Course outline

Module 1:  Overview of R

• What is R?
• Basic Features of R
• Free Software
• Design of the R System
• Limitations of R
• R Resources
• Getting Started with R
• Installation
• Getting started with the R interface

Module 2:  R Nuts and Bolts

• Entering Input
• Evaluation
• R Objects
• Numbers
• Attributes
• Creating Vectors
• Mixing Objects
• Explicit Coercion
• Matrices
• Lists
• Factors
• Missing Values
• Data Frames
• Names

Module 3: Getting Data in and out of R

• Reading and Writing Data
• Reading Data Files with read.table()
• Reading in Larger Datasets with read.table
• Calculating Memory Requirements for R Objects
• Using the readr Package
• Using Textual and Binary Formats for Storing Data
• Using dput() and dump()
• Binary Formats
• Interfaces to the Outside World
• File Connections
• Reading Lines of a Text File
• Reading From a URL Connection

Module 4: Subsetting R Objects

• Subsetting a Vector
• Subsetting a Matrix
• Subsetting Lists
• Subsetting Nested Elements of a List
• Extracting Multiple Elements of a List
• Partial Matching
• Removing NA Values
• Vectorized Operations
• Vectorized Matrix Operations

Module 5: Dates and Times

• Dates in R
• Times in R
• Operations on Dates and Times
• Summary

Module 6: Managing Data Frames with the dplyr package

• Data Frames
• The dplyr Package
• dplyr Grammar
• Installing the dplyr package
• select()
• filter()
• arrange()
• rename()
• mutate()
• group_by()
• %>% 60

Module 7: Control Structures

• if-else
• for Loops
• Nested for loops
• while Loops
• repeat Loops
• next, break

Module 8: Functions

• Functions in R
• Your First Function
• Argument Matching
• Lazy Evaluation
• The … Argument
• Arguments Coming After the … Argument

Module 9: Scoping Rules of R

• A Diversion on Binding Values to Symbol
• Scoping Rules
• Lexical Scoping: Why Does It Matter?
• Lexical vs. Dynamic Scoping
• Application: Optimization
• Plotting the Likelihood

Module 10: Coding Standards for R

• Loop Functions
• Looping on the Command Line
• lapply()
• sapply()
• split()
• Splitting a Data Frame
• tapply
• apply()
• Col/Row Sums and Means
• Other Ways to Apply
• mapply()
• Vectorizing a Function

Module 11: Debugging

• Something’s Wrong!
• Figuring Out What’s Wrong
• Debugging Tools in R
• Using traceback()
• Using debug()
• Using recover()

Module 12: Profiling R Code

• Using system.time()
• Timing Longer Expressions
• The R Profiler
• Using summaryRprof()

Module 13: Simulation

• Generating Random Numbers
• Setting the random number seed
• Simulating a Linear Model
• Random Sampling