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.
Universidad Johns HopkinsAcerca de este Curso
781,878 vistas recientes
Resultados profesionales del estudiante
32%
comenzĆ³ una nueva carrera despuĆ©s de completar estos cursos
32%
consiguiĆ³ un beneficio tangible en su carrera profesional gracias a este curso
11%
consiguiĆ³ un aumento de sueldo o ascenso
Certificado para compartir
ObtƩn un certificado al finalizar
100Ā % en lĆnea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas lĆmite flexibles
Restablece las fechas lĆmite en funciĆ³n de tus horarios.
Nivel intermedio
Familiarity with regression is recommended.
Aprox. 57 horas para completar
InglƩs (English)
SubtĆtulos: Ćrabe (Arabic), FrancĆ©s (French), PortuguĆ©s (de Brasil), Chino (simplificado), Italiano, PortuguĆ©s (de Brasil), Vietnamita, Coreano, AlemĆ”n (German), Ruso (Russian), Turco (Turkish), InglĆ©s (English), EspaƱol (Spanish), JaponĆ©s, Chino (Chinese)
QuƩ aprenderƔs
Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler
Habilidades que obtendrƔs
Data AnalysisDebuggingR ProgrammingRstudio
Resultados profesionales del estudiante
32%
comenzĆ³ una nueva carrera despuĆ©s de completar estos cursos
32%
consiguiĆ³ un beneficio tangible en su carrera profesional gracias a este curso
11%
consiguiĆ³ un aumento de sueldo o ascenso
Certificado para compartir
ObtƩn un certificado al finalizar
100Ā % en lĆnea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas lĆmite flexibles
Restablece las fechas lĆmite en funciĆ³n de tus horarios.
Nivel intermedio
Familiarity with regression is recommended.
Aprox. 57 horas para completar
InglƩs (English)
SubtĆtulos: Ćrabe (Arabic), FrancĆ©s (French), PortuguĆ©s (de Brasil), Chino (simplificado), Italiano, PortuguĆ©s (de Brasil), Vietnamita, Coreano, AlemĆ”n (German), Ruso (Russian), Turco (Turkish), InglĆ©s (English), EspaƱol (Spanish), JaponĆ©s, Chino (Chinese)
ofrecido por
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.
Programa - QuƩ aprenderƔs en este curso
25 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.
25 horas para completar
28 videos (Total 129 minutos), 9 lecturas, 8 cuestionarios
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 prƔctica
Week 1 Quiz30m
12 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.
12 horas para completar
13 videos (Total 91 minutos), 3 lecturas, 5 cuestionarios
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 prƔctica
Week 2 Quiz30m
Programming Assignment 1: Quiz30m
9 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.
9 horas para completar
8 videos (Total 61 minutos), 2 lecturas, 4 cuestionarios
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 prƔctica
Week 3 Quiz30m
11 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.
11 horas para completar
6 videos (Total 42 minutos), 4 lecturas, 5 cuestionarios
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 prƔctica
Week 4 Quiz30m
Programming Assignment 3: Quiz30m
ReseƱas
Principales reseƱas sobre PROGRAMACIĆN R
por EJ11 de jul. de 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 WH2 de feb. de 2016
"R Programming" forces you to dive in deep. These skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
por JM11 de ago. de 2019
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.
por AK26 de may. de 2017
This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox. This is perhaps the best course on R Programming designed for a small duration.
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