“I saw the need to be able to demonstrate what I can do with my skills and I thought Workspace gave that opportunity... [so] I started playing around with it.”
- Salman, behavioural science practitioner
If you edit or create a new R workspace, it will open the workspace files in RStudio, the most popular tool for doing data science or statistical analyses. If you edit or create a new Python workspace, you'll be able to edit and execute the source files in JupyterLab.
Only you can see all the workspaces that you own and their contents. In other words, your work is completely private unless you decide to publish your workspace. It is currently not possible to share your workspace with other people to collaborate, but you are able to share workspace publications with others (see Publications section for more).
If the total size of all the files in your workspace exceeds 5GB, you will no longer be able to access your workspace. If you unintentionally ended up in this situation and want to continue, let us know and we'll help you out.
All your work is automatically saved in the cloud. This means your workspace experience can be free of worries; all you need to do is code. If you close and re-enter the workspace at a later point in time, all your changes will still be in place.
One of the first steps when starting a new workspace is typically getting data into your session for further analysis. This data can come from various places and in various forms, like text files, SQL databases, files on an AWS S3 bucket, etc. This section explains how to import data from a CSV file, short for comma-separated values.
After getting the dataset (e.g. from a public dataset on the internet), you need to upload the dataset to your workspace. In JupyterLab, you can use the ⬆ icon in the the sidebar. In RStudio, use the
Upload button in the toolbar of your file explorer.
To import a CSV file into your Python session, you can use the
read_csv() function in
pandas. Check out our Workspace templates for examples to see how it's done.
To import a CSV file into your R session, you can use base R's
read.csv() function or the
readr::read_csv() function from the tidyverse. Again, you can check out our Workspace templates for examples to see how it's done.