R is a programming language and programming environment for statistical analysis and data visualization. The R language is inspired by S language, which was invented and developed by John Chambers from Bell Laboratories and which is the base for commercial software S-PLUS. In contrast to S-PLUS, R is a freeware application, introduced by Ross Ihaka and Robert Gentleman (the R in their first names gave the name to the program).

1. Download the installation file of the newest R version from here: CRAN¹⁾ (choose your operating system and download the installation file).
2. Install the program - you may keep the default location of installation files in Program Files/R. On Windows, the R folder in your Program Files folder may contain several subfolders with different R versions installed in parallel, e.g. R-2.10.1, R-2.11.1 etc.
3. When you run the program, you may change some basic setting (e.g. the size of the font, a colour of the background (if you feel it is more comfortable to have it e.g. orange) in menu Edit > GUI preferences. If you change anything in this window, you need to click on the Save button and save the file Rconsole to the folder /Program Files/R/R-version/etc.

If you have R already installed on your computer and you want to update to a newer version, the easiest way is to install the latest version along with the old one (and later possibly delete the old one if you don't need it or you want to save space on hard disk). All R versions are, if not changed, installed beside each other in R subfolder of Program Files folder (in case of Windows). You may need to install also all the libraries you used in the previous version. You can copy the folder library from the old R version to new R version and run an update procedure in new R version using the command update.packages (however, I do not recommend to do this if you are updating to new major version, e.g. from version 3.4.x to 3.5.0; in that case, the manual installation of packages (using install.packages function) is preferred). For this, do the following:

1. Close all running R programs (or RStudio) before starting!
2. Open some file manager (like Total Commander or Windows Explorer) and locate the library folder with R libraries from the previous version (usually located in c:\Program Files\R\R-x.x.x\library, where R-x.x.x is the number of the previous R version, e.g. R-3.1.2).
3. Copy all subfolders in library folder into library folder of the new version. Do not overwrite the newer versions of files in the library folder of the new R version.
4. Open the new R version, and update all libraries using command update.packages(ask = F)²⁾. This should update all the libraries to their latest version compatible with current version of R.

After you installed R on your computer, download the latest version of RStudio from RStudio website and follow instructions for installation (or directly download the RStudio Desktop software for your operation platform in this link). If you already have RStudio installed on your computer, you may check whether you have the latest version and update if you don't (in the RStudio menu, go to Help > Check for Updates).

RStudio is a convenient software, which combines the R program with a text editor and graphical interface (and offers much more, like the organization of scripts and outputs into projects within a single folder, and for advanced users also a convenient building of packages, document markup etc.). One thing I found a bit not handy is the RStudio's graphical output - it offers its' own unique sizeable graphical output into one of the subpanels, but this sometimes produces troubles (especially in the case when you draw more complex figures). The workaround is to use RStudio for coding, but open the original R graphics device and use it for plotting (in Windows, type windows () into the console and a new active window will open; you can move it around the screen, or right-click on it by mouse and select “Stay on top” to keep it as the top window always visible on your screen).

Optionally, there is a few other text editors, which can be associated with R, e.g. Tinn-R.

R has a great option to extend the basic functionality for other functions wrapped into packages. There are thousands of packages in central repositories like CRAN or Bioconductor, and also in developers repositories like R-Forge or GitHub. For an overview of documentation for packages aggregated from these repositories, check the website R Documentation.

If you have administrator rights for the computer, the packages will be installed into the library subfolder, usually located in c:\\Program Files\R\R-x.x.x\ ³⁾. If you don't have administrator rights, you will be asked to install the libraries into some writeable location of the hard disc, by default into the Documents subfolder of your account (here, the following structure of subfolders will be created: Documents\R\win-library\3.2).

Most of the commonly used R packages for analysis of ecological data are available from CRAN, and to install them, you can use install.packages command in R. When using install.packages function, the name of the package should be enclosed by quotation marks, e.g. install.packages ('vegan'); if more than one packages needs to be installed, wrap the names into character vector, e.g. install.packages (c('vegan', 'ade4')).

If you are using RStudio, you may use built-in function to install the packages, which is (in default setting of RStudio) located in bottom right panel, in the folder Packages - click the button Install, in Install from choose Repository (CRAN), and in Packages type the name of the package.

However, many packages are not available from CRAN, e.g. because they are still under development (beta versions), or because they do not fit rather strict CRAN rules for hosting packages. Alternative places where you can find R packages are, e.g. GitHub or R-Forge.

GitHub doesn't offer precompiled package versions - you need to compile the packages from source on your computer. However, there is a convenient R function in R package devtools developed by Hadley Wickham, which can help you with installing packages hosted on GitHub: install_github. To install some package (here called MyPackage, first install devtools from CRAN, and then apply the function install_github:

install.packages ("devtools")
devtools::install_github ("MyPackage")

(note that :: means that install_github function will be read from the devtools namespace without need to actually upload the whole devtools library). If the GitHub package contains C/C++/Fortran code which needs to be compiled, you still need to install the set of compilation tools called Rtools on your computer (see Install Rtools (for Windows users only) below).

R-Forge contains both source packages (files with the extension *.tar.gz containing the original R code, which needs to be compiled during installation) and also already compiled binaries for Windows (*zip files, do not need to be built). The binaries are available only for the latest R version available, so if you are using the older version of R, you need to build the library from source. For example, to install package packfor⁴⁾ for forward selection of variables directly from R-Forge, type

install.packages("packfor", repos="http://R-Forge.R-project.org")

If you are using the latest R version, the package will probably install without problems. If not, you may receive the following warning message:

Warning in install.packages :
  package ‘packfor’ is not available (for R version 3.1.2)

In that case, you need to install the package from source. Manually download the binary file from R-Forge (with *.zip extension) to your computer, and use the command install.packages with argument repos = NULL:

install.packages ("C:/Users/Downloads/packfor_0.0-8.zip", repos = NULL)

(note that this works if your downloaded file is the version of packfor 0.0-8 and it is saved in Downloads folder - modify this for your current needs). If even this option doesn't work, you may need to install the package from the source - download *.tar.gz version to your computer and use install.packages function with repos = NULL and type = “source”:

install.packages ("C:/Users/Downloads/packfor_0.0-8.tar.gz", repos = NULL, type = "source")

However, installing R packages from source is nontrivial task and may require that your computer has installed set of compilation tools, so called Rtools, because installing from source consists of several steps (compiling C/C++/Fortran codes, making documentations, checking for code errors, running examples etc.) - see instructions below how to install.

Packages hosted on CRAN usually contain package binaries - you can directly install packages without compiling them from the source code. If you install packages from GitHub, R-Forge or other repositories, you may need to compile them from the source. To do it, do the following:

• download Rtools.exe from Rtools (choose the version compatible with your version of R, either 32- or 64-bit one);
• if you already have installed an older version of Rtools on your computer, uninstall it (Rtools are usually installed in the folder c:\Rtools);
• follow the installation wizard of Rtools, it is pretty straight forward. You may need to make sure that the path to Rtools will be added into the computer's registry. Although the website of Rtools mentions a special procedure how to do it, in my experience it is added into the registry (PATH) automatically during the installation if the “Save version information into the registry” option is selected (this is done automatically):

You do not need to install Rtools if

• you use devtools::install_github function to install package from GitHub and the package hosted on GitHub does not contain any compiled code;
• you plan to install a package from R-forge for the latest version of R, for which R-forge usually offers compiled binaries; however, if you are using an older version of R, you still need to build the package from the source (R-forge offers binaries only for the current version of R, not older ones; this is different from CRAN, which offers compiled binaries of packages for several R versions backwards).