VTEC Training

VTEC Training

Portland,
ME

Class Dates:

1/1/0001

Length:

2 Days

Cost:

$1360.00

Class Time:

Technology:

Developer

Delivery:

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.

**TOPICS**

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

- 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