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.
Este curso faz parte do Programa de cursos integrados Data Science
R Programming
oferecido por
Informações sobre o curso
4.6
12,760 classificações
O que você vai aprender
Collect detailed information using R profiler
Configure statistical programming software
Make use of R loop functions and debugging tools
Understand critical programming language concepts
Habilidades que você terá
Data AnalysisDebuggingR ProgrammingRstudio
Programa - O que você aprenderá com este curso
Semana
1Week 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 vídeos (total de (Total 129 mín.) min), 9 leituras, 8 testes
28 videos
Installing R on Windows3min
Installing R Studio (Mac)1min
Writing Code / Setting Your Working Directory (Windows)7min
Writing Code / Setting Your Working Directory (Mac)7min
Introduction1min
Overview and History of R16min
Getting Help13min
R Console Input and Evaluation4min
Data Types - R Objects and Attributes4min
Data Types - Vectors and Lists6min
Data Types - Matrices3min
Data Types - Factors4min
Data Types - Missing Values2min
Data Types - Data Frames2min
Data Types - Names Attribute1min
Data Types - Summary43s
Reading Tabular Data5min
Reading Large Tables7min
Textual Data Formats4min
Connections: Interfaces to the Outside World4min
Subsetting - Basics4min
Subsetting - Lists4min
Subsetting - Matrices2min
Subsetting - Partial Matching1min
Subsetting - Removing Missing Values3min
Vectorized Operations3min
Introduction to swirl1min
9 leituras
Welcome to R Programming10min
About the Instructor10min
Pre-Course Survey10min
Syllabus10min
Course Textbook10min
Course Supplement: The Art of Data Science10min
Data Science Podcast: Not So Standard Deviations10min
Getting Started and R Nuts and Bolts10min
Practical R Exercises in swirl Part 110min
1 exercício prático
Week 1 Quiz40min
Semana
2Week 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 vídeos (total de (Total 91 mín.) min), 3 leituras, 5 testes
13 videos
Control Structures - If-else1min
Control Structures - For loops4min
Control Structures - While loops3min
Control Structures - Repeat, Next, Break4min
Your First R Function10min
Functions (part 1)9min
Functions (part 2)7min
Scoping Rules - Symbol Binding10min
Scoping Rules - R Scoping Rules8min
Scoping Rules - Optimization Example (OPTIONAL)9min
Coding Standards8min
Dates and Times10min
3 leituras
Week 2: Programming with R10min
Practical R Exercises in swirl Part 210min
Programming Assignment 1 INSTRUCTIONS: Air Pollution10min
2 exercícios práticos
Week 2 Quiz20min
Programming Assignment 1: Quiz20min
Semana
3Week 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 vídeos (total de (Total 61 mín.) min), 2 leituras, 4 testes
8 videos
Loop Functions - apply7min
Loop Functions - mapply4min
Loop Functions - tapply3min
Loop Functions - split9min
Debugging Tools - Diagnosing the Problem12min
Debugging Tools - Basic Tools6min
Debugging Tools - Using the Tools8min
2 leituras
Week 3: Loop Functions and Debugging10min
Practical R Exercises in swirl Part 310min
1 exercício prático
Week 3 Quiz10min
Semana
4Week 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 vídeos (total de (Total 42 mín.) min), 4 leituras, 5 testes
6 videos
The str Function6min
Simulation - Generating Random Numbers7min
Simulation - Simulating a Linear Model4min
Simulation - Random Sampling2min
R Profiler (part 1)10min
R Profiler (part 2)10min
4 leituras
Week 4: Simulation & Profiling10min
Practical R Exercises in swirl Part 410min
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10min
Post-Course Survey10min
2 exercícios práticos
Week 4 Quiz20min
Programming Assignment 3: Quiz20min
28%
comecei uma nova carreira após concluir estes cursos
28%
consegui um benefício significativo de carreira com este curso
11%
recebi um aumento ou promoção
Melhores avaliações
por EJ•Jul 12th 2016
Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!
por AK•May 27th 2017
This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.\n\nThis is perhaps the best course on R Programming designed for a small duration.
Instrutores
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
Sobre 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.
Sobre o Programa de cursos integrados Data Science
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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