Start Date: 02/16/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/r-programming
Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
R Programming and Advanced Topics in R R Programming is the study of programming in the R programming language. This course focuses on the R language, which is the standard language used for running interactive data science projects. After completing this course, you will be able to understand and program R packages. You will also be familiar with basic R concepts and language basics. This course is the second in a sequence. Beginning in the first course, which focused on data validation, we will move to higher level of R programming. We will introduce higher level R constructs, such as Racket, RethinkDB, and Roxygen. We will then discuss higher level R options, such as the R package development lifecycle and packages, which are the core of all R packages. We hope you enjoy the course!What is R? Data Types, Racket, and Verilab Racket for Scheme Higher Level R: Verilab and Packages R Interface and Capstone Project In the Capstone project, you will design a program that satisfies the requirements for an R interface, including libraries and utilities. You will use the R Interface to create a working interface between your program and the R language. You will implement a simple program (a library) and a purer interface (a utility). The interface program will then be shared by many other programs and used by other people. You will earn a
Article | Example |
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Reflection (computer programming) | The following is an example in [[R (programming language)|R]]: |
R (programming language) | IBM offers support for in-Hadoop execution of R, and provides a programming model for massively parallel in-database analytics in R. |
R (programming language) | "The R Journal" is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles on the use, and development of R, including packages, programming tips, CRAN news, and foundation news. |
R (programming language) | R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. A generic function acts differently depending on the classes of arguments passed to it. In other words, the generic function dispatches the function (method) specific to that class of object. For example, R has a generic codice_2 function that can print almost every class of object in R with a simple codice_3 syntax. |
R (programming language) | R is highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its lexical scoping rules. |
R (programming language) | R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. |
The R Journal | The journal publishes research articles in statistical computing that are of interest to users of the R programming language. |
R (programming language) | R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered. |
Metasyntactic variable | The R programming language often adds "norf" to the list. |
R (programming language) | Revolution R Enterprise DevelopR (part of Revolution R Enterprise), |
R (programming language) | R is a GNU package. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available. |
R (programming language) | Other R package resources include Crantastic, a community site for rating and reviewing all CRAN packages, and R-Forge, a central platform for the collaborative development of R packages, R-related software, and projects. R-Forge also hosts many unpublished beta packages, and development versions of CRAN packages. |
Java GUI for R | JGR (pronounced 'Jaguar') is a universal and unified Graphical User Interface for the R programming language, licensed under the GNU General Public License. |
R (programming language) | 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", of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000. |
R (programming language) | In 2007, Revolution Analytics was founded to provide commercial support for Revolution R, its distribution of R, which also includes components developed by the company. Major additional components include: ParallelR, the R Productivity Environment IDE, RevoScaleR (for big data analysis), RevoDeployR, web services framework, and the ability for reading and writing data in the SAS file format. They also offer a distribution of R designed to comply with established IQ/OQ/PQ criteria which enables clients in the pharmaceutical sector to validate their installation of REvolution R. In 2015, Microsoft Corporation completed the acquisition of Revolution Analytics. |
R (programming language) | R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. Many of R's standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Advanced users can write C, C++, Java, .NET or Python code to manipulate R objects directly. |
R (programming language) | The official annual gathering of R users is called "useR!". |
R (programming language) | R is an interpreted language; users typically access it through a command-line interpreter. If a user types codice_1 at the R command prompt and presses enter, the computer replies with 4, as shown below: |
R (programming language) | R functionality has been made accessible from several scripting languages such as Python, Perl, Ruby, F# and Julia. Scripting in R itself is possible via a front-end called littler. |
R (programming language) | A core set of packages is included with the installation of R, with more than 7,801 additional packages () available at the Comprehensive R Archive Network (CRAN), Bioconductor, Omegahat, GitHub, and other repositories. |