- #DOWNLOAD PACKAGE RSTUDIO INSTALL#
- #DOWNLOAD PACKAGE RSTUDIO UPDATE#
- #DOWNLOAD PACKAGE RSTUDIO CODE#
#DOWNLOAD PACKAGE RSTUDIO CODE#
He gave me the permission to share the tip, so here is the code I now use to perform the task of installing and loading R packages: # Package names
#DOWNLOAD PACKAGE RSTUDIO INSTALL#
I am glad he did as he introduced me to a much more efficient way to install and load R packages. Then one day, a colleague of mine shared some of his code with me. Reinstalling all packages every time I opened my script or R Markdown document was a waste of time. Moreover, I tended to reinstall all packages as I was working on 4 different computers and I could not remember which packages were already installed on which machine. Here is a preview of the code from my PhD thesis showing how the installation and loading of R packages looked like when I started working on R (only a fraction of them are displayed to shorten the code): # Installation of required packagesĪs you can guess the code became longer and longer as I needed more and more packages for my analyses.
As you use more and more packages you will soon start to have (too) many lines of code just for installing and loading them. Inefficient way to install and load R packagesĭepending on how long you have been using R, you may use a limited amount of packages or, on the contrary, a large amount of them.
#DOWNLOAD PACKAGE RSTUDIO UPDATE#
Note that packages must be installed only once (until you update your R, then you have to install them again), whereas packages must be loaded every time you open R. To load a package, run library(name_of_package) (this time "" around the name of the package are optional, but can still be used if you wish). Once the package is installed, you must load the package and only after it has been loaded you can use all the functions and datasets it contains. In order to use a package, it needs to be installed on your computer by running install.packages("name_of_package") (do not forget "" around the name of the package, otherwise R will look for an object saved under that name!). R is open source so everyone can write code and publish it as a package, and everyone can install a package and start using the functions or datasets built inside the package, all this for free. Packages extend existing base R functionalities by adding new ones. These extensions which are are collections of functions and datasets developed and published by R users are called packages. You will thus often need to install some “extensions” to perform the analyses you want.
Unlike other programs, only fundamental functionalities come by default with R.
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