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It is a good habit to use informative name, otherwise you may forget its content when come back from a holiday.
![conda jupyterlab conda jupyterlab](https://www.ceos3c.com/wp-content/uploads/2018/09/install-jupyterlab-001-300x200.png)
Here I named the env as r_3.5.1 so that I know what this specific env is create for. conda create -name r_3.5.1 -c r r-base=3.5.1 r-essentials Something like this: (base) I generated a specific R environment for R v3.5.1 (most recent version is R v3.6.1, but some of the packages I am using are not compatible with latest version). In addition, your command line will be preceded with ‘(base)’ to denote you are in the base conda environment. An asterisk will denote where you currently are, likely ‘base’. Creating conda environment for R and Pythonįirst, we can check the virtual environments that have been created by typing: conda info -envsĪll the conda environments have been created on the server should be listed. You can use these conda environments for different purposes, they can be program-specific (for running specific tools) or project-specific (for storing dependencies for a whole pipeline). Most importantly, it can also create compartmentalised computational environments to avoid mess-up in your computer/server when you want to test some new tools or scripts.
#Conda jupyterlab install
Basically, Anaconda is a software package manager that helps us to install programs and packages. Before we begin, please make sure you have conda or miniconda installed on the server.
![conda jupyterlab conda jupyterlab](https://user-images.githubusercontent.com/50761413/65488943-b7d6f480-dedc-11e9-8312-c7a4c2441153.png)
#Conda jupyterlab full
I believe if you end up reading this article, you must be a fan of it and want to take full use of it. It allows me to transfer between Python and R seamlessly. This is the tool I really enjoy using for data analysis. During the learning process, I learned about Jupyter Lab and impressed by its clean and simple design. I could fully utilise the computing power from remote servers by running Python on them. Recently, I started converting from R to Python as I found out that more and more my daily data analysis could be smoothly handled by Python, especially when the data size is getting huge and requiring considerable computing power.