03 May 2022

What Is R Programing? Overview And Basics Of R

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Works at Ekeeda

What Is R Programing? Overview, And Basics Of R What Is R Programing? Overview, And Basics Of R

“R” is a programming language and software being developed by Ross Ihaka & Robert Gentleman in 1993. R language provides an extensive catalogue of statistical and graphical methods. It includes – ML, Algorithms, time series, linear regressions, statistical interference, and more. Most of the R libraries are written in R; however, for deep computational tasks – C, C++, and Fortan codes will be preferred.
R Programming is used by academics and big tech companies like Uber, Microsoft, Accenture, Google, Wipro, Airbnb, Facebook, and more.

Introduction To R and R-Studio
R is a popular language for statistical computing & graphics. It is widely used by statisticians, data miners, and data analysts to develop statistical software and data analysis. R-Studio is an integrated environment (IDE) for R that will be available in two formats – R Studio Desktop – a regular desktop application and R-Studio Server – runs on a remote server and allows access to R-Studio via the web browser. 
Why Should You Learn R Programming?
Simple, R language is an open-source programming platform that is constantly updated and improved by collaborative developers around the globe.
It has various external packages that will be suited for different purposes such as data manipulation, data cleaning, data visualization, wrangler, and more.
It is a very easy and straightforward language to pick once you’re comfortable with the basic R syntax
Nearly everyone who works with data know how R works? And how to make efficient use of it, so should you right?
Take an advanced R Programming Language today!

R-Studio Interface and R Basics
R-Studio is divided in four parts – Script, Environment, Console and Plot. Let’s understand each one of them in detail – 

  • Script – Commands are written, executed or saved 

  • Environment – Lists the data, variables, functions that are currently in the workspace

  • Console – Can quickly test codes. Commands & outputs are displayed, except plots. 

  • Plot - Graphics are displayed in this section

Here is the list of basic commands for R-Studio as follows: 

  • Clear console: Ctrl + L

  • Quit R-Studio: Ctrl + Q or quit ()

  • Run code from the script: Ctrl + Enter

  • Remove saved variable: remove ()

  • Clear everything in the workspace: remove (list = ls ())

  • Access previous command: Arrow up

  • R awaits the next command: >

  • R is expecting more inputs: +

  • Comment / uncomment code in script: Ctrl + Shift + C

  • Getting help: help () or?

For Arithmetic & Variable Assignment
R is used for Basic Arithmetic. You can assign values to variable to make calculations more flexible & robust. 

  • Add: +

  • Subtract: -

  • Multiply: *

  • Divide: /

  • Power: ^ or **

  • Integer divide: %/%

  • Modulo (remainder after division): %%

  • Variable assignment: = or <-

Comparison and Logical Operators
It will compare a pair of values and return either a true or a false. 

  • Equal to: == (note the difference between ==, which is used for comparison and =, which is used for assignment)

  • Not equal to: !=

  • Greater than: >

  • Less than: <

  • Greater than or equal to: >=

  • Less than or equal to: <=

Logical operators are used to combine multiple true and false statements.

  • And: &

  • Or: |

Data types
There are 3 main data types in R:

  • Numeric: numbers e.g. 0, 2.5

  • Character: Contains letters, numbers, and special characters e.g. “hello, world”

  • Logical: Boolean values i.e. true or false

Features of R Programming Language
R fits every process or system and has several reasons why you should learn R for data science. A few of the features are given below: 
Open-Source
It lets anyone access, modify and share the source code and libraries without any restrictions. 
Best Visualizations
Libraries such as ggplot2, Knitr, dplyr, Caret, RCrawler, and tidyr offer some of the best data visualizations that are attractive yet very potential. 
Support for Extensions 
Open-source allows you to make modifications in various libraries, even allowing you to create new ones to meet your needs. R has a vast collection of libraries. 
Wide Community Support 
R Programming Language comes with active community support for people of all skill levels. And boot camps & workshops encourage cooperative behaviour.
Simple to Understand
If stats are in your genes, then you will have a smooth time understanding and working with R as it is designed to meet the needs of statisticians especially ones from the data science field. 
Some of the Top R Software libraries for Data Science –

  • Dplyr

  • Ggplot2

  • Esquisse

  • BioConductor

  • Shiny

  • Lubridate

  • Knitr

  • Mlr

  • Quanteda.dictionaries

  • DT

  • RCrawler

  • Caret

  • RMarkdown

  • Leaflet

  • Janitor

What Is The Use of R Software?
R language is mainly used for statistical interference, data analysis, and Machine Learning algorithms. 
As far as industry view is concerned R is a language to do statistics. R is used in other industries like healthcare, shipping and logistics, banking and financial statistics, government and consulting firms, etc.
R Programming Language will primarily be used for statistics, visualization, and machine learning.  Nearly 12000 R libraries are stored in CRAN – a free and open-source space. You can download and use numerous libraries to perform Machine Learning or time series analysis.

Communicate with R 
R Software has various simple and smooth ways to present and share work, either through markdown documents or shiny apps. Everything can be hosted in Rpub, GitHub or a business website.
R-studio accepts markdown to write a document and you can export the document in various formats like: Document, HTML, PDF/Latex, Word, Presentation, HTML, and PDF beamer.

Why Use R Software For Data Science?
Data science is playing an instrumental role in shaping the way companies run their businesses. If company refrain their selves from Artificial Intelligence and Machine Learning, it will lead to massive losses and failure. The big question is which is the tool to counter these issues and bring a balance to analysis.
Well! There are plenty of tools available in the market to perform data analysis. Learning a new language will require more time investment. And if you need to get the best insights from data, then you will have to spend more time learning the appropriate tool and its ‘R programming language’. 
Although you have Excel and PowerBI, these tools are simple to learn but don’t offer good business capability, especially in terms of modelling. You might even have Python and SAS but even they have their own limitations. For instance, Python is a language with a one-sided learning curve – it’s a fantastic tool to deploy Machine Learning and AI but lacks communication features. And SAS is a dedicated tool to run the statistical analysis for business and it’s not FREE. So then what’s the ideal tool that brings an identical learning curve and is free of costs – R. R language is a good trade-off language is excellent communicative, free, and is best for implementation and data analysis.
For data visualization, you might use Tableau. It’s a great tool to discover patterns through graphs and charts. Learning Tableau is also not time-consuming. But one problem would be you might end up not finding patterns or creating a lot of charts. A tableau is a good tool for quick data visualization or business intelligence. For statistics and decision-making, R is the most appropriate tool. 
Stack Overflow is one of the biggest communities for programming languages. If you have coding issues or wanna understand the model, Stack Overflow will be of great help. Over the year, the percentage of question-views has increased a lot for R.
In fact, in data science, there are two tools that compete with each other R and Python. And these are the programming languages that would define ‘Data Science’.
You will have time to learn both if you study data science. Learning statistical modelling, data structures & algorithms is more important than learning a programming language. A programming language is a tool to compute & communicate your discovery. But the most important thing in data science is the way you deal with data – import, clean, prep, feature selection, etc. This should be your main focus and if you try to learn R and Python without a good background in statistics, it will prove to be stupid. Data scientists are the ones who understand the data, manipulate it and turn them into actionable insights to derive business.
Experts suggest the principal audience for data science will be business professionals. In businesses, communication is very important and there are several ways to communicate such as - report, web app, dashboard, etc. You need tools that will be all together.

Three Top Reasons To Use R Language For Data Science
Further, we will go through a few reasons why R is considered the best programming language for data science and also why it’s one of the best options for number crunch and data representations. 
Convenient Learning with ‘Tidyverse’
‘Tidyverse’ library has a vast R packages collection that eases the steep learning curve presented by R for Data Science. It's developed by Hadley Wickham & his team, and the idea behind developing Tidyverse was to have a consistent and structural programming interface with a vision of unified underlying design philosophy, grammar, and data structures. These packages enable an extensive range of functionalities such as data manipulation, data visualization, data importing, tidying, functional programming, and many more.
Data Wrangling
Data Science mainly deals with the process of gathering data, cleaning it, managing it, and deriving valuable insightful information by using several techniques and algorithms. The final thing is to get the data in the right form so that it could be processed. Data Wrangling is the sub-process that deals with gathering appropriate data and transforming it into a usable form.
And R Comes with several resourceful packages that will help in data wrangling such as: Esquisse, Dplyr, Knitr, tidyr, Ggplot2, Datapasta, and more than being used for data exploration and transformation, and some will also help in efficiently reading the data off several file formats.

Data Visualization
Data visualization is one of the key features of R programming languages as it is built exactly to meet this purpose. Data Visualization is one of the primary things in the process of data analysis, and R makes the activity quite easy and simple. It involves creating reports from analyses, and R-markdown comes into play for this. It brings an intuitive charts and graphs into presentable format that is preferred by businesses and corporates – when paired with htmlwidgets, shiny package, flexdashboard package or book down packages
Is Programming in R Difficult? 
Well a couple of years ago, R seemed to be a difficult nut to crack. It was confusing and was not structured as compared to other programming tools. To overcome the issue, Hadley Wickham developed a collection of packages known as ‘Tidyverse’ and everything just changed for the best. Data manipulation became important and interesting; the creation of graphs was not difficult anymore. 
The best algorithms for ML could be implemented with R. Packages like Keras and TensorFlow allowed the creation of excellent ML techniques. R came up with a package to conduct Xgboost, termed as one of the best algorithms for the Kaggle competition.
R can communicate with other languages and it's possible to call Python, Java, and C++ in R. You can also access big data in R and connect R with different databases like Spark or Hadoop. 
R has evolved and allowed run operations parallel to speed up the communication. The parallel package let you perform tasks in different machine cores. 

On an ending note –
R is an excellent tool to explore and process the potential data for businesses. You can do elaborate analysis such as clustering, correlation, and data reduction with the help of R. It is the most crucial part that comes with great featured engineering and model. 
Both R and Python programming are quite easy to learn. If you’re familiar with concepts on Java, and C++ then you will find it easy to adapt to Python, and if you’re intrigued by mathematics and statistics, then R will be a bit easier route for you to upskill and become industry-ready. 
There are several online platforms that offer r programming certification; however, it's advisable to google it, check reviews and ratings, faculty experience, internship and job opportunities, and other aspects before you sign up with any platform. You will find online learning, practical hands-on projects, and job assistance in top companies and start-ups in India. 
Learn R programming language from the industry masters like Google, Accenture, Wipro, Flipkart, etc., and earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  
Our r programming course will help you gain an edge over the cut-throat competition, infact programmers should always stay updated with the latest trends and industry techniques. Therefore, Ekeeda has come up with online courses on trending technology services like Blockchain, Software Development, Embedded & IoT, and more.
All the best and advanced your career in R today!

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