And you can install development packages of others with two lines of code. In R it makes sharing of your packages easy.You can see what changes between different versions of your code, analysis or written text!.You can revert back to a previous version, if you find errors or accidently deleted something.You can also report errors (bugs) or suggest new additions (features) to projects. People can contribute to your project and vice-versa. It makes sharing of your projects easy (once it’s setup, you’ll get there).Git allows you to track and share your code and analysis. R in combination with the distributed version control system Git provides a convenient setup to make your research project reproducible. Clone/fork an existing project from GitHub.(The tutorial was originally created on GitHub and hosted here.) If updates are available, select All (or just tidyverse), and click Install Updates.This tutorial in the context of the Reproducible Research Workshop provides you with the first steps on how to use Git with R and RStudio. To update packages, go to Tools > Check for Package Updates. You should be able to click “Next” to all dialogs to finish the installation. When the file finishes downloading, double-click to install. When you restart RStudio, it will use the updated version of R.
On Mac, you can simply download and install the newest version of R. You are ready for Data Science Essentials for R! This will start the updating process of your R installation by: “finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation.”įrom within RStudio, go to Help > Check for Updates to install newer version of RStudio (if available, optional). Install and load installr: install.packages("installr") and library(installr)Ĭall updateR() function. To update R on Windows, try using the package installr (only for Windows)
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