At TenBytes Institute, we recognize the value of providing our students with the resources necessary to be successful in the data science sector. Considering this, we offer comprehensive R programming training.

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R PROGRAMMING COURSE

R-programming

R Programming Course

We provide R programming classes for people of all ability levels, from novices who have never programmed before to seasoned pros who want to improve their data analysis skills.Our teachers, who are seasoned professionals in the area, offer a disciplined classroom setting where students can put their knowledge to use through hands-on activities and real-world case studies. They assist students in making effective use of R’s extensive package library to solve challenging data analysis problems.

R Programming Course Details

  • Course Duration
  • 4 Months
  • Fee
  • PKR 60,000

Course Schedule

  • Classes

    Classes will be held four times a week.

  • Three time slots available

    Morning 10AM To 12PM
    Evening 2PM to 4 PM
    Night 6PM to 8PM.

  • Holiday

    Holiday classes provided for uninterrupted learning

Course Content

  • R Programming Course Outline

Course Outlines

1. WHAT IS R (i)
  • 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
2. WHAT IS R (ii)
  • System Date and Time
  • R Objects and Workspace
  • Printing Objects
  • Arithmetic Operators
  • Logical Operators
  • Operations
  • User-defined Functions
  • Control Statements
  • Conditional Execution
  • Repetitive Execution
  • Repetitive execution
  • Built-in Functions
  • Summary
 
3. 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
4. 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
5. 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
 
6. R TYPE SYSTEM AND STRUCTURES
  • 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
 
7. Projects CSS3
  • 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
 
8. 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
9. 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
10. 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
11. STATISTICAL COMPUTING FEATURES IN R
  • 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
12. 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
13. DATA MANIPULATION AND TRANSFORMATION IN R
  • 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
14. DATA VISUALIZATION IN R (i)
  • 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()
15. DATA VISUALIZATION IN R (ii)
  • 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
 
16. 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
 
17. 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
  •  
 
18. Additional Topics Included in this project.
  • How to start freelancing?
  • How to start our business?
  • How to create CV for job?
  • Reference for job
  • Totally practical this course

Why Choose Our Course?

✅ Expert Instructors: Learn from industry professionals with extensive experience in R programming .

✅ Hands-on Practical Experience: Gain real-world skills through interactive projects and case studies.

✅ Updated Curriculum: Stay up-to-date with the latest trends, tools, and techniques in R programming 

✅ Flexible Class Schedule: Morning and evening time slots to suit your convenience.

✅ Weekend Classes: Additional classes available on weekends to ensure comprehensive learning.

✅ Career Opportunities: Enhance your job prospects with sought-after R programming  skills.

🏆 Course Completion and Benefits:

Upon successful completion of the course, you will receive a certificate of achievement. Additionally, you will gain:

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