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	4.4 stars Average User Rating 4.4See 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

Video: Welcome to the R Programming Environment
Reading: Course Textbook: Mastering Software Development in R
Reading: Syllabus
Reading: Swirl Assignments
Reading: Datasets
Reading: Lesson Introduction
Reading: Evaluation
Reading: Objects
Reading: Numbers
Reading: Creating Vectors
Reading: Mixing Objects
Reading: Explicit Coercion
Reading: Matrices
Reading: Lists
Reading: Factors
Reading: Missing Values
Reading: Data Frames
Reading: Names
Reading: Attributes
Reading: Summary
Reading: The Importance of Tidy Data
Reading: The “Tidyverse”
Reading: Reading Tabular Data with the readr Package
Reading: Reading Web-Based Data
Reading: Flat files online
Reading: Requesting data through a web API
Reading: Scraping web data
Reading: Parsing JSON, XML, or HTML data

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

Reading: Basic Data Manipulation
Reading: Piping
Reading: Summarizing data
Reading: Selecting and filtering data
Reading: Adding, changing, or renaming columns
Reading: Spreading and gathering data
Reading: Merging datasets
Reading: Working with Dates, Times, Time Zones
Reading: Converting to a date or date-time class
Reading: Pulling out date and time elements
Reading: Working with time zones

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

Reading: Text Processing and Regular Expressions
Reading: Text Manipulation Functions in R
Reading: Regular Expressions
Reading: RegEx Functions in R
Reading: The stringr Package
Reading: Summary
Reading: The Role of Physical Memory
Reading: Back of the Envelope Calculations
Reading: Internal Memory Management in R

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

Reading: Working with Large Datasets
Reading: In-memory strategies
Reading: Out-of-memory strategies
Reading: Diagnosing Problems
Reading: How to Google Your Way Out of a Jam
Reading: Asking for Help
Reading: Quiz Instructions

Graded: Reading and Summarizing Data

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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

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Ratings and Reviews

Rated 4.4 out of 5 of 584 ratings

An excellent course. Very good introduction to R and certain packages such as dplyr and grepl. Hands-on and challenging

A really good introduction to R.

very good start

English is not my native language so i had troubles with tasks, because it is sometimes

hard to understand what you have to do. My advice - rewrite final quiz and make it more easy for understanding.

The R Programming Environment

The R Programming Environment