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 forma parte de Programa Especializado - Ciencia de Datos
Programación R
ofrecido por
Acerca de este Curso
4.6
13,581 calificaciones
Qué aprenderás
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 obtendrás
Data AnalysisDebuggingR ProgrammingRstudio
Programa - Qué aprenderás en este curso
Semana
125 horas para completar
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 videos (Total 129 minutos), 9 readings, 8 quizzes
28 videos
Installing R on Windows3m
Installing R Studio (Mac)1m
Writing Code / Setting Your Working Directory (Windows)7m
Writing Code / Setting Your Working Directory (Mac)7m
Introduction1m
Overview and History of R16m
Getting Help13m
R Console Input and Evaluation4m
Data Types - R Objects and Attributes4m
Data Types - Vectors and Lists6m
Data Types - Matrices3m
Data Types - Factors4m
Data Types - Missing Values2m
Data Types - Data Frames2m
Data Types - Names Attribute1m
Data Types - Summary43s
Reading Tabular Data5m
Reading Large Tables7m
Textual Data Formats4m
Connections: Interfaces to the Outside World4m
Subsetting - Basics4m
Subsetting - Lists4m
Subsetting - Matrices2m
Subsetting - Partial Matching1m
Subsetting - Removing Missing Values3m
Vectorized Operations3m
Introduction to swirl1m
9 lecturas
Welcome to R Programming10m
About the Instructor10m
Pre-Course Survey10m
Syllabus10m
Course Textbook10m
Course Supplement: The Art of Data Science10m
Data Science Podcast: Not So Standard Deviations10m
Getting Started and R Nuts and Bolts10m
Practical R Exercises in swirl Part 110m
1 ejercicio de práctica
Week 1 Quiz40m
Semana
212 horas para completar
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 videos (Total 91 minutos), 3 readings, 5 quizzes
13 videos
Control Structures - If-else1m
Control Structures - For loops4m
Control Structures - While loops3m
Control Structures - Repeat, Next, Break4m
Your First R Function10m
Functions (part 1)9m
Functions (part 2)7m
Scoping Rules - Symbol Binding10m
Scoping Rules - R Scoping Rules8m
Scoping Rules - Optimization Example (OPTIONAL)9m
Coding Standards8m
Dates and Times10m
3 lecturas
Week 2: Programming with R10m
Practical R Exercises in swirl Part 210m
Programming Assignment 1 INSTRUCTIONS: Air Pollution10m
2 ejercicios de práctica
Week 2 Quiz20m
Programming Assignment 1: Quiz20m
Semana
310 horas para completar
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 videos (Total 61 minutos), 2 readings, 4 quizzes
8 videos
Loop Functions - apply7m
Loop Functions - mapply4m
Loop Functions - tapply3m
Loop Functions - split9m
Debugging Tools - Diagnosing the Problem12m
Debugging Tools - Basic Tools6m
Debugging Tools - Using the Tools8m
2 lecturas
Week 3: Loop Functions and Debugging10m
Practical R Exercises in swirl Part 310m
1 ejercicio de práctica
Week 3 Quiz10m
Semana
411 horas para completar
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 videos (Total 42 minutos), 4 readings, 5 quizzes
6 videos
Simulation - Generating Random Numbers7m
Simulation - Simulating a Linear Model4m
Simulation - Random Sampling2m
R Profiler (part 1)10m
R Profiler (part 2)10m
4 lecturas
Week 4: Simulation & Profiling10m
Practical R Exercises in swirl Part 410m
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10m
Post-Course Survey10m
2 ejercicios de práctica
Week 4 Quiz20m
Programming Assignment 3: Quiz20m
32%
comenzó una nueva carrera después de completar estos cursos
32%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
11%
consiguió un aumento de sueldo o ascenso
Principales revisiones
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!
"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!
Instructores
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
Acerca de Universidad Johns Hopkins
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.
Acerca del programa especializado Ciencia de Datos
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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