Reproducible Quantitative Methods

Lesson 9

Making better plots / Academic publishing

Topics and Resources

Plotting in R

There are several ways we can approach plotting lessons, but I suggest taking your target dataset and plotting it in a variety of ways- scatter plots, box plots (I love box plots), bar graphs, etc. Work with your group to identify what sorts of visualizations make sense- here's some ideas from ggplot2, but there are some inspiring resources written with base R too. The way you do this will depend somewhat on the form your data takes- be sure to take inspiration from the conventions of data presentation in your field. We'll work together interpret the graph at each step, and talk about the advantages and disadvantages of presenting the data each way.

ProTip

A helpful hint from those that came before

Keep it real. It's very rewarding to produce "real" journal quality figures right from your R scripts early in the process. If time permits, create multi-panel figures or add annotations to graphs- and I'm here to help with that- these often trip students up when working on projects solo.
Academic publishing

This week, I wanted to focus in on a piece of writing by Shea Swauger, who is Head of Researcher Support Services at Auraria Library, University of Colorado, Denver. He is an expert on academic publishing, digital inclusion, and using open data, open access and community engagemnt to foster better policy decisions. Read this article and we will discuss "predatory publishing" and what it means for your work.

Exercises

Making figures

Work through creating figures, based on your project data. Think about things like color palettes, and what that means for accessibility.

Discussion

Academic publishing

So, how do you decide where to publish? It’s easy to find resources about choosing a journal from a more conventional framework (TL:DR- most of them are some variation of “Choose the journal with the highest impact factor that also fits your work”)- but issues around open access muddies this old advice a bit. This is a really good opportunity to talk about issues with valuing science and scientists by impact factors of journals. We suggest reading Eigenfactor Index of Open Access Fees to get you started. Open access seems simple on the surface, but we get into a very tangled web of corporate interests, biased metrics, and misinformation very quickly.