File Merger with Metadata Integration with Dashboard for analysis
Budget: $30 – $250 USD
I need combine final merged file with the meta data tab and dashboard to make all need analysis qualitative and quantitative
So, I have two files that I need to combine into one while keeping everything as it is—no data changes, no overwriting, nothing lost. The first file is my reference file; it’s already structured the way I want, with specific formatting, metadata links, and key columns that define how things should look. The second file is more like a supplementary dataset—it has additional or overlapping data, but the structure doesn’t fully match the first file, so it needs to be aligned.
The idea is to create one single file that includes all rows and columns from both files. Each row should clearly show where it came from—whether it’s from the first file or the second. But even though everything will be in one place, I still need to be able to analyze the files separately, as well as together. For example, I want to filter all rows from the second file without losing the ability to analyze how the two files connect.
Here’s the technical side of what’s needed:
• Everything in both files stays exactly the same—formatting, colors, font styles, conditional rules. If there’s color-coding in one file or specific alignment, it stays as-is. This includes things like text being centered, dates/times formatted consistently (e.g., Oct 01, 24 14:00:00), and numbers keeping their original formats (e.g., percentages, currencies).
• The second file’s columns need to be aligned to match the structure of the first file. If the second file is missing columns, placeholders should be added—like “NA” for text, 0 for numbers, or a default date like “Jan 01, 00 00:00:00.”
• I need a few extra columns added in the final file to make the analysis easier:
• A column that shows which file the row came from (File A or File B).
• A column to flag duplicates—both exact matches and rows that are similar (like matching times but slightly different content).
• Flags for rows that are in one file but not the other.
• A column summarizing themes or patterns in the data (e.g., identifying key sources like “Google” or “ChatLog”).
• A way to show correlations or links between rows, like shared metadata or similar timestamps.
The first file also has a metadata tab, which links extra information to specific rows. That metadata needs to be pulled into the merged file wherever it applies. If a row from the second file matches something in the metadata tab, it should get the same metadata added. If there’s no match, the metadata column can just have “NA.” And the metadata file itself needs to be cleaned up—structured properly and double-checked to ensure all calculations are accurate.
Once the two files are merged, I need to be able to analyze them in different ways. For example:
• I should be able to look at all rows together to identify duplicates, gaps, or trends.
• At the same time, I should be able to filter rows by their original source (File A or File B).
• The final file needs to support advanced filtering, sorting, and analysis without breaking the original formatting or structure of the files.
At the end of the day, the goal is to have one well-organized file that’s easy to work with, keeps everything intact, and allows me to do both independent and joint analysis of the two files. It’s about making sure the data stays clean, organized, and technically sound while giving me all the tools I need for a detailed review.
So, I have two files that I need to combine into one while keeping everything as it is—no data changes, no overwriting, nothing lost. The first file is my reference file; it’s already structured the way I want, with specific formatting, metadata links, and key columns that define how things should look. The second file is more like a supplementary dataset—it has additional or overlapping data, but the structure doesn’t fully match the first file, so it needs to be aligned.
The idea is to create one single file that includes all rows and columns from both files. Each row should clearly show where it came from—whether it’s from the first file or the second. But even though everything will be in one place, I still need to be able to analyze the files separately, as well as together. For example, I want to filter all rows from the second file without losing the ability to analyze how the two files connect.
Here’s the technical side of what’s needed:
• Everything in both files stays exactly the same—formatting, colors, font styles, conditional rules. If there’s color-coding in one file or specific alignment, it stays as-is. This includes things like text being centered, dates/times formatted consistently (e.g., Oct 01, 24 14:00:00), and numbers keeping their original formats (e.g., percentages, currencies).
• The second file’s columns need to be aligned to match the structure of the first file. If the second file is missing columns, placeholders should be added—like “NA” for text, 0 for numbers, or a default date like “Jan 01, 00 00:00:00.”
• I need a few extra columns added in the final file to make the analysis easier:
• A column that shows which file the row came from (File A or File B).
• A column to flag duplicates—both exact matches and rows that are similar (like matching times but slightly different content).
• Flags for rows that are in one file but not the other.
• A column summarizing themes or patterns in the data (e.g., identifying key sources like “Google” or “ChatLog”).
• A way to show correlations or links between rows, like shared metadata or similar timestamps.
The first file also has a metadata tab, which links extra information to specific rows. That metadata needs to be pulled into the merged file wherever it applies. If a row from the second file matches something in the metadata tab, it should get the same metadata added. If there’s no match, the metadata column can just have “NA.” And the metadata file itself needs to be cleaned up—structured properly and double-checked to ensure all calculations are accurate.
Once the two files are merged, I need to be able to analyze them in different ways. For example:
• I should be able to look at all rows together to identify duplicates, gaps, or trends.
• At the same time, I should be able to filter rows by their original source (File A or File B).
• The final file needs to support advanced filtering, sorting, and analysis without breaking the original formatting or structure of the files.
At the end of the day, the goal is to have one well-organized file that’s easy to work with, keeps everything intact, and allows me to do both independent and joint analysis of the two files. It’s about making sure the data stays clean, organized, and technically sound while giving me all the tools I need for a detailed review.