R Programming from the Ground Up Training

Course Details

• 1. What is R
• What is R?
• Positioning of R in the Data Science Space
• The Legal Aspects, Microsoft R Open
• R Integrated Development Environments
• Running R, Running RStudio, Getting Help
• General Notes on R Commands and Statements
• Assignment Operators, R Core Data Structures
• Assignment Examplem R Objects and Workspace
• Printing Objects, Arithmetic Operators
• Logical Operators, System Date and Time
• Operations, User-defined Functions, Control Statements
• Conditional Execution, Repetitive Execution, Repetitive execution, Built-in Functions
• 2. Introduction to Functional Programming with R
• What is Functional Programming (FP)?
• Terminology: Higher-Order Functions, A Short List of Languages that Support FP
• Functional Programming in R, Vector and Matrix Arithmetic
• Vector Arithmetic Example, More Examples of FP in R
• 3. Managing Your Environment
• Getting and Setting the Working Directory
• Getting the List of Files in a Directory
• The R Home Directory
• Executing External R commands
• Loading External Scripts in RStudio
• Listing Objects in Workspace, Removing Objects in Workspace
• Saving Your Workspace in R, in RStudio, , in R GUI
• Loading Your Workspace
• Diverting Output to a File
• Batch (Unattended) Processing
• Controlling Global Options
• 4. R Type System and Structures
• The R Data Types, System Date and Time
• Formatting Date and Time, Using the mode() Function
• R Data Structures, What is the Type of My Data Structure?
• Creating Vectors, Logical Vectors, Character Vectors
• Factorization, Multi-Mode Vectors, The Length of the Vector
• Getting Vector Elements, Lists, A List with Element Names
• Extracting List Elements, Adding to a List
• Matrix Data Structure, Creating Matrices, Creating Matrices with cbind() and rbind()
• Working with Data Frames, Matrices vs Data Frames
• A Data Frame Sample, Creating a Data Frame
• Accessing Data Cells, Getting Info About a Data Frame
• Selecting Columns in Data Frames, Selecting Rows in Data Frames
• .
• Getting a Subset of a Data Frame
• Sorting (ordering) Data in Data Frames by Attribute(s)
• Editing Data Frames, The str() Function
• Type Conversion (Coercion), The summary() Function
• Checking an Object's Type
• 5. Extending R
• The Base R Packages, Loading Packages
• What is the Difference between Package and Library?
• Extending R, The CRAN Web Site
• Extending R in R GUI
• Extending R in RStudio
• Installing and Removing Packages from Command-Line
• 6. Read-Write and Import-Export Operations in R
• Reading Data from a File into a Vector
• Example of Reading Data from a File into A Vector
• Writing Data to a File
• Example of Writing Data to a File
• Reading Data into A Data Frame
• Writing CSV Files
• Importing Data into R
• Exporting Data from R
• 7. Statistical Computing Features in R
• Statistical Computing Features, Descriptive Statistics
• Basic Statistical Functions
• Examples of Using Basic Statistical Functions
• Non-uniformity of a Probability Distribution
• Writing Your Own skew and kurtosis Functions
• Generating Normally and Uniformly Distributed Random Numbers
• Using the summary() Function
• Math Functions Used in Data Analysis
• Examples of Using Math Functions, Correlations and Correlation Example
• Testing Correlation Coefficient for Significance
• The cor.test() Function, The cor.test() Example, Regression Analysis
• Types of Regression, Simple Linear Regression Model
• .
• Least-Squares Method (LSM)
• LSM Assumptions
• Fitting Linear Regression Models in R
• Example of Using lm()
• Confidence Intervals for Model Parameters
• Example of Using lm() with a Data Frame
• Regression Models in Excel
• Multiple Regression Analysis
• 8. Data Manipulation and Transformation in R
• Applying Functions to Matrices and Data Frames
• The apply() Function, Using apply(), Using apply() with a User-Defined Function
• apply() Variants, Using tapply(), Adding a Column to a Data Frame
• Dropping A Column in a Data Frame, The attach() and detach() Functions
• Sampling, Using sample() for Generating Labels
• Set Operations, Example of Using Set Operations
• The dplyr Package, Object Masking (Shadowing) Considerations
• Getting More Information on dplyr in RStudio
• The search() or searchpaths() Functions
• Handling Large Data Sets in R with the data.table Package
• The fread() and fwrite() functions from the data.table Package
• Using the Data Table Structure
• 9. Data Visualization in R
• Data Visualization, Data Visualization in R
• The ggplot2 Data Visualization Package
• Creating Bar Plots in R, Creating Horizontal Bar Plots
• Using barplot() with Matrices, Using barplot() with Matrices Example
• Customizing Plots, Histograms in R
• Building Histograms with hist(), Example of using hist()
• Pie Charts in R, Examples of using pie()
• Generic X-Y Plotting, Examples of the plot() function
• Dot Plots in R, Saving Your Work
• Supported Export Options, Plots in RStudio
• Saving a Plot as an Image
• 0. Using R Efficiently
• Object Memory Allocation Considerations
• Garbage Collection, Finding Out About Loaded Packages
• Using the conflicts() Function
• Getting Information About the Object Source Package with the pryr Package
• Using the where() Function from the pryr Package
• Timing Your Code, Timing Your Code with system.time()
• Sleeping a Program
• Handling Large Data Sets in R with the data.table Package
• Passing System-Level Parameters to R
• Lab Exercises
• Lab 1. Getting Started with R
• Lab 2. Learning the R Type System and Structures
• Lab 3. Read and Write Operations in R
• Lab 4. Data Import and Export in R
• Lab 5. k-Nearest Neighbors Algorithm
• Lab 6. Creating Your Own Statistical Functions
• Lab 7. Simple Linear Regression
• Lab 8. Monte-Carlo Simulation (Method)
• Lab 9. Data Processing with R
• Lab 10. Using R Graphics Package
• Lab 11. Using R Efficiently