# Running a JupyterLab Notebook
## Use Cases for a Single JupyterLab Instance
* R needs to run in a regular notebook and for downstream analysis
* If directly interacting with the database/ dataset, it is recommended that you either 1) use Python and/ or 2) use Spark for extracting the data that is relevant for the downstream analysis
## General “Recipe” for Utilizing Single Instance JupyterLab Notebooks
1. Create a DX JupyterLab Notebook so that it will automatically save onto the Trusted Research Environment. You can do so by selecting these 2 different options:
1. Option 1 is from the Launcher:
b. Option 2 is from the DNAnexus Tab:
2. Start writing your JupyterLab Notebook. Select which kernel you are going to use (options will vary depending on the Image you selected in set up).
3. Download packages and save the software environment as a snapshot
1. Download Packages
```
pip install ___ #python
install.packages() #R
```
b. Save the Snapshot of the environment
4. Start writing your code.
1. Load Packages
import ____ #python
library() #R
b. Download or Access data files to the JupyterLab environment
```
%%bash
#option 1: dx download
dx download "PATH TO FILE"
#option 2: dx fuse
data = pd.read_csv("/mnt/project/PATH.csv")
```
c. Import the data
```
import ___ as pd
NAME = pd.read_csv("PATH.csv")
```
d. Then, perform the analysis for your data
e. Upload results back to Project Space
```
%%bash
dx upload FILE --destination /your/path/for/results
```
5. Save your DX Jupyterlab Notebook
## Opening Notebooks from Project Storage
* Notebooks can also be directly opened from project storage
* When you save in JupyterLab, the notebook gets uploaded to the platform as a new file. This goes back to the concept of immutability.
* The old version of notebook goes into .Notebook\_archive/ folder in project.
