R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team.
The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python or FORTRAN languages for efficiency.
R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac.
R is free software distributed under a GNU-style copy left, and an official part of the GNU project called GNU S.

Evolution of R

R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. R made its first appearance in 1993.
• A large group of individuals has contributed to R by sending code and bug reports.
• Since mid-1997 there has been a core group (the “R Core Team”) who can modify the R source code archive.

Features of R

As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R −
• R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
• R has an effective data handling and storage facility,
• R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
• R provides a large, coherent and integrated collection of tools for data analysis.
• R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.
As a conclusion, R is world’s most widely used statistics programming language. It’s the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission critical business applications. This tutorial will teach you R programming along with suitable examples in simple and easy steps.

R – Environment Setup

Local Environment Setup

If you are still willing to set up your environment for R, you can follow the steps given below.
Windows Installation
You can download the Windows installer version of R from R-3.2.2 for Windows (32/64 bit) and save it in a local directory.
As it is a Windows installer (.exe) with a name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is 32-bit version, it installs the 32-bit version. But if your windows is 64-bit, then it installs both the 32-bit and 64-bit versions.
After installation you can locate the icon to run the Program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.
Linux Installation
R is available as a binary for many versions of Linux at the location R Binaries.
The instruction to install Linux varies from flavor to flavor. These steps are mentioned under each type of Linux version in the mentioned link. However, if you are in a hurry, then you can use yum command to install R as follows −
$ yum install R
Above command will install core functionality of R programming along with standard packages, still you need additional package, then you can launch R prompt as follows −
$ R
R version 3.2.0 (2015-04-16) — “Full of Ingredients”
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type ‘license()’ or ‘licence()’ for distribution details.

R is a collaborative project with many contributors.
Type ‘contributors()’ for more information and
‘citation()’ on how to cite R or R packages in publications.

Type ‘demo()’ for some demos, ‘help()’ for on-line help, or
‘help.start()’ for an HTML browser interface to help.
Type ‘q()’ to quit R.
>
Now you can use install command at R prompt to install the required package. For example, the following command will install plotrix package which is required for 3D charts.
> install.packages(“plotrix”)

R – Basic Syntax

As a convention, we will start learning R programming by writing a “Hello, World!” program. Depending on the needs, you can program either at R command prompt or you can use an R script file to write your program. Let’s check both one by one.

R Command Prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
$ R
This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

myString <- "Hello, World!"
> print ( myString)
[1] "Hello, World!"

Here first statement defines a string variable myString, where we assign a string “Hello, World!” and then next statement print() is being used to print the value stored in variable myString.

R Script File

Usually, you will do your programming by writing your programs in script files and then you execute those scripts at your command prompt with the help of R interpreter called Rscript. So let’s start with writing following code in a text file called test.R as under −

# My first program in R Programming
myString <- "Hello, World!"
print ( myString)

Save the above code in a file test.R and execute it at Linux command prompt as given below. Even if you are using Windows or other system, syntax will remain same.
$ Rscript test.R
When we run the above program, it produces the following result.
[1] “Hello, World!”

Comments

Comments are like helping text in your R program and they are ignored by the interpreter while executing your actual program. Single comment is written using # in the beginning of the statement as follows −
# My first program in R Programming
R does not support multi-line comments but you can perform a trick which is something as follows −

if(FALSE) {
"This is a demo for multi-line comments and it should be put inside either a 
single OR double quote"
}

myString <- "Hello, World!"
print ( myString)
[1] "Hello, World!"

Though above comments will be executed by R interpreter, they will not interfere with your actual program. You should put such comments inside, either single or double quote.

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What you will Learn in this Course?

Introduction to Data Science
• Need for Data Science
• What is Data Science
• Life Cycle of Data Science
• Applications of Data Science
• Introduction to Big Data
• Introduction to Machine Learning
• Introduction to Deep Learning
• Introduction to R&R-Studio
• Project Based Data Science
Introduction to R
• Introduction to R
• Data Exploration
• Operators in R
• Inbuilt Functions in R
• Flow Control Statements & User Defined Functions
• Data Structures in R
Data Manipulation
• Need for Data Manipulation
• Introduction to dplyr package
• Select (), filter(), mutate(), sample_n(), sample_frac() & count() functions
• Getting summarized results with the summarise() function,
• Combining different functions with the pipe operator
• Implementing sql like operations with sqldf()
Visualization of Data
• Loading different types of datasets in R
• Arranging the data
• Plotting the graphs
Introduction to Statistics
• Types of Data
• Probability
• Correlation and Co-variance
• Hypothesis Testing
• Standardization and Normalization
Introduction to Machine Learning
• What is Machine Learning?
• Machine Learning Use-Cases
• Machine Learning Process Flow
• Machine Learning Categories
• Supervised Learning algorithm: Linear Regression and Logistic Regression
Logistic Regression
• Intro to Logistic Regression
• Simple Logistic Regression in R
• Multiple Logistic Regression in R
• Confusion Matrix
• ROC Curve
Classification Techniques
• What are classification and its use cases?
• What is Decision Tree?
• Algorithm for Decision Tree Induction
• Creating a Perfect Decision Tree
• Confusion Matrix
• What is Random Forest?
• What is Naive Bayes?
• Support Vector Machine: Classification
Decision Tree
• Decision Tree in R
• Information Gain
• Gini Index
• Pruning
Recommender Engines
• What is Association Rules & its use cases?
• What is Recommendation Engine & it’s working?
• Types of Recommendations
• User-Based Recommendation
• Item-Based Recommendation
• Difference: User-Based and Item-Based Recommendation
• Recommendation use cases
Time Series Analysis
• What is Time Series data?
• Time Series variables
• Different components of Time Series data
• Visualize the data to identify Time Series Components
• Implement ARIMA model for forecasting
• Exponential smoothing models
• Identifying different time series scenario based on which different Exponential Smoothing model can be applied

Got a question for us? Please mention it in the comments section and we will get back to you.

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