R Programming Tutorial

• Introduction to R Programming
• Applications of R
• Advantages and Limitations of R
• Installing R
• Installing RStudio
• RStudio Interface
• R Console and Scripts
• Creating and Running an R File
• R Packages and Libraries
• R Help and Documentation

All 10 lessons →

• Basic Syntax in R
• Comments in R
• Keywords in R
• Variables in R
• Assignment Operators in R
• Data Types in R
• Numeric Data Type
• Integer Data Type
• Logical Data Type
• Character Data Type
• Complex Data Type
• Factors in R
• Type Conversion in R
• Printing Output in R
• Reading Input in R
• Operators in R
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• Special Operators

All 21 lessons →

• Data Structures in R
• Vectors in R
• Creating Vectors
• Vector Indexing
• Vector Operations
• Vector Recycling
• Lists in R
• List Indexing
• Nested Lists
• Matrices in R
• Matrix Indexing
• Matrix Operations
• Arrays in R
• Array Indexing
• Data Frames in R
• Factors and Categorical Data

All 16 lessons →

• Creating Data Frames
• Accessing Data Frame Columns
• Filtering Data Frames
• Sorting Data Frames
• Adding and Removing Columns
• Merging Data Frames
• Joining Data Frames
• Reshaping Data Frames
• Tibbles

All 9 lessons →

• Decision Making in R
• if Statement in R
• if-else Statement
• Nested if
• ifelse() Function
• switch() Function
• for Loop in R
• while Loop in R
• repeat Loop
• break in R
• next in R
• Nested Loops

All 12 lessons →

• Functions in R
• Creating User-Defined Functions
• Function Arguments
• Default Arguments
• Return Values
• Anonymous Functions
• Recursive Functions
• Higher-Order Functions
• Closures in R
• Lazy Evaluation
• apply() Family
• lapply()
• sapply()
• vapply()
• tapply()
• mapply()

All 16 lessons →

• Strings in R
• String Concatenation
• String Length
• Substring in R
• String Replacement
• String Splitting
• String Formatting
• Regular Expressions in R
• grep() and grepl()
• sub() and gsub()
• Stringr Package

All 11 lessons →

• Reading CSV Files in R
• Writing CSV Files in R
• Reading Excel Files in R
• Writing Excel Files in R
• Reading JSON in R
• Writing JSON in R
• Reading XML in R
• Working with Text Files
• Working Directory in R
• Importing Data from Databases
• Data Import and Export Best Practices

All 11 lessons →

• Data Manipulation in R
• dplyr Introduction
• select()
• filter()
• mutate()
• arrange()
• summarise()
• group_by()
• across()
• case_when()
• Joins with dplyr
• tidyr Introduction
• pivot_longer()
• pivot_wider()
• separate() and unite()
• Missing Data Handling
• NA, NaN and NULL
• Data Cleaning in R

All 18 lessons →

• Data Visualization in R
• Base R Plotting
• Bar Chart in R
• Line Chart in R
• Histogram in R
• Pie Chart in R
• Scatter Plot in R
• Box Plot in R
• Heatmap in R
• ggplot2 Introduction
• ggplot2 Aesthetics
• ggplot2 Geoms
• ggplot2 Facets
• ggplot2 Themes
• ggplot2 Customization
• Interactive Visualization with Plotly
• Visualization Best Practices

All 17 lessons →

• Statistics with R
• Mean, Median and Mode
• Measures of Dispersion
• Variance and Standard Deviation
• Quantiles and Percentiles
• Correlation in R
• Covariance in R
• Probability in R
• Conditional Probability
• Probability Distributions
• Normal Distribution
• Binomial Distribution
• Poisson Distribution
• Central Limit Theorem in R
• Confidence Intervals
• Hypothesis Testing
• t-Test in R
• Chi-Square Test
• ANOVA in R
• Non-Parametric Tests

All 20 lessons →

• Linear Regression in R
• Multiple Linear Regression
• Polynomial Regression
• Logistic Regression
• Model Summary and Interpretation
• Train-Test Split
• Model Evaluation
• Cross Validation
• Feature Selection
• Regularization
• Time Series Introduction
• ARIMA in R
• Forecasting in R

All 13 lessons →

• Machine Learning with R
• Data Preprocessing
• Train and Test Data
• K-Nearest Neighbors
• Decision Trees
• Random Forest
• Support Vector Machines
• Naive Bayes
• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis
• Association Rule Mining
• Model Tuning
• Machine Learning Evaluation Metrics

All 14 lessons →

• OOP in R
• S3 Classes
• S3 Methods
• S4 Classes
• S4 Methods
• Reference Classes
• R6 Classes
• Encapsulation in R
• Polymorphism in R

All 9 lessons →

• Error Handling in R
• warnings()
• stop()
• try()
• tryCatch()
• withCallingHandlers()
• Debugging R Functions
• debug() and browser()
• traceback()
• Rprof Profiling

All 10 lessons →

• R Packages
• Installing Packages
• Loading Packages
• Creating an R Package
• DESCRIPTION File
• NAMESPACE File
• Package Documentation
• renv for Reproducible Environments
• Reproducible Research with R
• R Markdown
• Quarto with R

All 11 lessons →

• Shiny Introduction
• Shiny UI and Server
• Reactive Programming
• Reactive Expressions
• Shiny Inputs and Outputs
• Shiny Modules
• Deploying Shiny Apps
• Building Dashboards with Shiny

All 8 lessons →

• Data Science with R
• Data Wrangling Workflow
• Exploratory Data Analysis
• Feature Engineering
• Working with Large Datasets
• Database Connectivity with R
• Spark with R
• Parallel Computing in R

All 8 lessons →

• R Basic Programs
• R Vector Programs
• R Matrix Programs
• R Data Frame Programs
• R Function Exercises
• R String Programs
• R File Handling Programs
• R Data Manipulation Exercises
• R Visualization Exercises
• R Statistics Exercises
• R Machine Learning Exercises
• R Beginner Projects
• R Data Analysis Projects
• R Visualization Projects
• R Machine Learning Projects
• R Shiny Projects
• R Interview Questions
• R Cheat Sheet

All 18 lessons →