R Tutorial | Learn R Programming Language

R is an interpreted programming language widely used for statistical computing, data analysis and visualization. R language is open-source with large community support. R provides structured approach to data manipulation, along with decent libraries and packages like Dplyr, Ggplot2, shiny, Janitor and more.

Hello World Program in R Language

Here is an example of the first Hello World program in R Programming Language. To print in R language you just need to use a Print function.

# Code
print("Hello World!")

Output

Hello World! 

R-Basics

• Introduction to R Programming Language
• Interesting Facts about R
• R vs Python
• How to Install R Studio on Windows and Linux?
• Creation and Execution of R File in R Studio
• Hello World in R Programming

Fundamentals of R

• Basic Syntax
• Comments
• Operators
• Keywords
• Data Types

Variables

• Variables
• Scope of Variable
• Dynamic Scoping
• Lexical Scoping
• Lexical Scoping vs Dynamic Scoping

Input and Output

• Taking Input from User
• Printing Output of R Program
• Print the Argument to the Screen – print() Function

Data Structures

Introduction to Data Structures

Vectors

• Introduction to Vectors
• Operations on Vectors
• Append Operation on Vectors
• Dot Product of Vectors
• Types of Vectors
• Assigning Vectors 
• Length of Vectors – length() Function
• Creating a Vector of sequenced elements – seq() Function
• Get Min and Max element of a Vector – range() Function
• Formatting Numbers and Strings – format() Function
• Replace the Elements of a Vector – replace() Function
• Sorting of a Vector – sort() Function
• Convert elements of a Vector to Strings – toString() Function
• Extracting Substrings from a Character Vector – substring() Function

>>> More Functions on Vectors

Lists

• Introduction to Lists
• Two Dimensional List
• Operations on Lists
• List of Vectors
• List of Dataframes
• Named List
• Check if the Object is a List – is.list() Function
• Convert an Object to List – as.list() Function
• Apply a Function over a List of elements – lapply() Function
• Operations on Multiple Lists simultaneously – mapply() Function

>>> More Functions on Lists

Matrices

• Introduction to Matrices
• Create Matrix from Vectors
• Operations on Matrices
• Matrix Multiplication
• Algebraic Operations on a Matrix
• Combining Matrices
• Matrix Transpose
• Inverse of Matrix
• Working with Sparse Matrices
• Check if the Object is a Matrix – is.matrix() Function
• Convert an Object into a Matrix – as.matrix() Function
• Get or Set Dimensions of a Matrix – dim() Function
• Calculate Cumulative Sum of a Numeric Object – cumsum() Function
• Compute the Sum of Rows of a Matrix or Array – rowSums Function

>>> More Functions on Matrices

DataFrames

• Introduction to Data Frames
• Matrix vs Dataframe
• DataFrame Operations
• DataFrame Manipulation
• Joining of Dataframes
• The Factor Issue in a DataFrame
• Data Reshaping
• Creating a Data Frame from Vectors
• Data Wrangling – Data Transformation
• Data Wrangling – Working with Tibbles
• Melting and Casting
• Subsetting of DataFrames
• Handling Missing Values
• Convert an Object to Data Frame – as.data.frame() Function
• Get the number of columns of an Object – ncol() Function
• Get the number of rows of an Object – nrow() Function
• Get Addition of the Objects passed as Arguments – sum() Function
• Create Subsets of a Data frame – subset() Function

>>> More Functions on DataFrames

Arrays

• Introduction to Arrays
• Multidimensional Array
• Array Operations
• Sorting of Arrays
• Convert values of an Object to Logical Vector – as.logical() Function
• Performing different Operations on Two Arrays – outer() Function
• Intersection of Two Objects – intersect() Function
• Get Exclusive Elements between Two Objects – setdiff() Function

>>> More Functions on Arrays

Factors

• Introduction to Factors
• Level Ordering of Factors
• Convert Factor to Numeric and Numeric to Factor
• Check if a Factor is an Ordered Factor – is.ordered() Function
• Convert Unordered Factor to Ordered Factor  – as.ordered() Function
• Checking if the Object is a Factor – is.factor() Function
• Convert a Vector into Factor – as.factor() Function

>>> More Functions on Factors

Strings

• Introduction to Strings
• Working with Text
• String Manipulation
• Concatenate Two Strings
• String Matching
• How to find a SubString?
• Finding the length of string – nchar() method
• Adding elements in a vector – append() method
• Convert string from Lowercase to Uppercase – toupper() function
• Convert String from Uppercase to Lowercase – tolower() method
• Splitting Strings – strsplit() method
• Print a Formatted string – sprintf() Function

>>> More Functions on Strings

Decision Making

• Decision Making
• if statement
• if-else statement
• Switch case

Control Flow

• Introduction to Control Statements
• Loops (for, while, repeat)
• For loop
• while loop
• Repeat loop
• goto statement
• Break and Next statements
• Next Statement

Functions

• Introduction to Functions
• Function Arguments
• Types of Functions
• Recursive Functions
• Conversion Functions

Object Oriented Programming

• Introduction to Object-Oriented Programming
• Classes
• Objects
• Encapsulation
• Polymorphism
• Inheritance
• Abstraction
• Looping over Objects
• Creating, Listing, and Deleting Objects in Memory
• S3 class
• Explicit Coercion
• R6 Classes
• Getting attributes of Objects – attributes() and attr() Function
• Get or Set names of Elements of an Object – names() Function
• Get the Minimum element of an Object – min() Function
• Get the Maximum element of an Object – max() Function

>>> More Functions on R Objects

File Handling

• Introduction to File Handling
• Reading Files
• Writing to Files
• Read Lines from a File – readLines() Function
• Working with Binary Files

Packages in R

• Introduction to Packages
• dplyr Package
• ggplot2 package
• Grid and Lattice Packages
• Shiny Package
• tidyr Package
• What Are the Tidyverse Packages?
• Data Munging

Data Interfaces

• Data Handling
• Importing Data in R Script
• How To Import Data from a File?
• Exporting Data from scripts 
• Working with CSV files 
• Working with XML Files
• Working with Excel Files
• Working with JSON Files 
• Reading Tabular Data from files
• Working with Databases
• Database Connectivity
• Manipulate Data Frames Using SQL

Error Handling

• Introduction to Error Handling
• Condition Handling
• Debugging in R Programming

Data Visualization

• Graph Plotting
• Graphical Models
• Data Visualization
• Charts and Graphs
• Add Titles to a Graph
• Adding Colors to Charts
• Adding Text to Plots
• Adding axis to a Plot
• Set or View the Graphics Palette
• Plotting of Data using Generic plots
• Bar Charts
• Line Graphs
• Adding Straight Lines to a Plot
• Addition of Lines to a Plot
• Histograms
• Pie Charts
• Scatter plots
• Create Dot Charts
• Boxplots in R Language
• Stratified Boxplot
• Create a Heatmap
• Pareto Chart
• Waffle Chart
• Quantile-Quantile Plot
• Creating 3D Plots
• Describe Parts of a Chart in Graphical Form
• Principal Component Analysis
• Social Network Analysis

Statistics

• Introduction to Statistics
• Calculate the Mean, Median, and Mode
• Calculate the Average, Variance, and Standard Deviation
• Homogeneity of Variance Test
• Covariance and Correlation
• Correlation Matrix
• Visualize correlation matrix using correlogram
• Distance Matrix by GPU
• Descriptive Analysis
• Normal Distribution
• Binomial Distribution
• Compute the Negative Binomial Density
• Poisson Functions
• ANOVA Test
• MANOVA Test
• Naive Bayes Classifier
• K-NN Classifier
• Central Tendency
• Variability
• Skewness and Kurtosis
• Absolute and Relative Frequency
• Permutation Hypothesis Test
• AB Testing
• Completely Randomized Design
• Randomized Block Design
• Bartlett’s Test
• Tree Entropy
• Tukey’s Five-number Summary
• Compute Summary Statistics of Subsets
• Hypothesis Testing
• Bootstrapping
• Time Series Analysis
• T-Test Approach

Machine Learning with R

• Introduction to Machine Learning
• Setting up Environment for Machine Learning
• Supervised and Unsupervised Learning
• Classification
• Regression and its Types
• Regression Analysis
• Decision Tree
• Random Forest Approach
• Root-Mean-Square Error
• Clustering
• Hierarchical Clustering
• DBScan Clustering
• Deep Learning
• Building a Simple Neural Network
• How Neural Networks are used for Regression?
• Multi Layered Neural Networks
• Survival Analysis
• Stem and Leaf Plots

Why Use R Programming Language?

R programming language is a best resource for data science, data analysis, data visualization and machine learning. R provides various statistical techniques like statistical tests, clustering and data reduction. Graph making is easy eg. pie chart, histogram, box, plot, etc. R is totally free and open-source Programming language. The community support with the R language is very large and it works on all OS. R programming comes with many packages (libraries of functions) to solve various problems.

Applications of R Programming Language

Some of the important applications of R Programming Language are listed below:

• R is used in wide range of industries for example academics, government, insurance, retail, energy, media, technology, and electronics.
• R helps in importing and cleaning data and data analysis.
• R is used in data science. R language provides us many libraries for data science e.g. Dplyr, Ggplot2, shiny, Lubridate, Knitr, Caret, Janitor.

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