R Programming

CHAPTER 1. INTRODUCTION

• Installing R
• Character Terminal and GUI Interfaces to R
• Other GUI Integrated Development Environments

CHAPTER 2. WORKING WITH R

• Running R
• Learning GUI Integrated Development Environment
• Interacting with R Interpreter
• R Sessions and Workspaces
• Saving Your Workspace
• Loading Your Workspace
• Removing Objects in Workspace
• Getting Help
• Getting System Information
• Standard R Packages
• Loading Packages
• CRAN (The Comprehensive R Archive Network)
• Extending R

CHAPTER 3. R SYNTAX

• General Notes on R Commands and Statements
• Variables
• Assignment Operators
• Arithmetic Operators
• Logical Operators

CHAPTER 4. R DATA STRUCTURES

• R Objects
• Vectors
• Logical Vectors
• Character Vectors
• Creating and Working with Vectors
• Lists
• Creating and Working with Lists
• Matrices
• Creating and Working with Matrices
• Data Frames
• Creating and Working with Data Frames
• Interactive Creation of Data Frames
• Getting Info about a Data Frame
• Sorting Data in Data Frames
• Matrices vs Data Frames

CHAPTER 5. FUNCTIONS

• Using R Common Functions
• Numeric Functions
• Character / String Functions
• Date and Time Functions
• Other Useful Functions
• Applying Functions to Matrices and Data Frames
• Type Conversion
• Creating and Using User-Defined Functions

CHAPTER 6. CONTROL STATEMENTS

• Conditional Execution
• Repetitive Execution

CHAPTER 7. SCRIPTS

• Creating Scripts
• Loading and Executing Scripts
• Batch Execution Mode

CHAPTER 8. INPUT / OUTPUT

• Reading Data from Files
• Writing Data to Files
• Getting the List of Files in a Directory
• Diverting System Output to a File

CHAPTER 9. DATA IMPORT AND EXPORT

• Import and Export Operations in R
• Working with CSV Files
• Reading Data from Excel
• Exporting Data in SPSS Data Format

CHAPTER 10. R STATISTICAL COMPUTING FEATURES

• Basic Statistical Functions
• Writing Your Own skew and kurtosis Functions
• Generating Normally Distributed Random Numbers
• Generating Uniformly Distributed Random Numbers
• Using the summary() Function
• Math Functions Used in Data Analysis
• Correlations
• Testing Correlation Coefficient for Significance
• Regression Analysis
• Types of Regression
• Simple Linear Regression Model
• Least-Squares Method (LSM)
• LSM Assumptions
• Fitting Linear Regression Models in R
• Confidence Intervals for Model Parameters
• Multiple Regression Analysis
• Finding the Best-Fitting Regression Model
• Comparing Regression Models with anova and AIC

CHAPTER 11. DATA VISUALIZATION

• R Graphics
• Graphics Export Options
• Creating Bar Plots in R
• Using barplot() with Matrices
• Stacked vs Juxtaposed Layouts
• Customizing Plots
• Histograms
• Building Histograms with hist()
• Pie Charts
• Generic X-Y Plotting
• Dot Plots

CHAPTER 12. DATA SCIENCE ALGORITHMS AND ANALYTICAL METHODS

• Supervised and Unsupervised Machine Learning Algorithms
• k-Nearest Neighbors
• Monte Carlo Simulation

Participants should have the general knowledge of statistics and programming