And to use R, you’ll need to install the R Compiler and R Studio. But did you know that you can also directly run your R code right in a Jupyter Notebook? This helps in so many ways if you are already used to using Jupyter Notebook for Machine Learning-related tasks using Python.

In this tutorial, I’ll show you exactly how you can set up your local machine to run the R programming language directly in Jupyter Notebook. The processes I am going to show you today are equally applicable to all major operating systems (Windows, MacOS, and Linux OSes).

Table of Contents

• Install Conda

• Create a New Environment

• Activate Your Conda Environment

• Install ipykernel and jupyter

• Install R in the Conda Environment

• Open the Jupyter Notebook

• Run R in Jupyter Notebook

• Conclusion

Install Conda

You’d normally use Conda to handle multiple environments in Python. And here, we’re going to use the same Conda program to install R in our environment. You can either use Anaconda or Miniconda.

I prefer Miniconda as it’s so lightweight. You’ll also get the opportunity to install the latest packages directly using Miniconda. But you can simply go with the Anaconda if you are already comfortable with that.

Create a New Environment

Many people tend to use the Base environment. But I never like to use the Base environment directly as you typically need multiple environments for handling different package and versions of packages as well.

So I’ll create a new environment where I’ll work on my R programming language-related tasks using Jupyter Notebook.

To create a new Conda environment, simply use the following command:

conda create --name r-conda

Here, r-conda is my Conda environment’s name. You can choose any other name, but keep in mind that the conda env name can not have any whitespaces in it.

It will create a new Conda environment named r-conda for me.

Activate Your Conda Environment

If you want to work on a separate conda environment, you’ll need to make sure that you’re activating that specific conda environment before starting to do anything.

I want to work on the r-conda conda environment. So I can simply activate the conda environment using the following command:

conda activate r-conda

You need to use the exact conda env name that you want if it’s different than r-conda in the command.

You will see the conda environment’s name as (conda-env-name) at the left side of your terminal.

Install ipykernel and jupyter

I always like to install the ipykernel and jupyter in all of my conda environments as they help manage different conda environments’ Jupyter notebooks/labs separately.

So I’m going to install them together in my conda env by using the command below:

conda install ipykernel jupyter

This will install both ipykernel and jupyter in the activated conda environment.

Install R in the Conda Environment

To install R directly in the conda environment, simply use the following command:

conda install -c r r-irkernel

This will install the necessary components that enable your local computer to run the R program in your Jupyter Notebook.

Open the Jupyter Notebook

Now you can open the Jupyter Notebook either by using jupyter notebook or jupyter notebook --ip=0.0.0.0 --port=8889 --no-browser --allow-root --NotebookApp.token=''. Just make sure to modify the IP, port, root configuration, and token as you see fit for your work.

Open the given link in the terminal to open Jupyter Notebook in your web browser.

Run R in Jupyter Notebook

After opening Jupyter Notebook in your web browser, when you want to create a new notebook for R, you will get R directly in the “New” menu like the image given below.

Now, you can use the R language directly in your Jupyter Notebook!

You can also see the R programming language logo at the top right side of your Notebook.

Conclusion

Thank you for reading the entire article. I hope you have learned something new here.

If you have enjoyed the procedures step-by-step, then don't forget to let me know on Twitter/X or LinkedIn. I would appreciate it if you could endorse me for some relevant skillsets on LinkedIn. I would also recommend that you subscribe to my YouTube channel for regular programming-related content.

You can follow me on GitHub as well if you are interested in open source. Make sure to check my website as well.

Thank you so much! 😀

In this article, you'll learn what a channel is, how to use default channels, and how add new channels in Conda.

What Is a Channel in Conda?

A channel is the location where packages are stored remotely.

When you install Conda for the first time, it comes with a channel called default. You can check that using the command below:

conda config --show channels

The default channel enables you to install packages that are currently available in its directory/storage.

To install a package using the default channel, you use the conda install command followed by the name of the package. That is:

conda install package-name

Although numerous packages can be installed from the default channel, it's possible to come across packages that are not accessible from it.

In cases like this, you'd usually get the "PackagesNotFoundError: The following channels are not available from current channels" error message.

You'll see a workaround for that error in the next section.

How To Install a Package in Conda Using a Channel Name

In this section, you'll learn how to install a package that isn't available from the default channel in Conda by specifying the name of the channel where that package is stored remotely.

You'll also see how to add a channel to your list of channels.

Here's an example using Matplotlib:

conda install -c conda-forge matplotlib

In the command above:

• The -c flage denotes the word channel.
• conda-forge denotes the name of the channel where Matplotlib was installed from.

Although we installed Matplotlib from conda-forge, conda-forgewill not be added to our list of channels.

So if you run the conda config --show channels command, you'd only see the default channel.

You can add a channel to the list of channels using the conda config --add channels channel-name command. That is:

conda config --add channels conda-forge

The command above will add conda-forge to the list of Conda channels. This means that you don't have to specify the channel name if you are installing a package that is available from the conda-forge channel.

Summary

In this article, we talked about channels in Conda. They are used to access packages remotely.

We saw how to use Conda's default channel to install packages. We also saw how to use and add new channels that aren't built into Conda.

Happy coding!

In this article, you'll learn how to remove an environment's package using in built Conda commands.

You'll learn the following:

• How to create an environment.
• How to install packages in an environment.
• How to remove/delete an environment's package.

Let's get started!

How To Create an Environment in Conda

You can use the conda create package-name to create a new environment in Conda.

Here's an example:

conda create -n package-tutorial

The command above creates an environment called package-tutorial.

You can activate or switch to the package-tutorial environment using the conda activate environment-name command. That is:

conda activate package-tutorial

How To Install Packages in a Conda Environment

In the last section, we created and activated an environment called package-tutorial.

In this section, you'll see how to install a package in that environment. Let's install Matplotlib.

You can install a package using the conda install package-name command.

Here's one of the command for installing Matplotlib in Conda:

conda install -c conda-forge matplotlib

The installation might take a while to download and extract the package. You can check the packages that exist in your environment using conda list command.

Once the installation is complete, use the conda list command to verify that the package has been installed in your environment.

How To Remove a Package in Conda

You can remove a package in the current environment by running the conda remove package-name command.

In our case, we want to remove Matplotlib from the current environment (package-tutorial environment):

conda remove matplotlib

The command above removes Matplotlib from the current environment. When you run the conda list command, Matplotlib will no longer be listed as a package.

Summary

In this article, we talked about packages in Conda. They can be installed in Conda environments.

We saw how to create and activate a Conda environment . We also saw how to install and remove packages in Conda.

Happy coding!

With Conda, you can create separate environments for specific projects. You can have one environment for machine learning and another environment for data analytics.

Each environment can have its own packages. Packages installed in one environment can't be accessed in a different environment.

In this article, you'll learn how to delete an environment in Conda using built-in Conda commands.

How to Delete an Environment in Conda

You can get a list of existing Conda environments using the command below:

conda env list

Before you delete an environment in Conda, you should first deactivate it. You can do that using this command:

conda deactivate

Once you've deactivated the environment, you'd be switched back to the base environment.

To delete an environment, run the command below:

conda remove --name ENV_NAME --all

ENV_NAME denotes the name of the environment to be removed/deleted. Make sure you deactivate an environment before removing it by running the conda deactivate command.

The --all flag removes all the packages installed in that environment.

Here's a summary of the steps involved in deleting an environment in Conda:

• Deactivate the environment using the conda deactivate command.
• Delete the environment using the conda remove --name ENV_NAME --all command.

Summary

In this article, we talked about Conda. A package and environment management system that is used to install and manage separate coding environments and their packages.

We saw different commands that can be used to deactivate and remove an environment in Conda.

Happy coding!

Say no to pip install in the command line! Here's an alternative way to install TensorFlow on your local machine in 3 steps.

_Photo by [Unsplash](https://unsplash.com/@kowalikus?utm_source=ghost&utm_medium=referral&utm_campaign=api-credit">Krzysztof Kowalik / <a href="https://unsplash.com/?utm_source=ghost&utm_medium=referral&utmcampaign=api-credit)

Why am I writing this?

I played around with pip install with multiple configurations for several hours, trying to figure how to properly set my python environment for TensorFlow and Keras.

why is tensorflow so hard to install — 600k+ results

unable to install tensorflow on windows site:stackoverflow.com — 26k+ results

Just before I gave up, I found this…

_“One key benefit of installing TensorFlow using conda rather than pip is a result of the conda package management system. When TensorFlow is installed using conda, conda installs all the necessary and compatible dependencies for the packages as well. _”__

This article will walk you through the process how to install TensorFlow and Keras by using the GUI version of Anaconda. I assume you have downloaded and installed Anaconda Navigator already.

Let’s get started!

1. Launch Anaconda Navigator. Go to the Environments tab and click ‘Create’.

Go to ‘Environments tab’, click ‘Create’

2. Input a new environment name - I put ‘tensorflow_env’. Make sure to select Python 3.6 here! Then ‘Create’, this may take few minutes.

make sure to select Python 3.6

3. In your new ‘tensorflow_env’ environment, select ‘Not installed’, and type in ‘tensorflow’. Then, tick ‘tensorflow’ and ‘Apply’. The pop-up window will appear, go ahead and apply. This may take several minutes.

Do the same for ‘keras’.

Check your installation by importing the packages. If everything is okay, the command will return nothing. If the installation was unsuccessful, you will get an error.

no error pop up — Yeah!

You can also try with Spyder.

no error pop up — Yeah!

And…Ta-da! It’s done! You can follow this article to test your newly installed packages :)

Thank you for reading. Please give it a try, and let me know your feedback!

Consider following me on GitHub, Medium, and Twitter to get more articles and tutorials on your feed if you like what I did. :)

A simple guide to Anaconda and its installation on Ubuntu 16.04 (64-bit).

[Anaconda Logo](https://en.wikipedia.org/wiki/Anaconda(Python_distribution)" rel="noopener" target="blank" title=")

Hey everyone. I wrote this post to guide you through how Anaconda is installed in Ubuntu versions. Additionally, it covers various details about Anaconda and the area in which it is utilized.

For starters, what is Anaconda all about?

Before probing into what Anaconda really means and what it is about, we will first learn about Conda.

Quoting Conda’s official blog:

Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux.

Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer.

It was created for Python programs, but it can package and distribute software for any language.

The next question on the table is: why Conda, all of a sudden? We all know that is a package management system used to install and manage software packages written in Python.

That has its limitation too. It can only be used for Python packages.

pip is focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM which do not have a setup file in their source code.

To put it in simple words:

pip is a package manager that facilitates installation, upgrade, and uninstallation of python packages. It works with virtual python environments.

Conda is a package manager for any software (installation, upgrade and uninstallation). It works with virtual system environments.

Conda is a packaging tool and installer that aims to do more than what pip does: handle library dependencies outside of the Python packages as well as the Python packages themselves.

Conda also creates a virtual environment.

How does Anaconda come into the picture?

Conda is written entirely in Python which makes it easier to use in Python virtual environments. Furthermore, we can use Conda for C libraries, R packages, Java packages and so on.

It installs binaries. The conda build tool builds packages from source and conda install installs things from built conda packages.

Conda is the package manager of Anaconda, the Python distribution provided by Continuum Analytics. A skimpy line to describe Anaconda is this:

Anaconda is a python and R distribution. It aims to provide everything you need (python wise) for data science tasks.

Anaconda is a set of binaries that includes Scipy, Numpy, Pandas along with all their dependencies.

Scipy is a statistical analysis package.

Numpy is a numerical computing package .

Pandas is a data abstraction layer that exposes a way to merge and transform data.

Anaconda helps us by getting all of these together in one single shot.

The Anaconda binary is an installer that builds all of these packages and their dependencies in your system.

For more information on Anaconda, visit its official blog: https://anaconda.org/

Installing the easier way

Installing files can be a big major mess sometimes, but Anaconda is a lot more user friendly than you might think. I prefer Ubuntu, as installation just requires a couple of strong commands and a good network connection. It feels a lot easier. Here are the subsequent steps on installation of Anaconda.

(This whole installation process works on 64-bit computers alone).

Step 1: Download the Anaconda bash script

Download the latest version of the Anaconda installer bash script from their official website. It can be downloaded by running a curl command. If you don’t have curl installed in your system, install it by running the following command.

sudo apt-get updatesudo apt-get install curl

Go to the /tmp folder.

cd /tmp

After you have installed curl, run the following command using it:

curl -O https://repo.continuum.io/archive/Anaconda3-4.3.1-Linux-x86_64.sh

The setup usually consumes a few minutes as its size is approximately 500 MB. Please wait until the whole download process completes.

Anaconda install process

The screen shot attached was taken after the script has been downloaded. Make sure you have a strong connection or else download errors might occur.

Step 2: Verify the Integrity

To verify the data integrity of the installer, we use a cryptographic hash algorithm called SHA-2 (Secure Hash Algorithm).

sha256sum Anaconda3-4.3.1-Linux-x86_64.sh

A checksum will be generated in the next line after the command is executed.

Verifying data integrity using checksum

Step 3: Run the bash script

We are almost there. The package is downloaded and we just have to run the script by typing this command.

bash Anaconda3-4.3.1-Linux-x86_64.sh

A normal verification step asks whether you want to install Anaconda. Type yes so that the installation continues.

After running the bash script

Step 4: Installing the Cryptographic Libraries

This is a part of the prior process. The installer asks the user whether they want to install all the cryptographic libraries. Type in yes and you are good to go. Refer to the screen shot below. You’ll get details similar to it.

Cryptographic Libraries

Step 5: Confirm the Location

The last and final step is confirm the path to where you want to place all the Anaconda packages. After you have specified the path, press Enter and you are done! Anaconda will start working out its magic by installing all the essentials you’ll need!

Anaconda Path Set up

Step 6: Activate and Verify

In order to activate the installation, we should source the ~/.bashrc file by typing in the following command:

source ~/.bashrc

Verify your installation by using the conda command.

conda list

The output of all the packages available through the Anaconda installation is displayed.

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