Information Technology
Introduction to R Programming
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.
2 Days
16 Hrs
11 Lessons
Course Objectives
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.
Agenda
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- • 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
- • Summary
- 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
FREE
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Course Type: Instructor Led