Course Catalog  > Programming Languages & Software Dev
WA2711

R Programming from the Ground Up Training

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.
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Course Details

Duration

2 days

Prerequisites

Participants should have the general knowledge of statistics and programming

Target Audience

  • Business Analysts
  • Technical Managers
  • Programmers

Skills Gained

  • R data structures
  • R functions
  • Statistical data analysis with R
Course Outline
  • 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 Example
    • 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
  • 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
  • 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
    • Saving Your Workspace in RStudio
    • Saving Your Workspace in R GUI
    • Loading Your Workspace
    • Diverting Output to a File
    • Batch (Unattended) Processing
    • Controlling Global Options
  • 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
  • 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
  • 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
  • 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 Distributed Random Numbers
    • Generating Uniformly Distributed Random Numbers
    • Using the summary() Function
    • Math Functions Used in Data Analysis
    • Examples of Using Math Functions
    • Correlations
    • 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
  • 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
  • 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
  • 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()
    • 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