R Programming Technology is an open source programming language. Also, the R programming language is the latest cutting-edge tool. R Basics is the hottest trend. Moreover, the R command line interface (C.L.I) consists of a prompt, usually the > character.
John Chambers and colleagues developed R at Bell Laboratories. Basically, R programing language is an implementation of the S programming Language. Also combines with lexical scoping semantics inspired by Scheme. Although, R was named partly after the first names of two R programming language authors. Moreover, the project conceives in 1992, with an initial version released in 1995 and a stable beta version in 2000.
In this R tutorial, we are moving towards installations of R Programming and R Studio:
We have to follow three basic steps in the same order to run R programming language and R Studio on your system.
In respect to the operating system we are using we have to follow the below-mentioned steps:
First, we have to download the appropriate version of the .pkg file form the following link.
Further, open the downloaded .pkg file and Install R.
For Ubuntu with Apt-get installed, execute sudo apt-get install r-base in terminal.
Download the binary setup file for R from the following link.
Open the downloaded .exe file and Install R.
Choose the appropriate installer file for your operating system. Afterward, download it and then run it to install R-studio.
We require a particular package to be installed if we need to use R studio. Further, follow the instructions below:
Run R studio
Afterward, we need to click on the packages tab in the bottom-right section. Once, you complete this then click install. Thus, the dialog box will appear.
In the install packages dialog, write the package name you want to install the Packages field. And then click install. This will install the package you searched for. Either give you a list of matching package based on your package text.
Thus, the installation procedure for R Studio.
In this R Tutorial, following points describe reasons to learn R Programming.
R is best for business because it’s an open source. Also, it’s great for visualization. Moreover, the R programming language has far more capabilities as compared to earlier tools. Also, companies are using R programming as their platform and recruit trained users of R.
These are some R features:
a. Statistics Features of R Programming Language
b. Programming Features of R
Basically, R jobs are not only being offered by IT companies. Although, all types of companies are hiring high paid R candidates including-
Basically, as we know that there is a huge demand for R jobs among start-ups. Also, companies have several R job openings with various positions like:
R has become the tool of choice for data scientists and statisticians across the world. Also, to predict things like the pricing of their products, etc, companies are using analytics. Below is a list of few companies using R:
***“R has slowly won over the hearts of many large corporates”. ***Why Top Companies using R
Basically, skills that are being valued by the industry shows a lack of understanding. R programming language is a tool, and people can be trained in tools. It is, yet, difficult to train people in Statistics, Data Mining, and Data Analytics, and so on. So there are very good job opportunities for R experts in India.
Obviously! R is the best option as it’s trending so much. Also, the R programming language is being used in Big M.N.C’s to Small-scale companies everywhere. It is also used in NON-IT fields, Government, and Non-government companies.
The future scope is very bright. As R programming Language is trending these days. Also, it’s simple to learn for those who are new to the R programming language.
Moreover, the recent average salary of R programming is best so you can think how high it will reach in the future.
You can check various jobs for R technology at below job portals:
Following are the best Books to learn R Programming Language.
a. A Handbook of programming with R by Garrett Grolemund
Generally, if you are new to R then this is the best book for you. As the language of the book is quite simple to understand and examples can be reproduced easily.
b. The Art of R Programming by Norman Matloff
Basically, this book tells how to do software development. As from basic types and data structures to advanced topics. Also, no statistical knowledge is required. Moreover, your programming skills can range from hobbyist to pro.
c. An Introduction To Statistical Learning With Applications in R by Trevor Hastie and Rob Tibshirani
Even if you don’t have knowledge of R then this book is best. As its good for the theoretical and practical understanding of many important topics.
For Example- machine learning and statistical techniques.
d. Learning RStudio For R Statistical Computing by Mark P.J.van der Loo
Basically, this book was designed for R developers and analysts. Also, only for those people who want to do R statistical development using RStudio functionality. Thus, one can create and manage statistical analysis projects, generate reports and graphics.
e. Practical Data Science with R by Nina Zumel & John Mount
Basically, in this book, an author has focused only on data science methods and their applications in the real world.
f. Advanced R by Hadley Wickham
Basically, this book is about how R language works that creates a difference between the top 3 analytical tool — R vs SAS vs SPSS.
g. R Packages by Hadley Wickham
Basically, this book is made for advanced R programmers who are looking to write their own R Packages. As the author has written documentation on R packages. Also, explains the components of the R package, including unit tests and vignettes.
Hope you like our explanation.
I hope this blog will help you to learn in a very advanced manner. Furthermore, if you have any query in this R Tutorial, feel free to ask in the comment section.
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I currently lead a research group with data scientists who use both R and Python. I have been in this field for over 14 years. I have witnessed the growth of both languages over the years and there is now a thriving community behind both.
I did not have a straightforward journey and learned many things the hard way. However, you can avoid making the mistakes I made and lead a more focussed, more rewarding journey and reach your goals quicker than others.
Before I dive in, let’s get something out of the way. R and Python are just tools to do the same thing. Data Science. Neither of the tools is inherently better than the other. Both the tools have been evolving over years (and will likely continue to do so).
Therefore, the short answer on whether you should learn Python or R is: it depends.
The longer answer, if you can spare a few minutes, will help you focus on what really matters and avoid the most common mistakes most enthusiastic beginners aspiring to become expert data scientists make.
#r-programming #python #perspective #r vs python: what should beginners learn? #r vs python #r
We are going to learn the introduction of machine learning and linear regression in R 4.0 programming. We will start with the introduction of machine learning then we will discuss the introduction of linear regression. I will also discuss types of linear regression and use cases of linear regression. there are two types of linear regression; simple linear regression and multiple linear regression. Use cases of linear regression are in house price prediction, stock price prediction, Twitter popularity prediction. I will thereafter show you how to analyze the Boston housing dataset. We will analyze dataset variables to understand the variable dependency for the linear regression model. I will show you the linear and non-linear regression models. Thereafter, I will show how you can improve the accuracy of a linear regression model.
#machine-learning #r #r-programming #developer
R is an increasingly popular programming language, particularly in the world of data analysis and data science. But learning R can be a frustrating challenge if you’re not sure how to approach it.
If you’ve struggled to learn R or another programming language in the past, you’re definitely not alone. And it’s not a failure on your part, or some inherent problem with the language. Usually, it’s the result of a mismatch between what’s motivating you to learn and how you’re actually learning.
This mismatch causes big problems when you’re learning any programming language, because it takes you straight to a place we like to call the cliff of boring.
What is the cliff of boring? It’s the mountain of boring coding syntax and dry practice problems you’re generally asked to work through before you can get to the good stuff — the stuff you actually want to do.
Nobody signs up to learn a programming language because they love syntax. Yet many learning resources, from textbooks to online courses, are written with the idea that students need to master all of the key areas of R syntax before they can do any real work with it.
That’s where new learners tend to drop off in droves. You get excited about learning a programming language because you want to do something with it, and but then you’re immediately led to this huge wall of complicated, boring stuff that’s between you and what you actually want to be doing. It’s no surprise that lots of students give up or drop off at points along their climb up this “cliff.”
There’s no way around learning syntax, in R or any other programming language. But there is a way to avoid the cliff of boring.
It’s a shame that so many students drop off at the cliff, because R is absolutely worth learning! In fact, R has some big advantages over other language for anyone who’s interested in learning data science:
#learning and motivation #learn r #r #rstats #study
Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
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