Data Analysis with R Programming

  • 4.8
Approx. 37 hours to complete

Course Summary

This course will teach you how to use R programming language to analyze data, manipulate data and create visualizations. You'll learn how to use RStudio and various R packages to work with data effectively.

Key Learning Points

  • Learn how to use R to analyze and manipulate data
  • Understand the basics of data visualization and how to create effective visualizations
  • Get hands-on experience with real-world datasets
  • Learn best practices for cleaning and preprocessing data

Related Topics for further study


Learning Outcomes

  • Learn how to use R and RStudio for data analysis
  • Gain a better understanding of data visualization techniques
  • Develop skills for working with real-world datasets

Prerequisites or good to have knowledge before taking this course

  • Basic understanding of programming concepts
  • Familiarity with statistical analysis

Course Difficulty Level

Intermediate

Course Format

  • Self-paced
  • Online
  • Video Lectures

Similar Courses

  • Data Science Fundamentals
  • Data Visualization with Python
  • Introduction to Data Science

Related Education Paths


Related Books

Description

This course is the seventh course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. 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. This course will also cover 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.

Knowledge

  • 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 R Markdown to create structure and emphasize content

Outline

  • Programming and data analytics
  • Introduction to the exciting world of programming
  • Fun with R
  • Carrie: Getting started with R
  • Programming languages
  • Introduction to R
  • Intro to RStudio
  • Course syllabus
  • The R-versus-Python debate
  • Learning Log: Get ready to explore R
  • Ways to learn about programming
  • From spreadsheets to SQL to R
  • When to use RStudio
  • Connecting with other analysts in the R community
  • Glossary: Terms and definitions
  • Self-Reflection: Ask a question
  • Optional Hands-On Activity: Downloading and installing R
  • Optional Hands-On Activity: R Console
  • Test your knowledge on programming languages
  • Hands-On Activity: Cloud access to RStudio
  • Optional Hands-On Activity: Get started in RStudio Desktop
  • Test your knowledge on programming with RStudio
  • Weekly challenge 1
  • Programming using RStudio
  • Programming using RStudio
  • Programming fundamentals
  • Operators and calculations
  • The gift that keeps on giving
  • Welcome to the tidyverse
  • More on the tidyverse
  • Working with pipes
  • Connor: Coding tips
  • Vectors and lists in R
  • Dates and times in R
  • Other common data structures
  • Logical operators and conditional statements
  • Guide: Keeping your code readable
  • Available R packages
  • R resources for more help
  • Glossary: Terms and definitions
  • Test your knowledge on programming concepts
  • Hands-On Activity: R sandbox
  • Test your knowledge on coding in R
  • Hands-On Activity: Installing and loading tidyverse
  • Test your knowledge on R packages
  • Test your knowledge on the tidyverse
  • Weekly challenge 2
  • Working with data in R
  • Data in R
  • R data frames
  • Working with data frames
  • Cleaning up with the basics
  • Organize your data
  • Transforming data
  • Same data, different outcome
  • The bias function
  • More about tibbles
  • Data-import basics
  • File-naming conventions
  • More on R operators
  • Optional: Manually create a data frame
  • Wide to long with tidyr
  • Working with biased data
  • Glossary: Terms and definitions
  • Hands-On Activity: Create your own data frame
  • Hands-On Activity: Importing and working with data
  • Test your knowledge on R data frames
  • Hands-On Activity: Cleaning data in R
  • Test your knowledge on cleaning data
  • Hands-On Activity: Changing your data
  • Test your knowledge on R functions
  • Weekly challenge 3
  • More about visualizations, aesthetics, and annotations
  • Visualizations in R
  • Visualization basics in R and tidyverse
  • Getting started with ggplot()
  • Joseph: Career path to people analytics
  • Enhancing visualizations in R
  • Doing more with ggplot
  • Aesthetics and facets
  • Annotation layer
  • Saving your visualizations
  • Common problems when visualizing in R
  • Aesthetic attributes
  • Smoothing
  • Filtering and plots
  • Drawing arrows and shapes in R
  • Saving images without ggsave()
  • Glossary: Terms and definitions
  • Hands-On Activity: Visualizing data with ggplot2
  • Hands-On Activity: Using ggplot
  • Test your knowledge on data visualizations in R
  • Hands-On Activity: Aesthetics and visualizations
  • Hands-On Activity: Filters and plots
  • Test your knowledge on aesthetics in analysis
  • Hands-On Activity: Annotating and saving visualizations
  • Test your knowledge on annotating and saving visualizations
  • Weekly challenge 4
  • Documentation and reports
  • Documentation and reports
  • Overview of R Markdown
  • Using R Markdown in RStudio
  • Structure of markdown documents
  • Meg: Programming is empowering
  • Even more document elements
  • Code chunks
  • Exporting documentation
  • R Markdown resources
  • Optional: Jupyter notebooks
  • Output formats in R Markdown
  • Glossary: Terms and definitions
  • Hands-On Activity: Your R Markdown notebook
  • Test your knowledge about documentation and reports
  • Test your knowledge about creating R Markdown documents
  • Hands-On Activity: Adding code chunks to R Markdown notebooks
  • Hands-On Activity: Exporting your R Markdown notebook
  • Hands-On Activity: Using R Markdown templates
  • Test your knowledge on code chunks
  • Weekly challenge 5
  • Course challenge

Summary of User Reviews

Learn data analysis with R on Coursera. Users have given this course high ratings and praise it for its comprehensive content. Many users appreciate the practical approach of the course, with real-world examples and exercises.

Key Aspect Users Liked About This Course

The course covers a wide range of topics and provides practical exercises for learners to apply their knowledge.

Pros from User Reviews

  • Comprehensive content covering a wide range of topics
  • Practical approach with real-world examples and exercises
  • Instructor is knowledgeable and engaging
  • Course is well-structured and easy to follow
  • Great for beginners who want to learn R for data analysis

Cons from User Reviews

  • Some users would like more advanced content
  • Some users feel the course could be more challenging
  • Some users have experienced technical issues with the platform
  • Some users find the pace of the course too slow
  • Some users feel that the course could benefit from more interactive elements
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Approx. 37 hours to complete
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