From Excel to Publication: How I Finally Stopped Hating My Own Figures

Excel is the lingua franca of data entry. It's intuitive, ubiquitous, and excellent for organizing tabular data. However, when it comes to creating figures for scientific publications, it often falls short.

Researchers often find themselves trapped in a dilemma: stick with Excel's limited plotting capabilities and risk producing "amateurish" figures, or invest hundreds of hours learning Python or R.

Plotivy offers a third way: keep using Excel for your data, but use AI to generate professional, code-backed visualizations.

Why Excel Struggles with Scientific Figures

While Excel is capable of basic charting, it lacks the specialized features required for high-impact publishing.

• Raster Export Issues: Exporting high-resolution (300+ DPI) images from Excel is notoriously difficult. Screenshots often result in pixelation that is unacceptable for print journals.
• Limited Customization: Fine-tuning elements like error bar caps, tick mark direction, or specific font kerning can be frustrating or impossible.
• Reproducibility: Excel figures are often the result of manual clicks. If you need to recreate a plot six months later with new data, you have to remember every formatting step you took.

You don't need to abandon Excel entirely. Use it for what it's good at—data organization—and let Plotivy handle the visualization.

Step 1: Data Preparation in Excel

Ensure your data is "tidy":

• Each variable should have its own column.
• Each observation should have its own row.
• Include a header row with clear, descriptive names (e.g., "Time_min", "Absorbance_AU").

Step 2: Import to Plotivy

Simply drag and drop your `.xlsx` or `.csv` file into the Plotivy interface. The AI will parse the file structure automatically.

Step 3: Natural Language Plotting

Describe the figure you need. Plotivy translates your request into Python code (using libraries like Matplotlib), giving you the power of a programming language without the syntax struggle.

Example Prompt: "Create a scatter plot of 'Time_min' vs 'Absorbance_AU'. Add error bars using the 'StdDev' column. Fit a kinetic curve to the data and style the plot for a chemistry journal."

Step 4: Export Vector Graphics

Download your figure as an SVG or PDF. These formats are resolution-independent, ensuring your lines and text remain crisp whether viewed on a phone screen or printed on a poster.

The Best of Both Worlds

By bridging the gap between Excel's ease of use and Python's visualization power, you can dramatically improve the quality of your figures.

You get the reproducibility and quality of code-based plotting, with the familiarity and speed of your existing spreadsheet workflow.