Hi there, welcome to week 2 session.

Today we will learn,

- Why did I chose R over python
- Introduction to R language
- Basics of R

**Why R over python?**

We can choose R or python for data analysis. If you are already familiar with python, you can go with python. But I was newbie in both technologies.

I selected R because of the following reasons.

- R is object-oriented
- R is a functional programming language
- Operator overloading is much easier in R than in Python
- Parallelism in R has been much further developed than in Python
- R is designed for statistical analysis
- R is great for exploratory work
- R has huge number of
**packages**and readily usable**tests**that often provide you with the necessary tools to get up and running quickly - R can even be part of a big data solution

### Introduction to R language

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, of which Chambers is a member.

As you know, we need an environment to run any program. You need to have r-base to run R programs.

You can download r-base by following below links.

For Windows machine, click here

For mac OSX machine, click here

For Linux machine, click here

(if any of the link is broken, get the r-base from cran website)

Now we have **r-base**. We can start coding! But we always prefer to work with IDEs than working on command line. Even R has a beautiful IDE called **RStudio**.

RStudio is an open source IDE. You can download it from their website. Here is the link.

### Basics of R

Hope you have installed r-base and RStudio on your machine. Now launch RStudio or r-base interface.

After R is started, there is a console awaiting for input. At the prompt (>), you can enter numbers and perform calculations.

eg:

* > 1 + 2 *

output:

[1] 3

#### Variable assignment

We assign values to variables with the assignment operator “=”. Just typing the variable by itself at the prompt will print out the value. We should note that another form of assignment operator “<-” is also in use. I prefer using “<-” operator, for no specific reason!

eg:

*> x = 1
> x *

output:

[1] 1

* *

#### Comments

All text after the pound sign “#” within the same line is considered as a comment.

eg:

*> 1 + 1 # this is a comment *

output:

*[1] 2 *

#### Functions

R functions are invoked by its name, then followed by the parenthesis, and zero or more arguments. The following apply the function c to combine three numeric values into a vector.

eg:

*> c(1, 2, 3) *

output:

*[1] 1 2 3 *

#### Extension Package

Sometimes we need additional functionality beyond those offered by the core R library. In order to install an extension package, you should invoke the install.packages function at the prompt and follow the instruction.

eg:

*> install.packages(“package_name”) *

#### Getting Help

R provides extensive documentation. For example, entering ?c or help(c) at the prompt gives documentation of the function c in R.

eg:

*> help(c) *

If you are not sure about the name of the function you are looking for, you can perform a fuzzy search with the *apropos *function.

eg:

*> apropos(“can”)*

output:

*[1] “.rs.scanFiles” “canCoerce” “cancor” “scan” “volcano”*

*I will be writing about Sentiment analysis of twitter and WhatsApp data in the next post. *

*Thanks for visiting my blog. I always love to hear constructive feedback. Please give your feedback in the comment section below or write to me personally here.*

Reference:

https://cran.r-project.org/doc/manuals/R-intro.pdf

https://www.datacamp.com/courses/free-introduction-to-r