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Note that installation of a Python/R/Julia package is a one-time process. You will not have to re-install it once you have installed it.

Below are some basic ways to install packages in Python, R, and Julia. We also strongly recommend using tools like conda and Python virtual environments to manage your projects.

Installing Python Packages

Using Jupyter Notebook

In order to install Python packages in a notebook:

  1. Create a new notebook for Python by clicking the New drop-down menu at the top right corner and then choosing Python. Alternatively, you can open any of your existing notebooks for Python.
  2. Write the following code in a new cell of the notebook and run it:

    Code Block
    !pip3 install --upgrade PACKAGE_NAME


  3. Check if the package has been successfully installed.

Using the Terminal

In order to install Python packages from PyPI (Python Package Index) using pip:

  1. Click the New drop-down menu at the top right corner and then choosing choose Terminal.
  2. When a new browser tab with terminal access, type Type the following command in Terminal and press enter:

    Code Block
    python3 -m pip install --user PACKAGE_NAME_1, PACKAGE_NAME_2, ...
    Make sure to add --user option, which allows you to install the package in your local environment. Not doing so will return a permission error. Note that installation of a package is a one-time process. You will not have to re-install it once you have installed it. 
    Check


  3. Check if the package has been successfully installed.
  4. Close the tab.

Notes about Python package installation

1. Please see the pip documentation for more information about using pip.


2. Installing Python packages this way, you can find your Python packages in your user library:

Code Block
/home/HawkID/.local/lib/pythonx.x/site-packages

where HawkID is your HawkID, and pythonx.x indicates the Python version, for example, Python 3.7.


3. If you are using a user-installed Python package in Terminal, you will need to add your user library to PATH. In Terminal:

First, crease a .bashrc if you haven't done so before:

Code Block
touch ~/.bashrc

Add your user library to PATH:

Code Block
echo 'export PATH=$PATH:~/.local/bin' >> ~/.bashrc

And then run the following in Terminal. Note that .bashrc needs to be sourced every time you start a new Terminal session.

Code Block
source .bashrc

Installing R Packages

Using RStudio

In order to install R packages from CRAN (Comprehensive R Archive Network) :

  1. In the Console tab in RStudio, or in an R script file, type the following code and then run it:

    Code Block
    install.packages(c("PACKAGE_NAME_1", "PACKAGE_NAME_2", ...), repos="http://cran.r-project.org")


  2. Check if the packages have been successfully installed.
  3. Close the tab.

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Using Jupyter Notebook

In order to install R packages from CRAN (Comprehensive R Archive Network) :

  1. Create a new notebook for R by clicking the New drop-down menu at the top right corner and then choosing R. Alternatively, you can open any of your existing notebooks for R.
  2. Write the following code in a new cell of the notebook and run it:

    Code Block
    install.packages(c("PACKAGE_NAME_1", "PACKAGE_NAME_2", ...), repos="http://cran.r-project.org")

    Note that installation of a package is a one-time process. You will not have to re-install it once you have installed it. 


  3. Check Check if the packages have been successfully installed.

Installing Julia Packages

In order to install Julia packages using Pkg :

  1. Create a new notebook for Julia by clicking the New drop-down menu at the top right corner and then choosing Julia. Alternatively, you can open any of your existing notebooks for Julia.
  2. Write the following code in a new cell of the notebook and run it:

    Code Block
    using Pkg
    Pkg.add.(["PACKAGE_NAME_1", "PACKAGE_NAME_2", ...])

    Note that installation of a package is a one-time process. You will not have to re-install it once you have installed it. 


  3. Check Check if the packages have been successfully installed.

RStudio for R

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