Tag: New Feature

Refreshing your Tuva Datasets from Google Spreadsheets

Here is a scenario: You have imported a dataset from Google Spreadsheet into your repository on Tuva.

Dataset_Upload

Screenshot: Tuva Dataset titled “Census at School – Clean Data” has been imported from Google Spreadsheet

You make changes to your Google Spreadsheet, removing an attribute or adding 10 more rows of data. Till now, it was difficult to easily update and reflect these changes on your Tuva dataset.

Wouldn’t it be nice to be able to refresh your dataset directly from Tuva without have to import your Google Spreadsheet again?

Introducing Refresh

The Refresh feature allows you to easily update your Tuva Dataset so that any changes you make to your Google Spreadsheet will automatically be reflected in your dataset on Tuva.

Refresh_Dataset

Screenshot: Refresh button on Tuva Dataset page

Please note that the Refresh feature is only available for Tuva Datasets that you have imported from Google Spreadsheets. It is not available if you have imported the dataset from your computer.

Checkout the Refresh feature, and please feel free to share your comments and feedback on Tuva Discussions.

New Feature: Plotting Multiple Numerical Attributes on the Same Axis

When it comes to exploring data, how one organizes the dataset is incredibly important. Typically, each column should represent an attribute and each row should represent one observation. There are certain best practices one should follow when organizing a dataset, but we will leave that for another post.

There are times when the data is organized a bit differently, one needs to be able to plot multiple attributes on the same axis in order to meaningfully explore and analyze the data. Today, we are launching this functionality on Tuva.

Once you drag and drop an attribute on the x- or y-axis, you will be able to drag another attribute and drop it on the small rectangular box that appears below the attribute on that same axis. Here is a brief video showcasing this new feature in an example:

What do you think about this feature? Take it for a spin on the Climate Change or World Populations dataset, and post your comments, ideas, or feedback in the discussion thread.