R Tutorial

• Introduction to R Programming Language
• R vs Python
• Environments in R Programming
• Introduction to R Studio
• How to Install R Studio on Windows and Linux?
• Creation and Execution of R File in R Studio
• Clear the Console and the Environment in R Studio
• Hello World in R Programming

Fundamentals of R

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

Variables

• Introduction to 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

Decision Making

• Decision Making - if, if-else, if-else-if ladder, nested if-else, and switch
• 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

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

Vectors

• Introduction to Vectors
• Operations on Vectors
• Append Operation on Vectors
• Dot Product of Vectors
• Types of Vectors
• Assigning Vectors 
• Getting and Setting Length of the Vectors - length() Function
• Creating a Vector of sequenced elements - seq() Function
• Get the Minimum and Maximum 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

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
• Check if an Object of the Specified Name is Defined or not - exists() Function
• Apply a Function over a List of elements - lapply() Function
• Performing Operations on Multiple Lists simultaneously - mapply() Function

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

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

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 an Unordered Factor to an Ordered Factor  - as.ordered() Function
• Checking if the Object is a Factor - is.factor() Function
• Convert a Vector into Factor - as.factor() Function

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

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

Error Handling

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

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

Data Visualization

• Graph Plotting
• Graphical Models
• Plotting Graphs using Two Dimensional List
• 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 One Dimensional Scatterplots
• Create a Plot Matrix of Scatterplots
• Create Dot Charts
• Boxplots in R Language
• Stratified Boxplot
• Create a Heatmap
• Pareto Chart
• Waffle Chart
• Draw a 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
• 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

Introduction to Packages in R

In R, a package is a collection of functions, data sets, and documentation bundled together. Packages enhance the capability of R by adding new functions, methods, and classes. They allow for the organized and modular addition of features to the R language.

Why use packages?

1. Functionality: Packages can contain a variety of functionalities ranging from advanced plotting to sophisticated statistical tools. By using packages, you can extend the base functionalities of R.

2. Collaboration: The R community actively develops and maintains numerous packages. These are contributions from statisticians, data scientists, and researchers worldwide.

3. Standardization: Packages offer standardized solutions to recurring tasks. By adhering to package guidelines, developers ensure a level of consistency and reliability in their functions.

Commonly used packages:

• ggplot2: Advanced plotting.
• dplyr: Data manipulation.
• tidyr: Data tidying.
• stringr: String manipulation.
• lubridate: Date and time manipulation.
• caret: Classification and regression training for machine learning.
• shiny: Building interactive web applications.
• data.table: Advanced data manipulation.

Installing a package:

To install a package in R, use the install.packages() function. For instance, to install the ggplot2 package, you would use:

install.packages("ggplot2")

Loading a package:

Once a package is installed, it has to be loaded to be used in the current R session. To load a package, use the library() function:

library(ggplot2)

Note: The package only needs to be installed once, but it needs to be loaded using library() in each new session where you want to use it.

Checking installed packages:

You can see a list of all installed packages using:

installed.packages()[,"Package"]

Removing a package:

If you want to remove a package, you can use the remove.packages() function:

remove.packages("ggplot2")

Updating packages:

Packages are often updated with new functionalities, bug fixes, or improvements. You can update your installed packages with:

update.packages()

Conclusion:

Packages are an integral part of the R ecosystem. They offer specialized tools and functionalities to help you conduct a wide range of tasks, from data visualization and manipulation to statistical analysis and machine learning. Familiarizing yourself with the process of installing, loading, and managing packages is essential for any R user.

1. How to install and load packages in R:

   • Overview: Introduce the process of installing and loading R packages.

   • Code:

     # Installing a package from CRAN
     install.packages("ggplot2")
     
     # Loading the installed package
     library(ggplot2)
     

2. Overview of popular R packages:

   • Overview: Explore some popular R packages and their functionalities.

   • Code: Provide examples of packages like dplyr, tidyr, and ggplot2 with brief explanations.

     # Example: Using dplyr for data manipulation
     library(dplyr)
     

3. Using CRAN packages in R programming:

   • Overview: Explain how to use packages available on the Comprehensive R Archive Network (CRAN).

   • Code:

     # Installing and loading a CRAN package
     install.packages("tidyverse")
     library(tidyverse)
     

4. Introduction to devtools for R packages:

   • Overview: Introduce the devtools package for package development tasks.

   • Code:

     # Installing devtools
     install.packages("devtools")
     
     # Loading devtools
     library(devtools)