Course Code: 21360

R Programming from the Ground Up Training

Class Dates:
2 Days
Class Time:
Instructor-Led Training, Virtual Instructor-Led Training


  • Course Overview
  • This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.

    High octane introduction to R programming
    Learning about R data structures
    Working with R functions
    Statistical data analysis with R
  • Audience
  • Business Analysts, Technical Managers, and Programmers


  • Participants should have the general knowledge of statistics and programming

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