Data Formats

Exporting Data

Exporting Data This option is limited to table view only. This feature is exclusively available for table view. You can retrieve data from the view by accessing the view menu and selecting the desired export format. Exporting allows you to sync and copy data to your device. The exported file includes all field values present in the view, excluding row comments. Choosing a Datalexing View for Export: Supported File Formats: - Comma-Separated Values (CSV) - Extensible Markup Language (XML) - JavaScript Object Notation (JSON) To learn more on how to export data click here

Filters

In the world of big and complex data, data formats and applying filters are essential tools for effectively organizing and filtering information. This technique in the DataLexing system aims to facilitate the process of searching for and accessing the required data quickly and accurately. In this guide, we will explore how to apply formats to data in DataLexing and their benefits in organizing and filtering data effectively. Filters Filters in DataLexing are aimed at refining and organizing your data to quickly find the necessary information. Filters enable the classification and precise arrangement of data based on specific criteria. They simplify the display of data by showing only the rows that meet the specified conditions, facilitating the management of vast datasets. Filters contribute to reducing clutter and facilitating data management tasks, whether by isolating data from a specific date, filtering rows that match specific conditions, or arranging data in a particular sequence. Filter Components Before applying the filter, you must choose three field values: 1. Column 2. Condition 3. Value. When adding the condition, it represents the Condition that must apply to the rows to be displayed. | Type | Description | | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Columns | drop down list of all the columns that exist in the table | | Condition | refers to a specific criterion or rule used to select or exclude data based on.Conditions changes based on column Types. to learn more about conditions click here | | Value | a specific value or pattern that you want to compare against in the chosen column. It represents the target or reference point for the condition | To Learn more in how to add custom filters to the view, click here Customizing filters using multiple conditions allows you to refine filtering criteria to display rows that meet specific requirements. Complex filtering can be achieved by linking multiple conditions together. For multiple filtering rules, two types of logic are used: "AND" and "OR". This method enables applying multiple filters at once using "AND" and "OR" conditions. "AND" Conditions: Use "AND" when all conditions need to be met. Use the "AND" logic to find items that meet all requirements within a set of filter rules. For example, to identify projects with the status 'In Progress' and 'Urgent' in a table, select "AND" before adding the second rule. "OR" Conditions: Use "OR" when any condition is sufficient. Use the "OR" logic to find items that meet at least one of a set of filter rules. For example, to identify projects containing 'Site Renaming' or 'User Portal' in a table, select "OR" before adding the second rule. To add another condition for refinement, click on the 'Add Condition' button. This creates another workspace containing all conditions connected by "AND" or "OR". Enabling and Disabling Multiple Filter Options: You can toggle the 'Disable All' checkbox to enable or disable all current fields within the page. This action does not remove filters from the table but only disables their application.

Filters Conditions

| Condition | What it Does | Applies On | Example | | ---------------------- | ------------------------------------------------------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- | | is | Matches exactly the specified value. | Text, Number,Check Box | Column: Quantity. Value: 10. Example: Selects records where the Quantity column is exactly 10. | | is not | Does not match the specified value. | Text, Number | Column: Price Value: 25.50 Example: Selects records where the Price column is not equal to 25.50. | | contains | Contains the specified substring. | Text, Number, Date | Column: Product Name Value: "out of stock" Example: Selects records where the Product Name column contains the substring "apple". | | doesn't contain | Does not contain the specified substring. | Text, Number, Date | Column: Description Value: "out of stock" Example: Selects records where the Description column does not contain the substring "out of stock". | | contain word | Contains the specified whole word. | Text, Number | Column: Category Value: "Electronics" Example: Selects records where the Category column contains the whole word "Electronics". | | doesn't contain word | Does not contain the specified whole word. | Text | Column: Product Name Value: "apple" Example: Selects records where the Product Name column does not contain the whole word "apple". | | length is lower than | Length of the text is less than the specified value. | Text | Column: Description Value: 10 Example: Selects records where the length of the text in the Description column is less than 10 characters. | | is empty | field is empty. | All Types | Column: Comments Example: Selects records where the Comments column is empty. | | is not empty | field is not empty | All Types | Column: Customer Name Example: Selects records where the Customer Name column is not empty. | | is date | Matches exactly the specified date | Date | Column: Order Date Value: 10/10/2021 Example: Selects records where the Order Date column equals 10/10/2021 | | is not date | Does not match the specified date. | Date | Column: Order Date Value: 31/12/2021 Example: Selects records where the Order Date column equals 31/12/2021 | | is today | Date field matches the current date. | Date | Column: Deadline Date Example: Select records where the deadline date is today | | is before today | Date field is before the current date. | Date | Column: Order Date Example: Select records where the Order Date before today | | is after today | Date field is after the current date. | Date | Column: Exam Date Example: Select records where the Exam Date after today | | is within days | Date field is within a specified number of days from today. | Date | Column: Invoice Date Value: 7 Example: Selects records where the Invoice Date column is within 7 days from today. | | is within weeks | Date field is within a specified number of weeks from today. | Date | Column: Order Date Value: 2 Example: Selects records where the Order Date column is within the upcoming 2 weeks. | | is within months | Date field is within a specified number of months from today. | Date | Column: Subscription Start Date Value: 3 Example: Selects records where the Subscription Start Date column is within the upcoming 3 months. | | is days ago | Date field is a specified number of days ago from today. | Date | Column: Last Login Date Value: 7 Example: Selects records where the Last Login Date column is 7 days ago from today. | | is months ago | Date field is a specified number of months ago from today. | Date | Column: Account Creation Date Value: 6 Example: Selects records where the Account Creation Date column is 6 months ago from today. | | is years ago | Date field is a specified number of years ago from today. | Date | Column: Membership Renewal Date Value: 2 Example: Selects records where the Membership Renewal Date column is 2 years ago from today. | | in this week | Date field falls within the past week. | Date | Column: Task Due Date Example: Selects records where the Task Due Date column falls within the current week from Sunday to Saturday. | | in this month | Date field falls within the current month. | Date | Column: Payment Date Example: Selects records where the Payment Date column falls within the current month. | | in this year | Date field falls within the current year. | Date | Column: Project Start Date Example: Selects records where the Project Start Date column falls within the current year. | | day of the month is | Day of the month matches the specified value. | Date | Column: Appointment Date Value: 15 Example: Selects records where the Appointment Date column corresponds to the 15th day of the month. | | is before date | Date field is before the specified date. | Date | Column: Event Start Date Value: "2024-06-01" Example: Selects records where the Event Start Date column is before June 1, 2024. | | is before or same date | Date field is before or the same as the specified date. | Date | Column: Transaction Date Value: May 20, 2024 Example: Selects transactions that occurred on or before May 20, 2023. | | is after date | Date field is after the specified date. | Date | Column: Transaction Date Value: May 20, 2023 Example: Selects transactions that occurred after May 20, 2023 | | is after or same date | Date field is after or the same as the specified date. | Date | Column: Transaction Date Value: May 20, 2024 Example: Selects transactions that occurred on or after May 20, 2024. | | higher than | Number value is Numerically higher than the specified value. | Number | Column: Sales Amount Value: 1000 Example: Selects records where the Sales Amount column is greater than 1000. | | lower than | Number value is Numerically lower than the specified value. | Number | Column: Discount Value: 0.1 Example: Selects records where the Discount column is less than 0.1. |

How to Apply Filters

filters are used to selectively display data based on specified criteria, enabling users to focus on specific subsets of information within a dataset. They can be applied to columns to hide rows that don't meet the criteria, making it easier to analyze and work with large sets of data. to learn more about Filters Click Here To Create New Filter: 1. Navigate to Filter icon 2. Click "Add filter" 3. Choose a column to apply the changes on 4. Choose a condition 5. Choose or write a value 6. Filter has been applied, only the rows with Category "Research" Are feasible now  1. To apply more than one condition 1. Click on Add Filter 2. To Apply an "And Condition" Choose "And" to Learn more Click Here 3. Follow Same Steps where you Choose column, condition, value now all the rows listed are the rows under the two conditions 2. To Add an "OR Condition" 1. Click on Add Filter 2. To Apply an "OR Condition" Choose "OR" *to Learn more Click Here* 3. Follow Same Steps where you Choose column, condition, value now all the rows listed are the rows under one of these conditions  you can apply more conditions and combine between "AND, OR" until you achieve your admire results 1. if you want to disable the conditions and get back to them later click on "Disable all" 2. To Delete on of the conditions Click the trash icon next to the condition

How to Export Data

in DataLexing you can export a grid views only. To export a view: 1. Click on the three dots (...) next to the view you want to export to access your view settings. 2. Select "Export View". 3. Select the desired file format for export. 4. When exporting as CSV. you have additional options to specify column delimiter and encoding. The default delimiter is comma. Other delimiters include semicolon (;), pipe (|), tab (), record separator (30), and unit separator (31). 5. Encoding converts data into a format supported. 6. This option will be automatically selected if the first row contains column headers. 7. Click "Export" to prepare the file data. 8. Click "Download" to download the data to your device.

How to Highlight your Important Data

Row coloring refers to the ability to apply different colors to rows within your Views to visually distinguish between different types of Data or to highlight important data. This feature helps users organize and prioritize their tasks or projects more effectively, as color coding can provide instant visual cues and improve the readability of the information displayed. To read more about row coloring click here To Add A new Color: 1. At the top Right of the table, you'll find the Coloring icon. Click on the Color icon. 2. First we will Navigate on how to Color Left Borders. to Learn more about loft borders, Click Here 1. first we will color based on single select values click on "Single Select" 2. Choose the Single Select Values Listed in this case we only have "Status" 3. Now you can see the left borders of each row has been colored based on the Status Coloring 4. you can remove the color by selecting the trash icon 5. Now We Will Color the rows based on "Conditions" click on "Conditions" to learn more about conditions Click Here 6. from the drop down list Choose a column to create the condition based on it 7. Choose a Condition 8. Write a value 9. Choose a Color 10. Now, any expense has an amount more than 200 will be highlighted in red 11. if you want to add more colors based on different conditions, you can follow the same steps. ​Second, we will Navigate on how to Background Color. to Learn more about Background Coloring click here 1. first we will color based on single select values click on "Single Select" 2. Choose the Single Select Values Listed in this case we only have "Status" 3. Now you can see the Background of each row has been colored based on the Status Coloring 4. you can remove the color by selecting the trash icon 5. Now We Will Color the rows based on "Conditions" click on "Conditions" to learn more about conditions click here 6. from the drop down list Choose a column to create the condition based on it 7. Choose a Condition 8. Write a value 9. Choose a Color 10. Now, any expense has an amount more than 200 will be highlighted in red 11. if you want to add more colors based on different conditions, you can follow the same steps 3. third, we will navigate on how to Combine Both Left Borders and Background Coloring to learn more about it Click Here 1. first, we will color the left borders based on a single select value in this case it will be “status” 2. to color the background of each row we will click on “add decorator” 3. Click "Background color" 4. You can choose either to color based on Condition or Single Select Value here we will filter based on a condition 5. Set the condition parameters Column, Condition, value now you can see that using both coloring method will help you in highlighting your most important work

Row Coloring

Coloring rows and fields plays a vital role in enhancing efficiency and facilitating navigation through diverse data. Using the Datalexing system, users relay on row coloring to facilitate identifying priorities, statuses, and various classifications at a glance. Record Highlighting Record highlighting involves adding color to each record either on the left border or by coloring the background of the field based on conditions or field value selection. Background Color of Field Users benefit from this feature to highlight records based on their importance or Single Select Value. ​Left Border Color Defining a specific range within the field or providing contrast between records or values. This type of coloring is used by users to highlight critical information without affecting the readability of the rest of the field. Combining Background Color and Left Border Users can take advantage of this feature by setting a background color for the field while separately defining the color of the left border. This means you can highlight the field itself with a background color while highlighting a portion of the field with the left border color. Difference Between Single select and Conditional Formatting Users utilize both Single select and conditional formatting to customize data display. Single Select allows them to assign a specific color to each value in the selection field, making it easier to identify different categories. Meanwhile, conditional formatting is used to apply colors based on specific conditions. By using data formatting and applying filters in Datalexing, users can now enhance the quality of data management and maximize its utility. These tools enable more precise and efficient filtering and organization, contributing to achieving work goals effectively and yielding tangible results. Therefore, applying filters should be an integral part of the data management strategy in Datalexing to enhance efficiency and effectiveness in work.

Importing Data

Importing a File This section explains how to import a file as a new table, not importing data from an external source into an existing table since the feature does not currently work. You can easily import your data from almost anywhere into Datalexing from a data file. Let's get started quickly! The import feature facilitates transferring your data in bulk from another platform. The following file types are supported when importing a table into your workspace: - Paste Table Data - Comma-Separated Values (CSV) - Extensible Markup Language (XML) - JavaScript Object Notation (JSON) To learn more on how to import data click here

How to Import Data

Pasting Table Data Datalexing supports various fields, each with its own set of formatting rules. Always ensure that the data you're inputting into Datalexing is properly formatted to get the correct content in the appropriate fields. 1. Within your offers list, click on + Create Table. 2. Enter a name for the new table. 3. Select Paste Table Data. You can copy cells from a data table and paste them here. 4. If the copied row contains column headers, the checkbox is selected by default. 5. Click Add Table. Importing a CSV File You can import an existing CSV file by uploading a CSV file containing tabular data. Within your table in the sidebar: 1. Click on + Create Table. 2. Enter a name for the new table. 3. Select Import CSV File. 4. Click on Choose CSV File to browse and upload a file from your computer. 5. Ensure the selected encoding and format from the dropdown menus. 6. Check the box to confirm that the first row contains column headers if you want to include the first row of your data table in the import, or uncheck it to discard column headers.