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# Interpreting Data for Plotting
Philosophy of visualizing data
A useful tool is using the acronym **PCC** to enforce legibility of your data.
```mermaid
flowchart LR
A[Purpose] --> B[Composition]
B --> C[Color]
C --> |Clarify| A
```
Whether we are preparing figures for a lab report or a research paper, these three elements should always be applied when presenting data. They ensure that our figures are clear, effective, and convey the intended message to our audience.
- Purpose -> explain a process, compare of contrast, show a change or establish a relationship
- Composition -> How do you arrange to components of the plot to clearly show the purpose
- Color -> Using contrasts and colors to highlight important elements in a figure.
We may re-iterate a figure 2 or 3 times until the figure shows the data we want to present is clear.
*Remember:* Data don't lie and neither should your figures, even unintentionally.
## Syntax and semantics in Mathematics - The meaning of our data
In the English language, grammar defines the syntax—the structural rules that determine how words are arranged in a sentence. However, meaning arises only through semantics, which tells us what the sentence actually conveys.
Similarly, in the language of mathematics, syntax consists of the formal rules that govern how we combine symbols, perform operations, and manipulate equations. Yet it is semantics—the interpretation of those symbols and relationships—that gives mathematics its meaning and connection to the real world.
As engineers and scientists, we must grasp the semantics of our work—not merely the procedures—it is our responsibility to understand the meaning behind it. YouTube creator and rocket engineer Destin Sandlin, better known as SmarterEveryDay, illustrates this concept in his video on the “backwards bicycle,” which demonstrates how syntax and semantics parallel the difference between knowledge and understanding.
## Purpose - Why?
Does the figure show the overall story or main point when you hide the text?
Starting with the most important aspect of a figure is the purpose. What do you want to show? Why are we showing this? What is so important? These questions will help us decide on what time of plot we need. There are many types of plots and some are better for different purposes.
Often in engineering you find yourself **comparing** or **contrasting** or **show a change** between sets of data. For these cases you should use either a *line chart* or a *scatter plot*. This is often used when plotting mathematical function.
In a lab report you may find yourself **explaining a process**. For this you may want to use a: *flowchart*, *diagram*, info graphic, illustration, *Gantt chart*, timeline, .etc.
There are many other types of plots that you can choose from so it can be useful to think about who you're sharing your data with. This may be
- Colleague/Supervisor .etc
- Research conference
- Clients (may not always be technical professionals).
## Composition - Making good plots
Can you remove or adjust unnecessary elements that attract your attention?
Composition refers to how you choose to format your plot — including labeling, gridlines, and axis scaling.
Often, the main message of a figure can be obscured by too much information. To improve clarity, consider removing or simplifying unnecessary elements such as repetitive labels, bounding boxes, background colors, extra lines or colors, redundant text, and shadows or shading. You can also reduce clutter by adjusting or removing excess data and moving supporting information to supplementary figures.
If applicable, be sure to follow any additional formatting or figure guidelines required by your target journal.
<img src="image_1760986811788.png" width="500" center="true">
## Color - Highlight
Does the color palette enhance or distract from the story?
Similarly to composition using color or the absence thereof (gray scale) can help you draw the attention of the read to a specific element of the plot.
Here is an example of how color can be used to enhance the difference between the private-for-profit.
<img src="image_1760986545639.png" width="500">
## In the end
#### Data don't lie
And neither should your figures, even unintentionally. So it's important that you understand every step that stands between your raw data and the final figure. One way to think of this is that your data undergoes a series of transformations to get from what you measure to what ends up in the journal. For example, you might start with a set of mouse weight measurements. These numbers get 'transformed' into the figure as the vertical position of points on a chart, arranged in such a way that 500g is twice as far from the chart baseline as 250g. Or, a raw immunofluorescence image (a grid of photon counts) gets transformed by the application of a lookup table into a grayscale image. Either way, exactly what each transformation entails should be clear and reproducible. Nothing in the workflow should be a magic "black box."
#### Follow the formatting rules
Following one set of formatting rules shouldn't be too hard, at least when the journal is clear about what it expects, which isn't always the case. But the trick is developing a workflow that is sufficiently flexible to handle a wide variety of formatting rules — 300dpi or 600dpi, Tiff or PostScript, margins or no margins. The general approach should be to push decisions affecting the final figure format as far back in the workflow as possible so that switching does not require rebuilding the entire figure from scratch.
#### Quality
Unfortunately, making sure your figures look just the way you like is one of the most difficult goals of the figure-building process. Why? Because what you give the journal is _not_ the same thing that will end up on the website or in the PDF. Or in print, but who reads print journals these days? The final figure files you hand over to the editor will be further processed — generally through some of those magic "black boxes." Though you can't control journal-induced figure quality loss, you can make sure the files you give them are as high-quality as possible going in.
#### Transparency
If Reviewer #3 — or some guy in a bad mood who reads your paper five years after it gets published — doesn't like what he sees, you are going to have to prove that you prepared the figure appropriately. That means the figure-building workflow must be transparent. Every intermediate step from the raw data to the final figure should be saved, and it must be clear how each step is linked. Another reason to avoid black boxes.
Checklist
- [ ] Select appropriate type
- [ ] Labels
- [ ] Grid
- [ ] Axis
- [ ] Clarity
## Problem 1:
## Problem 2:
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