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Includes the following:
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Lecture Videos (HD)
About the Book
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 book 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.
If you are interested in a printed copy of this book, you can purchase one at Lulu.
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Copy embed codeTable of Contents
- 1. Stay in Touch!
- 2. Preface
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3. History and Overview of R
- 3.1 What is R?
- 3.2 What is S?
- 3.3 The S Philosophy
- 3.4 Back to R
- 3.5 Basic Features of R
- 3.6 Free Software
- 3.7 Design of the R System
- 3.8 Limitations of R
- 3.9 R Resources
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4. Getting Started with R
- 4.1 Installation
- 4.2 Getting started with the R interface
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5. R Nuts and Bolts
- 5.1 Entering Input
- 5.2 Evaluation
- 5.3 R Objects
- 5.4 Numbers
- 5.5 Attributes
- 5.6 Creating Vectors
- 5.7 Mixing Objects
- 5.8 Explicit Coercion
- 5.9 Matrices
- 5.10 Lists
- 5.11 Factors
- 5.12 Missing Values
- 5.13 Data Frames
- 5.14 Names
- 5.15 Summary
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6. Getting Data In and Out of R
- 6.1 Reading and Writing Data
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6.2 Reading Data Files with
read.table() - 6.3 Reading in Larger Datasets with read.table
- 6.4 Calculating Memory Requirements for R Objects
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7. Using the
readrPackage -
8. Using Textual and Binary Formats for Storing Data
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8.1 Using
dput()anddump() - 8.2 Binary Formats
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8.1 Using
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9. Interfaces to the Outside World
- 9.1 File Connections
- 9.2 Reading Lines of a Text File
- 9.3 Reading From a URL Connection
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10. Subsetting R Objects
- 10.1 Subsetting a Vector
- 10.2 Subsetting a Matrix
- 10.3 Subsetting Lists
- 10.4 Subsetting Nested Elements of a List
- 10.5 Extracting Multiple Elements of a List
- 10.6 Partial Matching
- 10.7 Removing NA Values
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11. Vectorized Operations
- 11.1 Vectorized Matrix Operations
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12. Dates and Times
- 12.1 Dates in R
- 12.2 Times in R
- 12.3 Operations on Dates and Times
- 12.4 Summary
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13. Managing Data Frames with the
dplyrpackage- 13.1 Data Frames
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13.2 The
dplyrPackage -
13.3
dplyrGrammar -
13.4 Installing the
dplyrpackage -
13.5
select() -
13.6
filter() -
13.7
arrange() -
13.8
rename() -
13.9
mutate() -
13.10
group_by() -
13.11
%>% - 13.12 Summary
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14. Control Structures
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14.1
if-else -
14.2
forLoops -
14.3 Nested
forloops -
14.4
whileLoops -
14.5
repeatLoops -
14.6
next,break - 14.7 Summary
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14.1
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15. Functions
- 15.1 Functions in R
- 15.2 Your First Function
- 15.3 Argument Matching
- 15.4 Lazy Evaluation
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15.5 The
...Argument -
15.6 Arguments Coming After the
...Argument - 15.7 Summary
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16. Scoping Rules of R
- 16.1 A Diversion on Binding Values to Symbol
- 16.2 Scoping Rules
- 16.3 Lexical Scoping: Why Does It Matter?
- 16.4 Lexical vs. Dynamic Scoping
- 16.5 Application: Optimization
- 16.6 Plotting the Likelihood
- 16.7 Summary
- 17. Coding Standards for R
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18. Loop Functions
- 18.1 Looping on the Command Line
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18.2
lapply() -
18.3
sapply() -
18.4
split() - 18.5 Splitting a Data Frame
- 18.6 tapply
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18.7
apply() - 18.8 Col/Row Sums and Means
- 18.9 Other Ways to Apply
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18.10
mapply() - 18.11 Vectorizing a Function
- 18.12 Summary
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19. Regular Expressions
- 19.1 Before You Begin
- 19.2 Primary R Functions
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19.3
grep() -
19.4
grepl() -
19.5
regexpr() -
19.6
sub()andgsub() -
19.7
regexec() - 19.8 Summary
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20. Debugging
- 20.1 Something’s Wrong!
- 20.2 Figuring Out What’s Wrong
- 20.3 Debugging Tools in R
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20.4 Using
traceback() -
20.5 Using
debug() -
20.6 Using
recover() - 20.7 Summary
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21. Profiling R Code
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21.1 Using
system.time() - 21.2 Timing Longer Expressions
- 21.3 The R Profiler
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21.4 Using
summaryRprof() - 21.5 Summary
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21.1 Using
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22. Simulation
- 22.1 Generating Random Numbers
- 22.2 Setting the random number seed
- 22.3 Simulating a Linear Model
- 22.4 Random Sampling
- 22.5 Summary
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23. Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.
- 23.1 Synopsis
- 23.2 Loading and Processing the Raw Data
- 23.3 Results
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24. Parallel Computation
- 24.1 Hidden Parallelism
- 24.2 Embarrassing Parallelism
- 24.3 The Parallel Package
- 24.4 Example: Bootstrapping a Statistic
- 24.5 Building a Socket Cluster
- 24.6 Summary
- 25. About the Author
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About the Author
Roger D. Peng
Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is also a co-founder of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the general public, and the Not So Standard Deviations podcast. He is the recipient of the 2016 Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to health statistics. Roger can be found on Twitter and GitHub at @rdpeng.
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