Download R for Windows; R is part of many Linux distributions, you should check with your Linux package management system in addition to the link above. Source Code for all Platforms Windows and Mac users most likely want to download. R-directory Reference Links Free Data Sets Free Datasets. If you work with statistical programming long enough, you're going ta want to find more data to work with, either to practice on or to augment your own research. Here are a handful of sources for data to work with. All of the datasets listed here are free for download. R is a free software programming language and a software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

• Install R and RStudio on windows

In our previous article, we described what is R and why you should learn R. In this article, we’ll describe briefly how to install R and RStudio on Windows, MAC OSX and Linux platforms. RStudio is an integrated development environment for R that makes using R easier. It includes a console, code editor and tools for plotting.

To make things simple, we recommend to install first R and then RStudio.

1. R can be downloaded and installed on Windows, MAC OSX and Linux platforms from the Comprehensive R Archive Network (CRAN) webpage (http://cran.r-project.org/).

2. After installing R software, install also the RStudio software available at: http://www.rstudio.com/products/RStudio/.

Install R for windows

1. Download the latest version of R, for Windows, from CRAN at : https://cran.r-project.org/bin/windows/base/

2. Double-click on the file you just downloaded to install R

3. Cick ok –> Next –> Next –> Next …. (no need to change default installation parameters)

Install Rtools for Windows

Rtools contains tools to build your own packages on Windows, or to build R itself.

1. Download Rtools version corresponding to your R version at: https://cran.r-project.org/bin/windows/Rtools/. Use the latest release of Rtools with the latest release of R.

2. Double-click on the file you just downloaded to install Rtools (no need to change default installation parameters)

R Statistics Download For Windows

Install RStudio on Windows

• Download RStudio at : https://www.rstudio.com/products/rstudio/download/

1. Download the latest version of R, for MAC OSX, from CRAN at : https://cran.r-project.org/bin/macosx/

2. Double-click on the file you just downloaded to install R

3. Cick ok –> Next –> Next –> Next …. (no need to change default installation parameters)

4. Download and install the latest version of RStudio for MAC at: https://www.rstudio.com/products/rstudio/download/

1. R can be installed on Ubuntu, using the following Bash script:

sudo apt-get install r-base

2. RStudio for Linux is available at https://www.rstudio.com/products/rstudio/download/

To install the latest version of R for linux, read this: Installing R on Ubuntu

It is relatively simple to install R, but if you need further help you can try the following resources:

• Previous chapters
• Next chapters

This analysis has been performed using R software (ver. 3.2.3).

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Download R Programming Language

Learn how to use R to turn raw data into insight, knowledge, and understanding. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License.

Book Description

Topics included: Introduction • Data visualisation • Workflow: basics • Data transformation • Workflow: scripts • Exploratory Data Analysis • Workflow: projects • Tibbles • Data import • Tidy data • Relational data • Strings • Factors • Dates and times • Pipes • Functions • Vectors • Iteration • Model basics • Model building • Many models • R Markdown • Graphics for communication • R Markdown formats • R Markdown workflow.

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