In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
提供：
このコースについて
611,555 最近の表示
学習内容
Collect detailed information using R profiler
Configure statistical programming software
Make use of R loop functions and debugging tools
Understand critical programming language concepts
習得するスキル
Data AnalysisDebuggingR ProgrammingRstudio
シラバス - 本コースの学習内容
週
125時間で修了
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
28件のビデオ （合計129分）, 9 readings, 8 quizzes
28件のビデオ
Writing Code / Setting Your Working Directory (Windows)7 分
Writing Code / Setting Your Working Directory (Mac)7 分
Introduction1 分
Overview and History of R16 分
Getting Help13 分
R Console Input and Evaluation4 分
Data Types - R Objects and Attributes4 分
Data Types - Vectors and Lists6 分
Data Types - Matrices3 分
Data Types - Factors4 分
Data Types - Missing Values2 分
Data Types - Data Frames2 分
Data Types - Names Attribute1 分
Data Types - Summary43
Reading Tabular Data5 分
Reading Large Tables7 分
Textual Data Formats4 分
Connections: Interfaces to the Outside World4 分
Subsetting - Basics4 分
Subsetting - Lists4 分
Subsetting - Matrices2 分
Subsetting - Partial Matching1 分
Subsetting - Removing Missing Values3 分
Vectorized Operations3 分
Introduction to swirl1 分
9件の学習用教材
Welcome to R Programming10 分
About the Instructor10 分
Pre-Course Survey10 分
Syllabus10 分
Course Textbook10 分
Course Supplement: The Art of Data Science10 分
Data Science Podcast: Not So Standard Deviations10 分
Getting Started and R Nuts and Bolts10 分
Practical R Exercises in swirl Part 110 分
1の練習問題
Week 1 Quiz40 分
週
212時間で修了
Week 2: Programming with R
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
13件のビデオ （合計91分）, 3 readings, 5 quizzes
13件のビデオ
Control Structures - While loops3 分
Control Structures - Repeat, Next, Break4 分
Your First R Function10 分
Functions (part 1)9 分
Functions (part 2)7 分
Scoping Rules - Symbol Binding10 分
Scoping Rules - R Scoping Rules8 分
Scoping Rules - Optimization Example (OPTIONAL)9 分
Coding Standards8 分
Dates and Times10 分
3件の学習用教材
Week 2: Programming with R10 分
Practical R Exercises in swirl Part 210 分
Programming Assignment 1 INSTRUCTIONS: Air Pollution10 分
2の練習問題
Week 2 Quiz20 分
Programming Assignment 1: Quiz20 分
週
310時間で修了
Week 3: Loop Functions and Debugging
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
8件のビデオ （合計61分）, 2 readings, 4 quizzes
8件のビデオ
Loop Functions - tapply3 分
Loop Functions - split9 分
Debugging Tools - Diagnosing the Problem12 分
Debugging Tools - Basic Tools6 分
Debugging Tools - Using the Tools8 分
2件の学習用教材
Week 3: Loop Functions and Debugging10 分
Practical R Exercises in swirl Part 310 分
1の練習問題
Week 3 Quiz10 分
週
411時間で修了
Week 4: Simulation & Profiling
This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.
6件のビデオ （合計42分）, 4 readings, 5 quizzes
6件のビデオ
Simulation - Random Sampling2 分
R Profiler (part 1)10 分
R Profiler (part 2)10 分
4件の学習用教材
Week 4: Simulation & Profiling10 分
Practical R Exercises in swirl Part 410 分
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10 分
Post-Course Survey10 分
2の練習問題
Week 4 Quiz20 分
Programming Assignment 3: Quiz20 分
4.6
3045件のレビュー37%
コース終了後に新しいキャリアをスタートした
35%
コースが具体的なキャリアアップにつながった
11%
昇給や昇進につながった
Rプログラミング からの人気レビュー
Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
講師
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
ジョンズ・ホプキンズ大学（Johns Hopkins University）について
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.
データサイエンス専門講座について
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
よくある質問
いつ講座や課題にアクセスできるようになりますか？
修了証に登録すると、すべてのビデオ、テスト、およびプログラミング課題（該当する場合）にアクセスできます。ピアレビュー課題は、セッションが開始してからのみ、提出およびレビューできます。購入せずにコースを検討することを選択する場合、特定の課題にアクセスすることはできません。
この専門講座をサブスクライブすると何を行うことができるようになりますか？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
コーセラは、世界トップクラスの教育を世界中に配信しています。
最高峰の大学や組織とのパートナーシップに基づくオンラインコースが提供されています。
© 2019 Coursera (株)すべての権利予約します。
