slides.qmd

---
title: "The theory and practice of effective scientific figures"
subtitle: "CORE Workshop - Ocean Sciences"
author: "Alexa Fredston"
date: "February 14, 2024"
format: 
  revealjs:
    slide-number: true
editor: visual
---

## What am I looking at, exactly?

I made these slides "programmatically" (in code) using a script type called [Quarto](https://quarto.org/docs/presentations/revealjs/) in the RStudio IDE.

You can access the slides or the code easily in the [GitHub repository](https://github.com/afredston/core-figures), or navigate to [github.com](https://github.com/), look up the username **afredston**, and click on the pinned repository entitled "core-figures".

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## Why did you do this, Alexa?

Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it's actually extremely useful for visual communication of scientific results, *especially* at preliminary stages. Quarto:

::: incremental
-   Is designed to integrate coding and formatting, which allows you to clearly and heavily annotate your figures / tables / results as you generate them. I often make these files to explain complicated methods and preliminary results to myself and collaborators.
:::

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## Why did you do this, Alexa?

Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it's actually extremely useful for visual communication of scientific results, *especially* at preliminary stages. Quarto:

::: incremental
-   Generates stuff (figures, tables, etc) programmatically: if you update the data / analysis / model / whatever, the stuff will all automatically update. This saves you time if you frequently re-visualize results. If you tell Quarto to generate text results from data (which is easy to do), it can also automatically update in-text stats.
:::

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## Why did you do this, Alexa?

Great question. Partly, I wanted an excuse to use Quarto. Mostly, I wanted to show you this because it's actually extremely useful for visual communication of scientific results, *especially* at preliminary stages. Quarto:

::: incremental
-   Allows you to write up results -- and potentially entire papers -- that are much less prone to error (and wasting time) updating and copy-pasting statistics, tables, and figures.
:::

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## None of that made sense

Don't worry about it. The main goal today is to talk about figures! I just wanted to explain how I make mine, and why the slides look the way they do.

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## Today's presentation

How to get from data and results to a figure

Some color theory

When to use figures or tables

Examples are (mostly) from my own papers, but this is for two reasons: (1) I wanted to be able to critique them freely and (2) I ran out of time to find other people's cooler figures

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## Is this really worth my time?

Lots of people -- *lots of people* -- only look at your title, abstract, and figures

It is extremely obvious to reviewers who spent time on their figures and who didn't

Your papers (and talks, and meetings) will be *so much easier for people to follow* if there are great figures

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## Know your message

Figures are like papers: they should really only contain one idea

If you can't summarize the main point of a figure in a single, relatively brief sentence, it may have too much information

Panels are OK for dividing up results but just be sure they are clearly delineated and annotated

It can take a while to hone in on this message, but you should know what it is by the time you are writing up final results

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##Example: one message, one figure

![Burgess *et al.* 2023](burgess_2023.png)

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## Use your imagination

Making and customizing beautiful and creative figures is much easier than it used to be

Don't be constrained by the default way your personal research workflow generates plots

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## Example: simple but effective

![Freeman *et al.* 2018](freeman_2018.png)

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## Plot types {.smaller}

Work backward from what you want to communicate

Some key questions to ask yourself:

-   What quantities do you want to visualize -- absolute values, relative values, grouped values, etc.?
-   What summary statistics (e.g., means, trends) do you want to emphasize? How can you do this while still plotting the raw data?
-   Do you need to plot your data over space? If so, does a map or an abstraction of space work better?
-   Do you need to plot your data over time?
-   How will you visualize uncertainty or variation in your data?

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## Stay close to the data

Some journals **require** you to plot raw data

*Be very hesitant about omitting outliers*

Violin plots are the new box-and-whisker plots

![Fredston *et al.* 2021](fredston_2021.png)

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## How do I match my idea to a figure?

Google is your friend, but you need to know how to describe what you're looking for

For example, "best plot types for two continuous and one categorical variable", or "how do I visualize relative change over time for many groups at once"

Try [from data to viz](https://www.data-to-viz.com/)

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## Example: "ridge" plots that show density by group

```{r}
library(ggridges)
library(ggplot2)
library(palmerpenguins)

ggplot(penguins, aes(x = bill_length_mm, y = species)) +
  geom_density_ridges()
```

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## Example: "ribbon" plots for uncertainty

![Exploratory plot by Alexa](stat_ribbon.png)

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## Example: "tile" plots that abstract space

![Exploratory plot by Alexa](tileplot.png)

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## Conceptual figures

I am a huge fan of the conceptual diagram, which can summarize a huge amount of what you did in a simple, appealing format

Some ways to use them:

-   Describing an experimental procedure
-   Outlining a model workflow
-   Summarizing a collaborative process
-   Characterizing a complicated dataset

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## Example: conceptual diagram

![Maureaud *et al.* 2024](maureaud_2024.png)

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## Example: conceptual diagram

![Halpern *et al.* 2022](halpern_2022.png)

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## Example: conceptual diagram

![Brodie *et al.* 2023](brodie_2023.png)

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## Annotation

Don't hesitate to draw on and with your plots!

Remember that point size, shape, color, and shading can all communicate for you

You can plot icons from [The Noun Project](https://thenounproject.com/) or [biorender](https://www.biorender.com/) or silhouettes of taxa from [phylopic](https://www.phylopic.org/)

I often do this in a software called [Inkscape](https://inkscape.org/) (a little buggy on Macs though), which is also great for resizing figures to meet journal guidelines

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## Figure captions

Should be as short and straightforward as possible while still explaining everything in the figure

Try to avoid acronyms and jargon. The more intuitively you design your figure, the less you need to explain here

Do not make readers hunt through your manuscript to find explanations of things being plotted that are not in the figure caption!!

If your figure caption is getting incredibly long that is a sign your figure may need editing

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## Maps: additional considerations

What to plot depends on what you want the viewer to take away -- could be bathymetry, country boundaries, major roads or rivers, model outputs... but don't try to put more than one or two of these on the same map

Choose your projection to align with the areas you want to be most visible

Do not assume people know where your study region is; consider including a zoomed-out map situating it on the continent/globe

Be aware it is extremely difficult to plot uncertainty on a map. People often plot multiple maps side-by-side to get around this

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## Example: study areas

![Fredston *et al.* 2023](fredston_2023_map.png)

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## Example: heatmaps ("chloropleths")

![New York Times](nyt.jpg)

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## Color choices {.smaller}

Use color only when you need color; remember people will often print papers in black and white

You can find color palettes or ramps online and copy over their IDs (look for "hex" or "RGB" codes)

Match colors to your variable types:

-   Continuous variables --\> continuous color gradients
-   Ordered variables --\> discrete color gradients
-   Categorical variables --\> discrete color palettes

*Make your plots colorblind-friendly.* Somewhere between 5-10% of the general population has some limitation to their color vision. [This site](https://mk.bcgsc.ca/colorblind/palettes.mhtml#page-container) has some great palettes for continuous and categorical data that are colorblind-friendly, and you can read a simple explainer [here](https://davidmathlogic.com/colorblind/#%23D81B60-%231E88E5-%23FFC107-%23004D40).

Use [colorbrewer](https://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3) to explore color choices, especially for maps

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## Matching scales to variables

![From the Datawrapper blog](color_scales.png)

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## Example: Barbie plot

![Fredston *et al.* 2023](fredston_2023_barbie.png)

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## More on color choices

Be mindful of the interpretation of your colors. For example, red colors are often interpreted as literally hotter, and blues are often interpreted as literally colder; green colors are often used for vegetation or productivity

If you aren't using a variable that is commonly associated with a color try to visualize it with a neutral color that won't be confusing

Be consistent across plots and use the same colors for the same variables in all your figures

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## Example: color choices

![Antão *et al.* 2020](antao_2020.png)

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## Example: using color over two dimensions

![Bivariate color ramp](https://www.joshuastevens.net/images/js_bivariateMix.gif)

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## Interactive plots, gifs, etc.

People increasingly make interactive plots to go with their papers; see [Gavin Fay's example](https://gavinfay.shinyapps.io/ebfm-ne-teaching-eg/)

You can also generate gifs of your figures to embed in talks, which is really helpful for showing (for example) things evolving through time

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## You didn't talk about tables

Tables in the main text of a paper can often be replaced by figures that show extra information, like variance in parameter values

However, sometimes tables and text boxes are important for summarizing descriptive information

I use them for reporting lots of p-values and models in supplementary information

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## Example: a table I begrudgingly made

![Fredston *et al.* 2023](fredston_2023_table.png)

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## More resources for data visualization

[Ten Simple Rules for Better Figures](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161295/)

[Journal of Biogeography post](https://journalofbiogeographynews.org/2020/07/15/figures-the-art-of-science/), a reminder to always follow the journal guidelines

[Betsy Mason article](https://knowablemagazine.org/content/article/mind/2019/science-data-visualization), full of examples of how scientists can make better visualizations

[Claus Wilke's book](https://clauswilke.com/dataviz/), a deep dive into data visualization

[The Data Visualization Catalogue](https://datavizcatalogue.com/), for browsing plot options

[Data Imaginist](https://www.data-imaginist.com/posts/2018-05-30-scico-and-the-colour-conundrum/index.html), on color theory