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Analyze data in a project
Projects are where you work with data on Redivis. In a project you can query, merge, reshape, and analyze any datasets that you have access to, all from within your web browser.
In a project, you can construct reproducible data transformations and analyses, and share and collaborate with your peers in real time.
Add a dataset to a new or existing project from any Dataset page where you have "Data access" by clicking the Analyze in project button.
Within projects you can navigate between entities on the left side of the screen, and inspect them further on the right panel. You can inspect your dataset further by clicking on any table to see its cells and summary statistics.
To add more data to this project you can click the Add dataset button in the top left of the project toolbar.
You can find this project later by going back to your workspace.
Transforming tables is a crucial step in working with data on Redivis. Conceptually, transforms execute a query on source table(s), whose results are materialized in a new output table. In most cases you'll want to use transforms to reshape your data to contain the information you're interested in, before analyzing that table in a notebook or exporting it for further use.
To create a Transform, select a table in this dataset and click the +Transform button. You can get started here building a query through the point and click interface or writing SQL code by adding a SQL step.
For all transforms you will need to select which variables you want to keep in your output table. The rest of the steps are up to you. Some common operations you can get started with include:
Once you've built your query, execute it by clicking the Run button in the top right of the transform. This will create a new output table where you can inspect the output of your query by clicking on the table beneath the transform in the map and making sure it contains the data we would expect.
From here you can create a new transform from this output table to continue reshaping your data, or go back to your original transform to make changes and rerun it.
As you become more familiar with transforms, you can start doing more advanced work such as geospatial joins, complex aggregations, and statistical analyses.
Once you have a table you're ready to analyze, you can select any table and click the + Notebook button to create a notebook that references this table.
Notebooks are available in Python and R, as well as Stata or SAS (with a corresponding license). When first creating your notebook, it will start up and automatically pull in your table (or a subset of your table if it is large).
Redivis notebooks come pre-installed with common libraries in the data science toolkit, but you can also customize the notebook’s dependencies and startup script to create a custom, reproducible analysis environment that meets your needs.
From here it’s all up to you in how you want to analyze and visualize your data. Once you’ve finalized your notebook, you can easily export it in different formats to share your findings!
You can share your in-progress work or finished results with collaborators by sharing this project.
Researchers can work side by side in this project in real-time. Leave comments to communicate, and see a visual cue for what each person is working on. You can even collaborate within a running notebook at the same time.
If any of the data in your project is restricted, your collaborator must also have access to those datasets in order to view their derivatives within your project.
Augment your data analysis in Redivis by uploading your own datasets, with the option to share with your collaborators (or even the broader research community).
Last modified 1mo ago