**What is R and its importance?**

** **R is a very easy popular programming language. It is very easy language for beginners to learn and write.

It is basically used for** **arithmetical calculations and pictorial(picture) presentation.

The most common use of R programming is to analyze and visualize data.

R is mainly used for statistical(arithmetical) computing and design. It has brought about thorough developments in large data and data analytics. It is the most extensively used language in the whole world of data science! Some of the big shots in the industries like LinkedIn, Google, Yahoo, and Facebook, rely on R for many of their functions.

R is best popularly used statistics programming language. It is the first range of data scientists as well as supported through a dynamic as well as talented community of contributors. R is actually trained within colleges as well as utilized in mission crucial business applications.

- It is a great measure for data-science, data-analysis, data-visualization, and machine-learning
- It supplies many statistical techniques (such as classification statistical tests, data assembling and data reduction).
- It’s very simple to draw graphs as well as charts in R, such as pie-chart, bar chart, histograms, scatter plot and so on.
- It has several packages (libraries of functions) which you can use to fix different complicated problems.
- It has a large community (group) assist.
- It works on various programs such as (Windows, Macintosh, Linux)
- It’s open source as well as free available on the internet. We have not pay any amount for it.

**Application of R**

- Social media : behavior analysis, sentiment anlysis
- Information Technology : business intelligence, software development
- Finance: stock market modeling, fraud detection
- Govermental uses : Weather forecasting, record keeping
- E-commerce

**What can I do with R?**

R can be used to perform a variety of tasks(functions) such as - built statistical models, data store and analyze data . Since data mining and data analysis are activities that need a variety of applications and different ideas to communicate so R is a perfect language to learn and very easy to use.

**Why R is so popular?**

R is the most prominent(famous) language in the world of Data Science. It is mainly used in examine data that is both assembled and disassembled . This has made R, the excellent language for doing statistical calculations(functions). R allows lot of different features that make it different from other Data Science languages.

**R Syntax:-**

1. To print the text in R, we use single or double quotes:

For example- “ Congratulations ! This is your first R program ”

2. To print numbers, just type the number (without any quotes):

For example-

56

78

For doing simple calculations, add numbers together (without any quotes):

For example- 9+7

**R Functions:-**

- A function is a collection of code which runs only when it's known as.
- In a function we can pass data, called parameters.
- A function can easily return data being an output.
- If any code which has to be used repeatedly in a program, then it is very difficult and time taking procedure to write that code again and again. There is no need to write a same code again and again in a program. Its best solution is to make a function. We give a name to that function and can be called by its name anywhere in the program.

**Creating a function:-**

For creating a function we use the `function()`

keyword.

**Pros and Cons of R**

Benefits: | Limitations: |

- Powerful Graphics - ML Operations - Array of packages - Cross Compatibility | - Slow - Poor security - Low support |

**Difference between the R and Python programming language:**

R | Python |

- These types of codes need much more maintence - Applied in data analysis and statistical modelling - Runs programs locally - Not cut out for handling large data sets - Some of the commonly used IDEs are Rstudio and R GUI - Essential libraries include caret, tidyverse, ggplot2 etc - R is inefficient as compared to python when it comes to speed - It is not easy to learn for beginners - It is best utilized for data visualization - It comprises hundreds of packages or even technique to complete a same task - It support only procedural programming for several functions and OOP for other functions. - It can be more slowly as compared with python - It makes it simple to utilize complicated statistical tests and mathematical calculations. | - These types of codes tend to be more rubust and can be easily maintained. - Applied in data science, web development, and embedded systems - Programs are integrated with web app for easy deployment - Can easily handle large data sets - Some of the commonly used IDEs are Jupyter, Spyder, Ipython etc - Essential libraries include Numpy, pandas, scikit-learn, scipy, TensorFlow etc - Python outshines R in terms of processing speed for majority cases - Simple and readable code structure makes python easier to learn for beginners. - It is utilized as the general purpose language for deployment as well as development - It is best utilized for deep learning - It's developed on the philosophy that there should be only one unique technique to perform it. Consequently, comprised few key packages to perform the task. - It's the multi paradigm language that means it supports several paradigms. such as functional, object oriented, aspect- oriented and so on - It is faster - It is good to develop something new from scrtch. It is utilized for application development also. |

Object in R:NULL Logical Integer Integer Real Complex String Vector/List Named list Vector Data frame Matrix Array | Object in python:None Bool Integer Long Float Complex String Tuple/List/Set/FrozenSet/Iterators Dictionary dimension NumPy array Dimension NumPy record array dimension NumPy array other NumPy array |

**Basic Syntax in R Programming Language:**

var1 = “Simple Homework” var2 <- “leftward homework” “rightward homework” -> var3 print(var1) print(var2) print(var3) |

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