Об этом курсе: 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.
Для кого этот курс: This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.
Автор: Johns Hopkins University
Преподаватели: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Преподаватели: Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University
| Основные сведения | Course 1 of 5 in the Mastering Software Development in R Specialization |
| Уровень | Intermediate |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 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 видео, 27 материалов для самостоятельного изучения
- Видео: 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
Оцениваемый: Swirl Lessons
НЕДЕЛЯ 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 материалов для самостоятельного изучения
- 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
Оцениваемый: Swirl Lessons
НЕДЕЛЯ 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 материалов для самостоятельного изучения
- 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
Оцениваемый: Swirl Lessons
НЕДЕЛЯ 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 материалов для самостоятельного изучения
- 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
Оцениваемый: Reading and Summarizing Data
Часто задаваемые вопросы
Как это работает
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.
Авторы
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.
Стоимость
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| Получить доступ к материалам курса | Доступен |
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| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Рейтинги и отзывы
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.
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SS
An excellent course. Very good introduction to R and certain packages such as dplyr and grepl. Hands-on and challenging