About this course: 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.

Who is this class for: This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.

Created by: Johns Hopkins University

Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University

Basic Info	Course 1 of 5 in the Mastering Software Development in R Specialization.
Level	Intermediate
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.3 stars Average User Rating 4.3See what learners said

Syllabus

WEEK 1

Basic R Language

In this module, you'll learn the basics of R, including syntax, some tidy data principles and processes, and how to read data into R.

1 video, 27 readings

Graded: Swirl Lessons

WEEK 2

Data Manipulation

During this module, you'll learn to summarize, filter, merge, and otherwise manipulate data in R, including working through the challenges of dates and times.

11 readings

Graded: Swirl Lessons

WEEK 3

Text Processing, Regular Expression, & Physical Memory

During this module, you'll learn to use R tools and packages to deal with text and regular expressions. You'll also learn how to manage and get the most from your computer's physical memory when working in R.

9 readings

Graded: Swirl Lessons

WEEK 4

Large Datasets

In this final module, you'll learn how to overcome the challenges of working with large datasets both in memory and out as well as how to diagnose problems and find help.

7 readings

Graded: Reading and Summarizing Data

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Pricing

	Audit	Purchase Course
Access to course materials	Available	Available
Access to graded materials	Not available	Available
Receive a final grade	Not available	Available
Earn a shareable Course Certificate	Not available	Available

Ratings and Reviews

Rated 4.3 out of 5 of 231 ratings

Useful, but it is all text so the course is not very fun

Easy, well fundamented course. Mentoring is great!

Good Practice..........!!!...really helpful for building data science concept through R Programming.........Really salute for hardworking of instructor..................!!!

It's a good course. I've learned many things. The packages tidyr and dplyr are really amazing.

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The R Programming Environment

The R Programming Environment

The R Programming Environment