R Tutorial & Roadmap| Master R Programming with Ease

Welcome to of R programming Tutorial, where data analysis and visualization come to life. If you’re looking to unlock the full potential of your data, you’ve come to the right place. This comprehensive guide will take you on a journey through the essentials of R, from the basics to advanced techniques that will transform you into a data wizard.

Our R Programming Tutorial caters to both newcomers and seasoned professionals. This complimentary R Tutorial enriches your understanding from the fundamentals to the complexities of the R programming language.

You’ll receive a thorough rundown, including an introduction, capabilities, setup procedures, variables, data types, operators, conditional statements, arrays, data management, visual representation, and statistical analysis within R programming.

Introduction to R Programming

R is a language and environment for statistical computing and graphics. It’s an open-source treasure trove that offers a variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more.

What is R Programming?

R stands as a versatile programming language and software environment, highly regarded in the realms of statistical computation and data scrutiny. The language was conceived by Ross Ihaka and Robert Gentleman at the University of Auckland, situated in New Zealand.

As an open-source language, R is accessible across prevalent operating systems such as Windows, Linux, and macOS. Typically accompanied by a command-line interface, it boasts an extensive array of packages tailored for diverse tasks. R’s interpreted nature facilitates both procedural and object-oriented programming paradigms.

Hello World Program in R Language

Certainly! Here’s how you can display the classic “Hello, World!” message in R using the `print` function:

# This is a simple R program to print “Hello, World!”
# Code
Print(“Hello, World!”)

Output

Hello World!

This code snippet demonstrates the use of the `print` function to output text to the console in R. It’s a fundamental concept that’s essential for beginners to understand as they start their journey in R programming.

Why Learn R?

In a world where data is king, R equips you with the scepter to rule your domain. Whether you’re a student, professional, or enthusiast, understanding R is like having a superpower in today’s data-driven landscape.

R programming language stands as a premier toolkit for data science, offering a suite of capabilities for data analysis, visualization, and machine learning.

It furnishes a plethora of statistical methods, including tests, clustering, and dimensionality reduction. Crafting graphs, such as pie charts, histograms, and box plots, is straightforward.

R’s open-source nature ensures it is freely available, backed by a robust community, and compatible with all major operating systems. Its extensive package ecosystem provides a rich library of functions to tackle a wide array of challenges.

Getting Started with R

Embarking on your R journey is as simple as installing the R software and its powerful companion, RStudio. These tools provide a user-friendly interface and a robust platform for all your programming needs.

R Roadmap & Tutorial Overview

¡》The Basics: Syntax and Structure

R’s syntax may seem daunting at first, but fear not. With a bit of practice, you’ll be slicing and dicing data sets with the best of them. We’ll cover variables, data types, and structures to give you a solid foundation.

¡¡》Data Manipulation Mastery

Data wrangling is a critical skill in R. You’ll learn to use packages like `dplyr` and `tidyr` to transform and tidy your data, making it ready for analysis.

¡V》Statistical Analysis and Hypothesis Testing

R shines in statistical analysis. You’ll explore how to perform hypothesis testing, leverage probability distributions, and conduct regression analysis to draw meaningful insights from your data.

V》Advanced Topics: Machine Learning and Beyond

Once you’ve got the basics down, it’s time to level up. We’ll delve into machine learning algorithms, time series forecasting, and even bioinformatics applications in R.

V¡》Creating Stunning Visualizations

They say a picture is worth a thousand words, and in R, this couldn’t be truer. You’ll learn to create compelling visualizations that tell stories with your data using `ggplot2` and other visualization packages.

Prerequisites for R Programming

Embarking on the R programming adventure is accessible to all, regardless of background. Here’s what can smooth your path:

Essentials:

Familiarity with basic computer use
A knack for logical reasoning and solving puzzles

Advantageous:

Experience with any programming language
A grasp of math and statistics
Understanding of data analysis fundamentals

Key to Success:

Consistent practice, a committed mind-set, and a systematic learning strategy are the true cornerstones of mastering R, or any new language for that matter. Dive in, and let the data dance begin!.

How to Install R

Go to https://cloud.r-project.org/ and download the latest version of R for Windows, Mac or Linux.

When you have downloaded and installed R, you can run R on your Command prompt or any IDE.

Applications of R Programming Language

R programming language is a versatile tool that finds its applications across various sectors:

¡》Industry Applications:

Academia: For research, statistical analysis, and educational purposes.
Government: In policy analysis, economic forecasting, and public administration.
Insurance: To model risks, calculate premiums, and analyze claims data.
Retail: For customer analytics, inventory management, and sales forecasting.
Energy: In analyzing consumption patterns and predicting future energy needs.
Media: To understand audience preferences and tailor content accordingly.
Technology & Electronics: For product development, quality testing, and market analysis.

¡¡》Data Management:

R simplifies the process of importing, cleaning, and analyzing data, making it an invaluable asset for data-driven decision-making.

Data Science Libraries: R’s rich ecosystem includes libraries like `Dplyr` for data manipulation, `Ggplot2` for advanced graphics, `Shiny` for interactive web applications, `Lubridate` for dealing with dates and times, `Knitr` for dynamic report generation, `Caret` for machine learning, and `Janitor` for data cleaning.

These applications highlight R’s flexibility and its capacity to empower professionals across different fields with the skills to handle data effectively.

Complete R Tutorial & Roadmap

1. Basics

Introduction to R Programming Language
Interesting Facts about 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

2. Fundamentals of R

Basic Syntax
Comments
Operators
Keywords
Data Types

3. Variables

Introduction to Variables
Scope of Variable
Dynamic Scoping
Lexical Scoping
Lexical Scoping vs Dynamic Scoping

4. Input and Output

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

5. Decision Making

Decision Making – if, if-else, if-else-if ladder, nested if-else, and switch
If statement
If-else statement
Switch case

6. Control Flow

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

7. Functions

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

8. Data Structures

Introduction to Data Structures

9. 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

10. 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

>>> More Functions on Vectors

11. 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

>>> More Functions on Lists

12. 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

13. 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

14. 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

>>> More Functions on Factors

15. 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

16. 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

17. Error Handling

Introduction to Error Handling
Condition Handling
Debugging in R Programming

18. File Handling

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

19. 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

20. 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

21. 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

22. 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

23. 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

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Conclusion:

As you continue your R journey, remember that practice makes perfect. Dive into projects, participate in online communities, and never stop exploring. With R, the possibilities are endless, and your path to becoming a data maestro is clear.

Embark on this adventure with enthusiasm, and watch as the doors to a new realm of possibilities swing wide open. Welcome to the R community – your journey begins now.

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