IBM
Introduction to R Programming for Data Science
IBM

Introduction to R Programming for Data Science

This course is part of multiple programs.

Taught in English

Some content may not be translated

Yan Luo

Instructor: Yan Luo

42,729 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.5

(432 reviews)

|

94%

Beginner level
No prior experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Manipulate primitive data types in the R programming language using RStudio or Jupyter Notebooks.

  • Control program flow with conditions and loops, write functions, perform character string operations, write regular expressions, handle errors.

  • Construct and manipulate R data structures, including vectors, factors, lists, and data frames.

  • Read, write, and save data files and scrape web pages using R.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(432 reviews)

|

94%

Beginner level
No prior experience required
10 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

Placeholder

Build your subject-matter expertise

This course is available as part of
When you enroll in this course, you'll also be asked to select a specific program.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
Placeholder
Placeholder

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV

Share it on social media and in your performance review

Placeholder

There are 5 modules in this course

Regardless of the programming language you use, all share some commonalities. For example, you’ll likely need to perform basic operations on different data types, like applying mathematical equations to numeric data. You’ll also need an environment in which to write your code, anbbd most modern integrated development environments (or IDEs) provide features that make writing code easier, like syntax checking, color coding, and integrated help. This module introduces you to the R language, its common data types, and techniques for manipulating them. You’ll also learn about the role of the R interpreter and how it transforms code into executable objects. Finally, you’ll be introduced to two of the most common IDEs for R development: RStudio and Jupyter Notebook.

What's included

7 videos1 reading2 quizzes2 app items

The R language supports many types of data structures that you can use to organize and store values in your code, including vectors, factors, lists, arrays, matrices, and data frames. Each data structure type serves a specific purpose and can contain specific kinds of data. So, it’s important to understand the differences between them so you can make the right choice based on your scenario. In this module, you’ll learn about the types of data you can store in each data structure and how to add, remove, or manipulate its contents.

What's included

5 videos1 reading2 quizzes3 app items

As with most programming languages, R supports coding features that you can use to control the flow of program execution, define functions that can perform specific tasks, work with common data types, like strings and dates, and make your code more robust by intercepting likely errors and handling them before they interrupt the execution of your code. In this module, you’ll learn how to implement these fundamental programming tasks in R.

What's included

6 videos1 reading2 quizzes3 app items

Data is everywhere! The data you need to analyze may come from a traditional database, but it may also come from a variety of different sources and systems, and it may come to you in one or more formats. For example, your data might be in text, Excel, .JSON, or .XML files. Or it may not be stored in a file at all, but instead lives on the pages of a website. How will you take all these different file formats and load them into your R working environment? This module provides you with the tools you need to read data from some common file formats and sources into data objects that you can then use and combine with other data objects in your data analysis.

What's included

5 videos1 reading2 quizzes3 app items

What's included

2 readings1 quiz1 peer review1 app item1 plugin

Instructor

Instructor ratings
4.5 (127 ratings)
Yan Luo
IBM
7 Courses289,566 learners

Offered by

IBM

Recommended if you're interested in Data Analysis

Why people choose Coursera for their career

Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

Showing 3 of 432

4.5

432 reviews

  • 5 stars

    71.49%

  • 4 stars

    20.22%

  • 3 stars

    3.21%

  • 2 stars

    1.37%

  • 1 star

    3.67%

EE
5

Reviewed on Mar 7, 2022

LI
4

Reviewed on Aug 4, 2021

GK
5

Reviewed on Aug 18, 2023

New to Data Analysis? Start here.

Placeholder

Open new doors with Coursera Plus

Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions