutes
Data-import basics10 minutes
File-naming conventions10 minutes
More on R operators10 minutes
Optional: Manually create a data frame 10 minutes
Wide to long with tidyr10 minutes
Working with biased data10 minutes
Glossary: Terms and definitions5 minutes

7 quizzesTotal 250 minutes

Module 3 challenge50 minutes
Hands-On Activity: Importing and working with data60 minutes
Test your knowledge on R data frames8 minutes
Hands-On Activity: Cleaning data in R 60 minutes
Test your knowledge on cleaning data 6 minutes
Hands-On Activity: Changing your data60 minutes
Test your knowledge on R functions6 minutes

1 assignmentTotal 60 minutes

Hands-on Activity: Create your own data frame60 minutes

1 pluginTotal 10 minutes

Clean, organize, and transform data with R10 minutes

R is a tool well-suited for creating detailed visualizations. In this part of the course, you’ll learn how to use R to generate and troubleshoot visualizations. You’ll also explore the features of R and RStudio that will help you with the aesthetics of your visualizations and for annotating and saving them.

What's included

9 videos7 readings9 quizzes1 plugin

9 videosTotal 56 minutes

Visualizations in R2 minutesPreview module
Visualization basics in R and tidyverse6 minutes
Getting started with ggplot()10 minutes
Joseph: Career path to people analytics3 minutes
Enhancing visualizations in R8 minutes
Doing more with ggplot8 minutes
Aesthetics and facets5 minutes
Annotation layer 6 minutes
Saving your visualizations5 minutes

7 readingsTotal 65 minutes

Common problems when visualizing in R10 minutes
Aesthetic attributes10 minutes
Smoothing10 minutes
Filtering and plots 10 minutes
Adding annotations in R10 minutes
Saving images without ggsave()10 minutes
Glossary: Terms and definitions5 minutes

9 quizzesTotal 374 minutes

Module 4 challenge50 minutes
Hands-On Activity: Visualizing data with ggplot260 minutes
Hands-On Activity: Using ggplot60 minutes
Test your knowledge on data visualizations in R8 minutes
Hands-On Activity: Aesthetics and visualizations 60 minutes
Hands-On Activity: Filters and plots60 minutes
Test your knowledge on aesthetics in analysis 8 minutes
Hands-On Activity: Annotating and saving visualizations60 minutes
Test your knowledge on annotating and saving visualizations8 minutes

1 pluginTotal 10 minutes

Elements of ggplot10 minutes

When you’re ready to save and present your analysis, R has different options to consider. In this part of the course, you’ll explore R Markdown, a file format for making dynamic documents with R. You’ll find out how to format and export R Markdown, including how to incorporate R code chunks in your documents.