# R Programming For Data Science – An Easy Guide In 8 Points

## Introduction

R is a potent language used broadly for statistical computing and data analysis. The passage of r language from a fundamental text writer to interactional r studio. This was conceivable only because of kind contributions by r worldwide users. The inclusion of powerful packages in r has made it more powerful. The packages like the data table, have prepared data manipulation, computation, and visualization much faster. To know what is r in data science or learn r for data science, then keep reading this article to explore it and have a better understanding of it.

## 1) Basics of R Programming for data science

Let’s start with the very basics. To get usual with the r coding atmosphere, begin with some very basic calculations. The r console can also be used as an interactive calculator. Let’s try by Typing the following in a console:

> 6 / 3

>  2

> 2 3

> 5

> sqrt (121)

> 11

> log(12)

> 1.07

Similarly, you can experiment with numerous ways of combinations of calculations and get the results. In case, you want to obtain the previous calculation, this can be done in two ways. Firstly click on the r console, and press the Up or Down Arrow key on the keyboard. This will activate the previously executed commands. Press Enter.

## 2) Essentials for R programming

R has five basic classes of objects. Whatever you see or create in r is an object. A vector, data frame, matrix, even a variable is an object. R has 5 basic classes of objects. This involves:

• Character
• Logical
• Numeric
• Complex
• Integer

These classes have attributes, that can include

• names, dimension names
• class
• dimensions
• length

Attributes of an object can be accessed using the attributes() function.

## 3) Exploratory data analysis in R

Exploratory data analysis comprises univariate (one variable) as well as bivariate (two variables) analysis. In this section, we will examine few functions.

Step 1 – Firstly approaching the data.

Step 2 – Analysing category variables.

Step 3 – Analysing numeric variables.

Step 4 – Analysing categorical and numerical at the same time.

Covering some key points in a basic exploratory data analysis:

• Data types
• Missing values
• Outliers
• Distributions for both, numerical and categorical variables.

## 4) Data manipulation in R

Data can be represented in the form of data analytics using R. With data manipulation in r, one of the crucial aspects of computing is that it entitles its consequent visualization and analysis. Let’s see some common data structures in r:

•  Vectors

These are organized containers of primaeval elements and are used for one-dimensional data.

•  Matrices

These are rectangular collections of elements and are useful when all data is of a single class that is numeric or characters.

• Lists

These are organized containers for random elements and are used for greater dimension data, like individuals data information of an organization.

•  Data Frames

These are 2-dimensional containers for variables and records and are used for constituting data from spreadsheets etc.

## 5) Using R for data science

R for data science can be used for statistics analysis and some other functions. There are numerous ways to begin on your path to learning r. Read to learn more about r in data science, practical applications, the best add-on packages, and more.

## 6) What is R in data science?

R base, a non-profit concentrated on encouraging further growth through the project of R, portrays r as a language for graphics and statistics computing. It is an opensource software as it is clear and adaptable. R’s links allow combining with other systems and applications. It is a language of programming providing operators, functions, and objects, that enable users to model, explore and visualize databases. It can be utilized for statistical modeling and data analysis. It has several graphical and statistical abilities. The r base notes that it can be used for clustering, classification, statistical tests, and nonlinear and linear modeling. Its contributors include individuals who have advised improvements bugs and formed addon packages.

## 7) How is R used in data science?

R used in data science focuses on the language’s statistical and graphical uses. When you learn R for data science, you’ll learn how to use the language to perform statistical analyses and develop visualizations. R’s statistical functions make it easy to analyze and import data. It may be equipped with an integrated development environment.  As per a computer software company,  the purpose of an integrated development environment is to writing and working with software packages easier. RStudio is an integrated development environment for it that enhances the graphic’s accessibility and involves a highlighting writer that assists with the execution of code. This might be useful as you start to study r for data science.

## 8) Data science projects that use R

R for data science is used in industries such as telecommunications, banking, and media. In the following section, we explore the instances of visualization of data in r over real-world projects.

• Google Analytics: R can be coupled with google analytics data to execute statistical analysis and form clear data visualizations, as per google developers. Installing the RGoogleAnalytics package will entitle these perceptions.
• T- Mobile: A global communications firm uses to categorize customer service messages so that they can lead customers to an intermediary. This shares as a freely accessible variant of their message transferring classification implementation programming interaction.
• The Financial Times: R  is accepted by the monetary times from the visualizations in its articles. The visualization maps out each and every world cup match and was formed using r.
• Twitter: R can be used to carry out tweets’ text analysis. Text analytics and scratching of Twitter data are conceivable over the twitter package.
• BBC: BBC prepared an r cookbook and package and to standard their visualization formation process. It provides training for its journalists to understand this process.

## Conclusion

In conclusion, r is a great tool to examine and scrutinize the data. Detailed analysis like correlation, clustering, and data reduction is done with r. This is the most important part, without a positive feature engineering and model, the deployment of the machine learning will not give meaningful or relevant results.  R is open-source and free offering the possibility for anyone to have access to world-class statistical analysis tools. It is used widely in academia and the private sector and is the most popular statistical analysis programming language today.

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