1: Introduction
1.1: Stay in Touch!
1.2: Preface
2: History and Overview of R
2.1: What is R?
2.2: What is S?
2.3: The S Philosophy
2.4: Back to R
2.5: Basic Features of R
2.6: Free Software
Exercise 1
2.7: Design of the R System
Exercise 2
2.8: Limitations of R
Exercise 3
2.9: R Resources
2.9.1: Official Manuals
2.9.2: Useful Standard Texts on S and R
2.9.3: Other Resources
Quiz 1
3 attempts allowed
3: Getting Started with R
3.1: Installation
3.2: Getting started with the R interface
4: R Nuts and Bolts
4.1: Entering Input
Exercise 4
4.2: Evaluation
Exercise 5
4.3: R Objects
Exercise 6
4.4: Numbers
Exercise 7
4.5: Attributes
Exercise 8
4.6: Creating Vectors
Exercise 9
4.7: Mixing Objects
Exercise 10
4.8: Explicit Coercion
4.9: Matrices
Exercise 11
4.10: Lists
Exercise 12
4.11: Factors
Exercise 13
4.12: Missing Values
Exercise 14
4.13: Data Frames
Exercise 15
4.14: Names
4.15: Summary
Exercise 16
Quiz 2
3 attempts allowed
5: Getting Data In and Out of R
5.1: Reading and Writing Data
Exercise 17
5.2: Reading Data Files with read.table()
Exercise 18
5.3: Reading in Larger Datasets with read.table
Exercise 19
5.4: Calculating Memory Requirements for R Objects
Exercise 20
Quiz 3
3 attempts allowed
6: Using the readr Package
Exercise 21
Quiz 4
3 attempts allowed
7: Using Textual and Binary Formats for Storing Data
Exercise 22
7.1: Using dput() and dump()
Exercise 23
7.2: Binary Formats
Exercise 24
Quiz 5
3 attempts allowed
8: Interfaces to the Outside World
8.1: File Connections
Exercise 25
8.2: Reading Lines of a Text File
Exercise 26
8.3: Reading From a URL Connection
Exercise 27
Quiz 6
3 attempts allowed
9: Subsetting R Objects
9.1: Subsetting a Vector
Exercise 28
9.2: Subsetting a Matrix
9.2.1: Dropping matrix dimensions
Exercise 29
9.3: Subsetting Lists
Exercise 30
9.4: Subsetting Nested Elements of a List
Exercise 31
9.5: Extracting Multiple Elements of a List
Exercise 32
9.6: Partial Matching
Exercise 33
9.7: Removing NA Values
Exercise 34
Quiz 7
3 attempts allowed
10: Vectorized Operations
Exercise 35
10.1: Vectorized Matrix Operations
Exercise 36
Quiz 8
3 attempts allowed
11: Dates and Times
11.1: Dates in R
Exercise 37
11.2: Times in R
Exercise 38
11.3: Operations on Dates and Times
Exercise 39
11.4: Summary
Exercise 40
Quiz 9
3 attempts allowed
12: Managing Data Frames with the dplyr package
12.1: Data Frames
12.2: The dplyr Package
12.3: dplyr Grammar
12.3.1: Common dplyr Function Properties
Exercise 41
12.4: Installing the dplyr package
Exercise 42
12.5: select()
Exercise 43
12.6: filter()
Exercise 44
12.7: arrange()
Exercise 45
12.8: rename()
Exercise 46
12.9: mutate()
Exercise 47
12.10: group_by()
Exercise 48
12.11: %>%
Exercise 49
12.12: Summary
Quiz 10
3 attempts allowed
13: Control Structures
13.1: if-else
Exercise 50
13.2: for Loops
13.3: Nested for loops
Exercise 51
13.4: while Loops
13.5: repeat Loops
Exercise 52
13.6: next, break
Exercise 53
13.7: Summary
Exercise 54
Quiz 11
3 attempts allowed
14: Functions
14.1: Functions in R
Exercise 55
14.2: Your First Function
Exercise 56
14.3: Argument Matching
Exercise 57
14.4: Lazy Evaluation
14.5: The ... Argument
Exercise 58
14.6: Arguments Coming After the ... Argument
Exercise 59
14.7: Summary
Exercise 60
Quiz 12
3 attempts allowed
15: Scoping Rules of R
15.1: A Diversion on Binding Values to Symbol
Exercise 61
15.2: Scoping Rules
Exercise 62
15.3: Lexical Scoping: Why Does It Matter?
Exercise 63
15.4: Lexical vs. Dynamic Scoping
Exercise 64
15.5: Application: Optimization
Exercise 65
15.6: Plotting the Likelihood
Exercise 66
15.7: Summary
Quiz 13
3 attempts allowed
16: Coding Standards for R
Exercise 67
Quiz 14
3 attempts allowed
17: Loop Functions
17.1: Looping on the Command Line
Exercise 68
17.2: lapply()
Exercise 69
17.3: sapply()
Exercise 70
17.4: split()
Exercise 71
17.5: Splitting a Data Frame
Exercise 72
17.6: tapply
Exercise 73
17.7: apply()
Exercise 74
17.8: Col/Row Sums and Means
Exercise 75
17.9: Other Ways to Apply
Exercise 76
17.10: mapply()
Exercise 77
17.11: Vectorizing a Function
Exercise 78
17.12: Summary
Quiz 15
3 attempts allowed
18: Regular Expressions
18.1: Before You Begin
18.2: Primary R Functions
Exercise 79
18.3: grep()
Exercise 80
18.4: grepl()
Exercise 81
18.5: regexpr()
Exercise 82
18.6: sub() and gsub()
Exercise 83
18.7: regexec()
Exercise 84
18.8: The stringr Package
Exercise 85
18.9: Summary
Exercise 86
Quiz 16
3 attempts allowed
19: Debugging
19.1: Something’s Wrong!
Exercise 87
19.2: Figuring Out What’s Wrong
Exercise 88
19.3: Debugging Tools in R
Exercise 89
19.4: Using traceback()
Exercise 90
19.5: Using debug()
Exercise 91
19.6: Using recover()
Exercise 92
19.7: Summary
Exercise 93
Quiz 17
3 attempts allowed
20: Profiling R Code
20.1: Using system.time()
Exercise 94
20.2: Timing Longer Expressions
Exercise 95
20.3: The R Profiler
Exercise 96
20.4: Using summaryRprof()
Exercise 97
20.5: Summary
Exercise 98
Quiz 18
3 attempts allowed
21: Simulation
21.1: Generating Random Numbers
Exercise 99
21.2: Setting the random number seed
21.3: Simulating a Linear Model
Exercise 100
21.4: Random Sampling
Exercise 101
21.5: Summary
Exercise 102
Quiz 19
3 attempts allowed
22: Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.
22.1: Synopsis
22.2: Loading and Processing the Raw Data
22.2.1: Reading in the 1999 data
22.2.2: Reading in the 2012 data
Exercise 103
22.3: Results
22.3.1: Entire U.S. analysis
22.3.2: Changes in PM levels at an individual monitor
22.3.3: Changes in state-wide PM levels
Exercise 104
Quiz 20
3 attempts allowed
23: Parallel Computation
23.1: Hidden Parallelism
23.1.1: Parallel BLAS
Exercise 105
23.2: Embarrassing Parallelism
Exercise 106
23.3: The Parallel Package
23.3.1: mclapply()
23.3.2: Error Handling
Exercise 107
23.4: Example: Bootstrapping a Statistic
23.4.1: Generating Random Numbers
23.4.2: Using the boot package
23.5: Building a Socket Cluster
23.6: Summary
Quiz 21
3 attempts allowed
24: Why I Indent My Code 8 Spaces
25: About the Author
R Programming for Data Science
R Programming for Data Science
This course brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this course will lay the foundation for you to begin your journey learning data science.
The instructor is letting you choose the price you pay for this course!
The instructor is letting you choose the price you pay for this course!
This course brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this course will lay the foundation for you to begin your journey learning data science.
About
About the Course
Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world.
This course is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
This course is based on the book R Programming for Data Science.
Price
Course Price
Minimum price
$129.00
$179.00
You pay
$179.00Author earns
$143.20Instructor
About the Instructor
Roger D. Peng
Roger D. Peng is a Professor of Statistics and Data Sciences at the University of Texas, Austin. Previously, he was Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on the development of statistical methods for addressing environmental health problems and on developing tools for doing better data analysis. He is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng.
Episode 16
An Interview with Roger D. Peng
Material
Course Material
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