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R Programming - Tutorialspoint



R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.




R Programming - Tutorialspoint



This tutorial is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.


Before proceeding with this tutorial, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.


Our R tutorial includes all topics of R such as introduction, features, installation, rstudio ide, variables, datatypes, operators, if statement, vector, data handing, graphics, statistical modelling, etc. This programming language was named R, based on the first name letter of the two authors (Robert Gentleman and Ross Ihaka).


"R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand." The R Development Core Team currently develops R. It is also a software environment used to analyze statistical information, graphical representation, reporting, and data modeling. R is the implementation of the S programming language, which is combined with lexical scoping semantics.


R not only allows us to do branching and looping but also allows to do modular programming using functions. R allows integration with the procedures written in the C, C++, .Net, Python, and FORTRAN languages to improve efficiency.


The history of R goes back about 20-30 years ago. R was developed by Ross lhaka and Robert Gentleman in the University of Auckland, New Zealand, and the R Development Core Team currently develops it. This programming language name is taken from the name of both the developers. The first project was considered in 1992. The initial version was released in 1995, and in 2000, a stable beta version was released.


R is a domain-specific programming language which aims to do data analysis. It has some unique features which make it very powerful. The most important arguably being the notation of vectors. These vectors allow us to perform a complex operation on a set of values in a single command. There are the following features of R programming:


There are several tools available in the market to perform data analysis. Learning new languages is time taken. The data scientist can use two excellent tools, i.e., R and Python. We may not have time to learn them both at the time when we get started to learn data science. Learning statistical modeling and algorithm is more important than to learn a programming language. A programming language is used to compute and communicate our discovery.


R programming is used for statistical information and data representation. So it is required that we should have the knowledge of statistical theory in mathematics. Understanding of different types of graphs for data representation and most important is that we should have prior knowledge of any programming.


R is a programming language is widely used by data scientists and major corporations like Google, Airbnb, Facebook etc. for data analysis. This is a complete course on R for beginners and covers basics to advance topics like machine learning algorithm, linear regression, time series, statistical inference etc.


R is a programming language and also a software environment for statistical computing and data analysis. R was developed by Ross Ihaka and Robert Gentleman at the university of Auckland, New Zealand. R is the open-source programming language and it is available at widely used platforms e.g. Windows, Linux and Mac. It generally comes with the command-line interface and provides a vast list of packages for performing tasks. R is an interpreted language that supports both procedural programming and object-oriented programming.


R programming tutorial is designed for beginners and experts. This tutorial gives you the knowledge of all concepts of that programming language.R language tutorial covers all the basic and advanced concepts of R, including introduction, features, installation, variables, data types, operators, if statement, vectors, data handling, graphics, and statistical modelling.


R is a language that was developed by Statisticians for Statisticians. So, if you are one of those statisticians who wants a simple and effective tool to work with, then R should be your go-to language. Now, even though R was created for the purpose of Statistical analysis, it is a versatile and powerful tool. So, in this R Programming Tutorial, we will comprehensively understand why R is so powerful and why you should learn R programming for the advancement of your career.


R is one of the most commonly used programming languages used in data mining. As of March 2022, R ranks 11th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020. In this R tutorial, we will start by learning what exactly is R.


Since R is an open-source programming language, you can download it for free and start to learn R Programming. And if you already are an expert at R Programming, you can contribute to the R Community by creating your packages which the entire R community can use. So, you can add your innovations to the existing set of libraries in R.


R can be run on low-end laptops and desktops having 4GB RAM and i3 Processor. It's a myth that R needs a powerful system. Obviously, if you need to handle large datasets, you will need more system memory with a powerful processor. So while a less expensive laptop is sufficient for practicing R programming and memory-intensive algorithms require a high-performance laptop.


R is used for a variety of purposes, from predictive model building to web scrapping. As a first step you should focus on the basics of R. Once done it helps you to jump to more advanced programming concepts.R has several packages for data science that are easy to use but require some background in statistics. If you already have a good knowledge of statistics, these packages are easy. Make your schedule and follow it without cheating. If you are ready for putting efforts in learning how to manipulate data using R and can spend 8 hours a day, you can learn it in a week. When you're done, use various statistical algorithms in R. You can make use of the publicly available datasets for practice


In our previous tutorial Loops in R: Usage and Alternatives , we discussed one of the most important constructs in programming: the loop. Eventually we deprecated the usage of loops in R in favor of vectorized functions. In this post we highlight some of the most used vectorized functions: the apply functions.


This is part 1 of a three part tutorial on ggplot2, an aesthetically pleasing (and very popular) graphics framework in R. This tutorial is primarily geared towards those having some basic knowledge of the R programming language and want to make complex and nice looking charts with R ggplot2.


GLM in R is a class of regression models that supports non-normal distributions and can be implemented in R through glm() function that takes various parameters, and allowing user to apply various regression models like logistic, poission etc., and that the model works well with a variable which depicts a non-constant variance, with three important components viz. random, systematic, and link component making the GLM model, and R programming allowing seamless flexibility to the user in the implementation of the concept.


The Data Science Initiative offers programming support and a wide array of workshops for UCSF researchers and staff with an emphasis on analytics, data management, and visualization. Round out your programming knowledge by attending one of our free workshops on Python, R, SQL, and Unix, scheduling a one-on-one consultation with a programming expert, or discover helpful resources below to help get you started.


Attend an upcoming programming workshop and stay up to date with the latest DSI news. Topics include basic programming and setup, code transfers, scripting, open source applications, and more. See the course catalog for a list of core DSI workshops and materials.


This chapter shows you how you can paint your own custom drawing (such as graphs, charts, drawings and, in particular, computer game avatars) because you cannot find standard GUI components that meets your requirements. I shall stress that you should try to reuse the standard GUI components as far as possible and leave custom graphics as the last resort. Nonetheless, custom graphics is crucial in game programming.


Animation usually involves multi-threading, so that the GUI refreshing operations does not interfere with the programming logic. Multi-threading is an advanced topics. Read "Multithreading & Concurrent Programming"


Dynamic programming (usually referred to as DP ) is a very powerful technique to solve a particular class of problems. It demands very elegant formulation of the approach and simple thinking and the coding part is very easy. The idea is very simple, If you have solved a problem with the given input, then save the result for future reference, so as to avoid solving the same problem again.. shortly 'Remember your Past' :) . If the given problem can be broken up in to smaller sub-problems and these smaller subproblems are in turn divided in to still-smaller ones, and in this process, if you observe some over-lapping subproblems, then its a big hint for DP. Also, the optimal solutions to the subproblems contribute to the optimal solution of the given problem ( referred to as the Optimal Substructure Property ). 041b061a72


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