Introduction to R Programming
E-Learning
Description
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.
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.
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.
Business Analysts, Technical Managers, and Programmers
| Lesson Id | Title | Description |
|---|---|---|
| 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 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 |
| 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 • Summary |
| 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 • 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 |
| 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 • Summary |
| 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 • Summary |
| 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 • Summary |
| 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 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 |
| 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 • Summary |
| 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 • Summary |
| 10 | 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 • Summary |
| 11 | 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 |
Self-Paced
Free
This course includes: :
Full lifetime access