Ultimate guide for data sheet
Cleaning up your data table before generating a no-code app from it is crucial for ensuring the app's functionality and accuracy. Here's a step-by-step guide
Understand Your Data
Identify the display column
AppAlloy requires each table to have 1 display column. This column will be detected as the title of the data rows when it is shown as an item in the app.
The display can be the name of a product, the name of a customer, or a unique ID of the row. Whatever it is, if you choose the column as the key column, keep it the first column of your table. AppAlloy detects the first column as the display column.
Check the data types
Checking data types is essential because it ensures that the data in each column is in the correct format for processing, analysis, and integration.
For Google Sheets and local files as CSV., XLSX., or XLS., we recommend using 3 basic types:
For string data, we recommend the type "Plain text".
For numeral data such as price and quantity, we recommend the type "Number".
For date, time, and date & time data, we recommend the type "Date" or "Date time".
Handle missing data
Complete heading row
The row chosen to be your table's heading row must contain the headings for all columns.
These headings will become the field names within your AppAlloy app, so make sure they accurately reflect the type of data contained within each column (e.g., "Customer Name," "Product Price," "Order Date").
No merged cells
While merged cells might seem like a space-saving strategy, they can wreak havoc during the import process.
AppAlloy interprets merged cells as a single data point, which can lead to import errors and compromise the integrity of your information. Treat each cell as a separate entity containing a distinct piece of data.
Fulfill all cells
Strive to ensure complete data for each row within your table, even when it duplicates the data of the other cells from the same column. Missing information can lead to incomplete records within your app, potentially hindering the user experience.
Standardize Data
Consistent data formats are crucial for accurate analysis, reliable integration, and proper display. They prevent errors and ensure that data is interpreted correctly across different systems and tools.
Here are key types to format consistently:
Dates: Ensure all dates follow the same format to sort and filter correctly. Example: YYYY-MM-DD or YY-MM-DD, only choose 1 format for all data records.
Numbers: Use a consistent format for numerical values, including decimal places and thousands of separators. Example: 1,000.00 vs. 1000
Text: Standardize text formats, such as capitalization. Example: "John Smith" (capitalize names).
Currency: Use the number type for currency data. Example: $450 should be saved as 45 with type number only.
Normalize Data
Normalizing data is a process used to organize your data to reduce redundancy and improve data integrity. It involves breaking down a large table into smaller ones and defining relationships between them.
Create Separate Tables
If your table has too much information, consider breaking it into smaller, related tables. For example, one table for customers and another for their orders.
Maintain a well-organized data file by dedicating separate sheets to each distinct data table within your app. This simplifies the import process and keeps your data categorized for clarity.
Define tables relation
In each table, assign a unique identifier for each row in a table. This key ensures each record is unique.
For example, customer ID in the Customer table, and order ID in the Order table.
Each table is the main table of a specific set of data, and also the related table for reference information of another table data.
The main table contains the core information. Then each related table contains this core information as related data to the main table.
For example, the order table with has the Order ID, Product, and Quantity as the core information, and the Customer ID as the related data from the Customer table.).
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