Data arrangement typically refers to organizing and structuring data in a systematic way to make it more manageable, understandable, and useful for analysis .The specific steps and methods you need to follow depend on the type of data you're dealing with and your objectives. Here's a general guide to help you with data arrangement:

1. Understand Your Data:

· Determine the type of data you have: Is it numerical, categorical, text, or time-series data?

· Identify the variables and their meanings: Understand what each column or field in your dataset represents.

· Identify missing or incomplete data: Address missing values to avoid bias in analysis.

2. Clean and Pre-process Data:

· Remove duplicates: Check for and remove any duplicate entries in your dataset.

· Handle missing data: Decide how to deal with missing values—either by imputing, removing, or using specialized techniques.

· Convert data types: Ensure that data types are appropriate for analysis (e.g., converting strings to numbers, dates to appropriate date formats).

· Normalize or scale data: If needed, scale numerical variables to have a consistent range (e.g., using Z-score normalization).

3. Choose a Suitable Data Structure:

· For tabular data: Use spreadsheets or databases to store data in rows and columns.

· For unstructured data: Store text, images, audio, or video in appropriate formats or databases.

· For time-series data: Organize data in chronological order, considering time intervals.

4. Structuring Categorical Data:

· One-hot encoding: Convert categorical variables into binary columns, with each category as a separate column.

· Label encoding: Assign numerical labels to categories, suitable for ordinal categories.

· Ordinal encoding: Assign numerical values to categories based on their order or importance.

5. Structuring Numerical Data:

· Binning: Group numerical data into bins or intervals for analysis.

· Discretization: Convert continuous variables into discrete categories.

6. Time-Series Data:

· Ensure consistent time intervals: Fill in missing time points if necessary to maintain a consistent interval.

· Handle irregular time intervals: Use techniques like resampling or interpolation.

7. Visualization:

· Create plots and charts: Visualize data to understand patterns, trends, and relationships.

· Select appropriate visualization types: Choose the right chart type based on the type of data and the insights you want to convey.

8. Data Analysis:

· Perform statistical analysis: Use descriptive and inferential statistics to gain insights.

· Apply machine learning algorithms: Train models to predict outcomes, classify data, or find patterns.

· Explore correlations: Identify relationships between variables using correlation matrices or scatter plots.

9. Documentation:

· Document your data arrangement process: Maintain clear documentation of the steps you've taken, transformations applied, and any insights gained.

· Keep track of changes: Update documentation as you clean, pre-process, and analyze data.

Remember that data arrangement is not a one-size-fits-all process. It depends on the nature of your data and your specific objectives.

Hello Kiya Adare, I've used meta a dos based program, plus also the revman software used by the cochrane library, many years ago to add one study to an existing meta analysis. Somewhere I have a book on systematic reviews by Prof. Paul Glasziou, however I believe when we had people stay someone shifted my pile of books and I don't know where it is now. Do you want me to look for it? Thanks

Thank you very much for your answer, I really appreciate your effort. thank you very much again