R programming is used for statistical information and data representation. So we must have knowledge of statistical theory in maths, an understanding of different types of graphs for data representation, and most importantly we should have prior knowledge of any programming. 

R is a programming as well as a software environment for statistical data processing, visual description, and reporting. R Programming was created by Robert Gentleman and Ross Ihaka at Auckland University in New Zealand in August 1993. 


what is r programming

R Programming is named after the first letters of the names of these two. Right, present this programming is being developed by the R Development Core Team. 

R Programming is open-source and is found for free under the GNU General Public License. R This is the Programming GNU Package. This programming has been designed for statistical computing, in which hundreds of packages have been developed. 


These programs have been preserving the Statistical Model in nearly every manner. It is extensively used for Statistics and Data Mining. The software environment in R is written in C and Fortran programming. R Programming, Linux, Windows, Mac, and nearly all OS systems


What is R Programming 

R Language was created by Robert Gentleman and Ross Ihaka in August 1993 at Auckland University in New Zealand. R language is called from the initial letters of these two names, now this language is being developed by R Development Core Team, R is a programming language and environment widely used in statistical computing, data analytics, and scientific research is used to. 


It is one of the most popular languages ​​used by statisticians, data analysts, researchers, and marketers to get clean, analyze, visualize and present data. Due to its rich syntax and easy-to-use interface, it has risen in popularity in recent years.

This programming language includes machine learning algorithms, linear regression, time series, statistical inference to mention a few, most of the R libraries are written in R, but for intensive computational work, C, C++, and Fortran programs are recommended. 

R programming is not only given by academics, but many big corporations also use the R programming language, like Uber, Google, Airbnb, Facebook, and so on.


It is also a software environment, which is used for analyzing statistical information, graphical representation, reporting, and data modeling. R is an implementation of the s programming language, combined with lexical scoping semantics. 

R not only lets us conduct branching and looping but also allows us to do modular programming using functions. Allows integration of processes developed in R, C, C++, .Net, Python, and Fortran languages ​​to improve efficiency.


The history of R is around 20-30 years ago. R was invented by Ross Dhaka and Robert Gentleman at the University of Auckland, New Zealand, and is developed by the R Development Core Team. 

This computer language derives its name from the names of both creators, the first proposal being explored in 1992. The original version was launched in 1995, then in 2000, a stable beta version was launched.


Why should I learn R Programming?

  • R is open-source, so it is free.
  • R is cross-platform compatible, thus it can be installed on Windows, Mac OSX, and Linux.
  • R provides a wide array of statistical tools and graphical capabilities.
  • R gives the opportunity to do repeatable research by integrating the script and the outcome in a single file.
  • R has a big community both in education and business.
  • It's easy to build R packages to tackle certain situations.


There are numerous tools available on the market to undertake data analysis. It takes time to learn new languages, data scientists may use all good tools, namely, R and Python, we may not have time to master them both. When we start learning data science. 

Learning statistical models and algorithms is more important than learning a programming language. A computer language is applied to compute and communicate our search.


Critical job in data science is the way we deal with data, clean, feature engineering, feature selection, and import should be our major emphasis. The role of a Data Scientist is to analyze the data, modify it and identify the optimal solution. 


For machine learning, the best algorithms can be implemented using R Keras and TensorFlow. Which helps us to develop high-end machine learning systems. R is a package to conduct Xgboost. xgboost is one of the top algorithms for the Kaggle competition.


Interacts with various languages ​​and maybe uses Python, Java, C++. Big data is also available to the globe R. We may link R to other databases like Spark or Hadoop. 

In summary, R is a wonderful tool for studying and analyzing data. Detailed analyses such as clustering, correlation, and data reduction are done with R.


R programming is now used extensively by software programmers, statisticians, data scientists, and data miners. It is one of the most common analytics tools used in data analytics and business analytics. 

It has a wide range of applications in sectors including healthcare, academics, consultancy, finance, media, and many more. Its broad application in statistics, data visualization, and machine learning has given birth to a need for capable people certified in R.


Features of R programming

R is a domain-specific programming language developed for data analysis. It has certain unique properties that make it highly strong. The most important is likely the idea of vectors. 

These vectors allow us to execute a complicated action on a group of values ​​in a single command, some of the main characteristics of R are as follows -


It is a free and open-source programming language published under the GNU (General Public License) (General Public License).

It has cross-platform connectivity which means it has distributions operating on Windows, Linux, and Mac. R code can be easily ported from one platform to another.

It utilizes an interpreter instead of a compiler, which contributes to the development of the code.

It successfully associates different systems, and it works well in getting information from Microsoft Excel, as well as Microsoft Access, MySQL, SQLite, Oracle, etc.

It is a type of personality that bridges the gap between software development and data analysis.

It provides a wide selection of packages with a diversity of code, functions, and features, including data analysis, statistical modeling, visualization, machine learning, and bespoke code for data import and editing.

It combines many strong tools to communicate reports in different forms such as CSV, XML, HTML, and PDF, and also through interactive websites with the aid of R packages.


Steps to conduct data analysis in R programming

Import − The first step is to import the data into the R environment. This implies that you take data saved in files, databases, HTML tables, etc., and put it into an R data frame to do data analysis on it.

Transform − In this phase, initially, we arrange our data by making each column a variable and each row an observation. Once we have well-organized data, we focus down on it to discover the observations of importance to us, create new variables that are functions of existing variables, and get summary statistics of the observations.

Visualization − It is used to make our data more understandable by showing the data in graphical form. Visualization makes it simple to discover patterns, trends, and outliers in our data. It helps us to convey information and outcomes in a fast and visible manner.

Model − Models are complementary tools to visualization, they are mathematical or computational tools used to solve problems linked to our observations.

Communication − In this final step of data analysis, we focus on sharing the outcomes from visualization and modeling with others. This gives the ease with which to generate well-designed print-quality plots for distribution throughout the world.


What did you learn today?

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