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index.qmd
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---
title: "Data Visualization & Communication"
subtitle: "Master of Environmental Data Science (MEDS)"
description: "Winter 2025"
title-block-banner: false
toc: false
---
```{r}
#| eval: true
#| echo: false
#| fig-align: "center"
#| out-width: "85%"
#| fig-alt: "An extended version of the classic R4DS schematic from Grolemund & Wickham, with environmental data science, communities, and communication added."
knitr::include_graphics("images/horst-eco-r4ds.png")
```
:::{.gray-text .center-text}
*Artwork by [Allison Horst](https://allisonhorst.com/)*
:::
## Course Description
Effectively communicating your work in a responsible, accessible and visually-pleasing way is a central part of data science. This course will focus on the basic principles for effective communication through data visualization and using programmatic tools and workflows for creating and sharing data visualizations with diverse audiences.
::: {.grid}
::: {.g-col-12 .g-col-md-7}
By the end of this course, learners should be able to:
- Identify which types of visualizations are most appropriate for your data and your audience
- Prepare (e.g. clean, explore, wrangle) data so that it's appropriately formatted for building data visualizations
- Build effective, responsible, accessible, and aesthetically-pleasing visualizations using the R programming language, and specifically `{ggplot2}` + ggplot2 extension packages
- Write code from scratch and read and adapt code written by others
- Apply a DEI (Diversity, Equity & Inclusion) lens to the process of designing data visualizations
- Assess, critique, and provide constructive feedback on data visualizations
:::
::: {.g-col-12 .g-col-md-5}
```{r}
#| eval: true
#| echo: false
#| out-width: "80%"
#| fig-align: "center"
knitr::include_graphics("images/EDS240-hex.png")
```
:::
::::
## Teaching Team
<br>
::: {.grid}
::: {.g-col-12 .g-col-md-4}
::: {.center-text .body-text-l}
**Instructor**
:::
```{r}
#| eval: true
#| echo: false
#| fig-align: "center"
#| out-width: "50%"
knitr::include_graphics("images/sam.png")
```
::: {.center-text}
[**Sam Shanny-Csik**]{.teal-text}
**Email:** [[email protected]](mailto::[email protected])
**Learn more:** [samanthacsik.github.io](https://samanthacsik.github.io/){target="_blank"}
:::
:::
::: {.g-col-12 .g-col-md-4}
::: {.center-text .body-text-l}
**TA**
:::
```{r}
#| eval: true
#| echo: false
#| fig-align: "center"
#| out-width: "50%"
knitr::include_graphics("images/annie.png")
```
::: {.center-text}
[**Annie Adams**]{.teal-text}
**Email:** [[email protected]](mailto::[email protected])
**Learn more:** [annieradams.github.io](https://annieradams.github.io/){target="_blank"}
:::
:::
::: {.g-col-12 .g-col-md-4}
::: {.center-text .body-text-l}
**TA**
:::
```{r}
#| eval: true
#| echo: false
#| fig-align: "center"
#| out-width: "50%"
knitr::include_graphics("images/tbd.png")
```
::: {.center-text}
[**Sloane Stephenson**]{.teal-text}
**Email:** [[email protected]](mailto::[email protected])
<!-- **Learn more:** tbd -->
:::
:::
:::
## Acknowledgements
Building this course meant learning from the many incredible folks who think *a lot* about producing effective, beautiful, and responsible data visualizations. I relied heavily on the open source R / `{ggplot2}` / data viz teaching materials and tutorials that this wonderful data science community shares so willingly. Attribution will be included on any slides / materials where content is adapted from other educators.