Course Summary
Learn how to use the R programming environment for data analysis, visualization, and statistical computing. This course covers the basics of programming in R and provides hands-on experience in working with data.Key Learning Points
- Understand the basics of programming in R
- Manipulate and manage data using R
- Create data visualizations in R
- Perform statistical analysis using R
- Learn best practices for programming in R
Related Topics for further study
Learning Outcomes
- Create data visualizations using R
- Perform statistical analysis using R
- Manage and manipulate data in R
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Experience with a programming language (recommended)
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on assignments
Similar Courses
- Data Science Essentials
- Data Visualization with Tableau
- Python for Data Science
Related Education Paths
Related Books
Description
This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.
Outline
- Basic R Language
- Welcome to the R Programming Environment
- Course Textbook: Mastering Software Development in R
- Syllabus
- Swirl Assignments
- Datasets
- Lesson Introduction
- Evaluation
- Objects
- Numbers
- Creating Vectors
- Mixing Objects
- Explicit Coercion
- Matrices
- Lists
- Factors
- Missing Values
- Data Frames
- Names
- Attributes
- Summary
- The Importance of Tidy Data
- The “Tidyverse”
- Reading Tabular Data with the readr Package
- Reading Web-Based Data
- Flat files online
- Requesting data through a web API
- Scraping web data
- Parsing JSON, XML, or HTML data
- Basic R Language: Lesson Choices
- Swirl Lessons
- Data Manipulation
- Basic Data Manipulation
- Piping
- Summarizing data
- Selecting and filtering data
- Adding, changing, or renaming columns
- Spreading and gathering data
- Merging datasets
- Working with Dates, Times, Time Zones
- Converting to a date or date-time class
- Pulling out date and time elements
- Working with time zones
- Data Manipulation: Lesson Choices
- Swirl Lessons
- Text Processing, Regular Expression, & Physical Memory
- Text Processing and Regular Expressions
- Text Manipulation Functions in R
- Regular Expressions
- RegEx Functions in R
- The stringr Package
- Summary
- The Role of Physical Memory
- Back of the Envelope Calculations
- Internal Memory Management in R
- Text Processing, Regular Expression, & Physical Memory: Lesson Choices
- Swirl Lessons
- Large Datasets
- Working with Large Datasets
- In-memory strategies
- Out-of-memory strategies
- Diagnosing Problems
- How to Google Your Way Out of a Jam
- Asking for Help
- Quiz Instructions
- Reading and Summarizing Data
Summary of User Reviews
Discover the R programming environment with this comprehensive course from Coursera. Learn the fundamentals of R programming and gain hands-on experience with practical assignments. Overall, this course has received positive reviews for its engaging content and helpful instructors.Key Aspect Users Liked About This Course
Many users appreciated the practical assignments that allowed them to apply the concepts they learned in the course.Pros from User Reviews
- Engaging and well-structured content
- Clear explanations and helpful instructors
- Practical assignments that apply the concepts learned
- Great for beginners to learn R programming
- Flexible schedule for completing assignments
Cons from User Reviews
- Some users found the pacing of the course to be too slow
- Not enough emphasis on advanced topics
- Limited interaction with other students
- Some technical issues with the Coursera platform
- Some users found the course to be too basic
Roger D. Peng, PhD
4.4 Raiting
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