What is Reader

In rubiscape, a reader is referred to as a dataset. Dataset is a collection of elements extracted from different sources that can be integrated into one. The datasets added can be shared across for different Projects. They are used to analyze and generate predictive results.

Types of Datasets

rubiscape supports a wide range of datasets that can be used to perform analysis. Availability of multiple types of datasets, makes sure that there are no limitations on what type of data you can use. The figure given below displays the types of data supported by rubiscape.

Figure: Types of Datasets

Adding a Dataset

A dataset is global and shared across the same workspace. Consider adding a dataset before creating a project. You can add a dataset from the supported data sources. The added datasets can be used in multiple projects.
To add a dataset, follow the steps given below.

  1. On the home page, click Create icon ().
    What would you like to do? page is displayed.

    (info)Note:

    You can create a dataset while working in workbook or workflow. To create dataset, click Create icon ( ) located in the top-right corner of the title pane.

  2. Hover over the dataset tile, and click Create Dataset.

    Figure: Creating Dataset

    A page with a list of available types of datasets is displayed.

  3. Click the dataset you that want to create. (In this example, Twitter dataset).
    Figure: Creating Dataset – Available Types

    Create Twitter Dataset page is displayed.

    Figure: Creating Dataset – Twitter Dataset

    The table given below describes the fields present on Create Twitter Dataset page.

    Table: Description of fields on Create Twitter Dataset page

    Field

    Description

    Name

    It is the name of your saved dataset.

    Description

    It is the description of your dataset.

    Hashtag #

    It is the list of twitter hashtags that you want to include in your dataset.

    Features

    They are the columns in the dataset. You cannot select, remove, or modify Features for twitter datasets.

    (info) Note:

    You can select, remove, or modify Features for datasets other than Social Media and API datasets.

  4. Enter the Name for your dataset.

  5. Enter the Description for your dataset

  6. Enter the Hashtag #. You can add multiple comma-separated hashtags.

  7. To create the dataset, click Create.
    The dataset is created and a confirmation message is displayed.

Importing a Dataset

You can import a previously exported dataset and use it in your projects.

(info) Note:

Dataset can be imported as a .DAT file only.

To import a dataset, follow the steps given below.

  1. On the home page, click Datasets.
    Recent Datasets for the current workspace are displayed.

  2. Click Import Dataset icon ( ).
     

    Figure: Datasets in current Workspace

    A message is displayed to confirm that if the dataset with the name you are importing already exists in the current workspace, the imported dataset will overwrite the existing dataset.

  3. To continue, click Yes.
    Open dialog box is displayed
  4. Browse to the location of dataset on your computer, select the required dataset, and then click Open.
    The dataset is imported and a confirmation message is displayed.
    The imported dataset is shown first in the list.

    Note:

    If you try to import a dataset which is not a .DAT file, it will prompt you to select a valid dataset file.

Searching a Dataset

You can search for a dataset by its name. It is especially helpful when the dataset list is long.

(info) Note:

Make sure you are in the correct Workspace, which includes the Dataset that you want to search.

To search a dataset, follow the steps given below.

  1. Open the workspace that includes your dataset. Refer to Changing Workspace.

  2. On the home page, click Datasets.
    Recent Datasets for the selected workspace are displayed.

  3. Type the dataset name in the Search field.

    As you start typing, the list of dataset names matching the search string is populated as shown in the figure below.

    Figure: Searching a Dataset

    (info) Note:

    Hover over a dataset and click the ellipsis ( ) to Edit, Export, or Delete the dataset.

Editing Dataset

After you add or import a dataset, you can edit it. For adding or importing a dataset, refer to Adding a Dataset or Importing a Dataset.

In Editing Dataset, you can,

  • Edit the name and description of the dataset

  • Select, remove, or modify the features of the selected dataset

  • Replace the selected dataset.

To edit a dataset, follow the steps given below.

  1. Open the Workspace that includes your dataset. Refer to Changing Workspace.

  2. On the home page, click Datasets.
    Recent Datasets for the selected workspace are displayed.

  3. Hover over the dataset you want to edit and click the ellipsis ( ), and then click Edit.
    Here, we consider CSV dataset.
    Figure: Editing a Dataset
    Update CSV Dataset page is displayed.
  4. Edit the required fields.

    (info)Note:

    While editing, you can select, remove, or modify Features for some type of datasets. To modify a feature, hover over the feature and click gear icon ( ).

  5. Click Update.
Figure: Editing fields of a Dataset

Exploring a Dataset

You can explore the existing dataset or the dataset that you have added or imported. For adding and importing a dataset, refer to Adding a Dataset or Importing a Dataset. Exploring the dataset shows you the information present in the selected dataset. It calculates the statistics of the data. In case of Numerical variables, this information is represented in the form of charts. For Categorical and Textual variables, the information is not available in the form of charts.

The explored dataset can be downloaded in the CSV format.

Figure: Feature Information in Explored Dataset

To explore a dataset, follow the steps given below.

  1. Open the Workspace that includes your dataset. Refer to Changing Workspace.

  2. On the home page, click Datasets.
    Recent Datasets for the selected workspace are displayed.

  3. Hover over a dataset you want to explore, and then click Explore.

    Figure: Exploring a Dataset

    The explored data is displayed in a page. The fields and statistics displayed depend on the explored data.

    (info) Note:

    To download the dataset in CSV format, click the download icon ().

Figure: Explored Dataset

Filtering a Dataset

You can filter each feature in the explored dataset to further sort and observe the results without downloading the dataset. You can filter Numerical, Categorical, Textual, and Date (Interval) variables using this feature.

Figure: Filtering Icon for the Features in Explored Dataset

(info) Note:

To filter a feature in the explored dataset, click the filter icon ().


To filter a feature in a dataset, follow the steps given below.

  1. Explore the dataset.
    The explored dataset is displayed in the Data page.

    Figure: Explored Dataset
  2. Click the filter icon () next to the Feature that you want to filter.

    Filtering dialog box for the Feature is displayed.

    Figure: Filtering Dialog Box
  3. Hover Condition to view the filtering conditions.
    Depending on the variable type: Numerical, Categorical, Textual, or Date (Interval), the filtering conditions are displayed.

    Figure: Filtering Conditions for Numerical Feature
  4. From the listed options, select the required condition.

  5. Enter the required numerical value, or text, or interval value in the Filter by Column Name

  6. Click Apply.

    Figure: Applying Filter

    In this example, for the feature Education, the filter condition selected is Greater than and the value entered in the Filter by Column Name field is 10. The result should display the rows which have values greater than 10.

    (info)Notes:

    • For Numerical and Date (Interval) variables, the filter conditions are Equals, Not Equals, Greater than, Less than, Between. The default condition is Equals.
    • For Categorical and Textual variables, the filter conditions are Equals, Not Equals, Contains. The default condition is Equals.
    • In case of Categorical and Textual variables, for filter condition Contains, the text that you enter in the Filter by Column Name field is not case-sensitive.

    The filtered dataset is displayed. The rows displayed for all the features in the dataset depend on the filtered data.

    Figure: Filtered Dataset

Exporting Dataset

You can export the dataset to save it in your system. You can use the exported dataset again by importing it into rubiscape.

To export a dataset, follow the steps given below.

  1. Open the Workspace that includes your dataset. Refer to Changing Workspace.

  2. On the home page, click Datasets.
    Recent Datasets for the selected workspace are displayed.

  3. Hover over a dataset you want to export and click the ellipsis, and then click Export.

    Figure: Exporting a Dataset

    The file is saved to your default download folder.

    (info)Notes:

    • Based on your browser settings, you might be prompted to select the location where you want to save your dataset. Select the destination folder, and then click Save.

    • The exported dataset is saved as a .DAT file on your system.

Deleting Dataset

To delete a dataset, follow the steps given below.

  1. Open the Workspace that includes your dataset. Refer to Changing Workspace.

  2. On the home page, click Datasets.
    Recent Datasets for the selected workspace are displayed.

  3. Hover over a dataset you want to delete and click the ellipsis, and then click Delete.

    Figure: Deleting a Dataset
    A message to confirm your action is displayed.
  4. To confirm, click Delete.
    The selected dataset is deleted from the rubiscape system and a confirmation message is displayed.


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