CRESCYNT Toolbox – R Resources for Graphing and Visualization

In a previous post we offered some solid supportive resources for learning R – a healthy  dinner with lots of great vegetables. Here we offer a dessert cart of rich resources for data visualization and graphing. It’s a powerful motivation for using R.

First up is The New R Graph Gallery – extensive, useful, and actually new. “It contains more than 200 data visualizations categorized by type, along with the R code that created them. You can browse the gallery by types of chart (boxplots, maps, histograms, interactive charts, 3-D charts, etc), or search the chart descriptions. Once you’ve found a chart you like, you can admire it in the gallery (and interact with it, if possible), and also find the R code which you can adapt for your own use. Some entries even include mini-tutorials describing how the chart was made.” (Description by Revolutions.)

Sometimes we want (or need) plain vanilla – something clean and elegant rather than extravagant. Check out A Compendium of Clean Graphs in R, including code. Many examples are especially well-suited for the spartan challenge of conveying information in grayscale. The R Graph Catalog is a similar resource.

If you’re just getting started with R, take a look at the Painless Data Visualization section (p. 17 onward) in this downloadable Beginner’s Guide.

In R, ggplot2,  based on the Grammar of Graphics, is perhaps the single most popular R package for data visualization. The R Cookbook‘s section on Graphs using ggplot2 is a helpful precursor to the R Graphics Cookbook. DataCamp’s DataVis with ggplot2 has a free segment of intro lessons.

For more on visualization and other capabilities, check out this recommended list of useful R packages in the R Studio support blog – succinct and terrific.

If you’re already skilled in R and want a new challenge, an indirect method of harnessing some of the power of D3.js for interactive web visualizations is available through plotly for R. Here’s getting started with plotly and ggplot2, plotly and Shiny, and a gallery. The resources offer code and in some cases the chance to open a visualization and modify its data.

Have a favorite resource? Please share as a comment, or email us!

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CRESCYNT Toolbox – What do we need on the workbench?

Science is a team sport. Collaborations allow us to ask more ambitious science questions, but also intensify the need to connect disparate datasets across scales of time and space. Solving data interoperability challenges requires technological solutions not yet in place, so we’re taking the initiative to review potential solutions.

We really want to include YOUR favorite tools and highest priority needs in this review, so please share those – we’ll report back.

A platform from EarthCube is some time and distance away, but we have a chance to start assembling tools already at hand and in use for coral reef research workflows and do some testing. The process also helps us ground the ideal in the practical.

What are  some criteria for a great infrastructure platform?

Ideally, solutions are: 1) modular, so when an improved tool is available it can be incorporated without restructuring the system; 2) free or low cost, so solutions are sustainable for most research labs; and 3) open source, allowing continued development from multiple disciplines and directions. However, we also want to start where people are, with the tools we’re already using – many of these are less than ideal but we make them work. That’s our starting place, and we want to hear about all of your tools.

It is tempting to set up a workbench for the challenge of analysis alone, but in a coral reef research lab we immediately crash into the realities of group data collection, field and lab work, physical specimens, and intersecting projects. All of these characteristics create additional layers of challenge. In the long run, infrastructure should help capture data and metadata generation at the source, and ease tracking, analysis, and replicability.

Good infrastructure solves more problems than it creates in compliance, skill demand, and management. An effective system helps graduate students and postdocs develop robust skills in managing data into the future, with guidelines that work for people, labs, and collaborators. Interoperability challenges must be solved for datasets that range from remote sensing to ecological surveys to bioinformatics work. Data cleaning, analysis, visualization, and mapping must be supported in flexible ways to clearly communicate research insights.

We have an opportunity to construct a preliminary array of tools and workflows on a cyberinfrastructure workbench for coral reef research, so if this is going to be a broadly useful start we need to know what you already like using and what more you wish you had. Dream big to start with, and along the way we’ll acknowledge the distinction between perfect and good-enough solutions.

Please complete this survey, or comment below. Thank you so much!

CRESCYNT Toolbox – Powerful Temperature Visualizations

Temperature is a critical environmental parameter that has profound relevance in describing where coral reefs do and do not occur, and growing direct and indirect threats to their existence.

Here are some powerful visualizations of global temperature data you may find useful or want to replicate for local or global temperature datasets, or use for education and outreach about climate change.

First, spiralling global temperatures by Ed Hawkins (using MATLAB):

Click to animate.     Author: Ed Hawkins

The climate spirals page of Hawkins’ Climate Lab Book features global temperature change updates, as well as atmospheric CO2 and arctic sea ice volume animations. His small multiples assembly of global temperatures by decade is very useful, and could be used to describe sequential environmental changes on regional map scales as well.

Ed Hawkins also links to Robert Geiseke and Malte Meinshausen’s interactive tool and a set of video animations for offline use that build upon his work.

NASA’s temperature graphs and charts are varied and useful, and have pages for downloading different temperature products for specific date ranges, including global maps, surface temperature animations, and this 135-year time chart of zonal temperature anomalies:

See NASA’s Global Climate Change – Global Temperature site for rich multimedia resources and apps, including global temperature anomaly maps with year sliders and an embeddable 3-min video which graphs different potential factors with global temperature changes (surprise: the correlation is with anthropogenic CO2). High global temperatures and low Arctic sea ice cover both broke records in the first half of 2016.

NOAA has its own further derivations of very useful temperature representations, particularly for coral reef work, including global and regional sea surface temperature contour maps,  a suite of coral bleaching data products with predictive bleaching alerts derived from virtual stations, and their degree heating weeks maps, which some NOAA scientists prefer as their most accurate post-event indicator of peak bleaching intensity.

Thanks for inspiration to Andrew Freedman’s excellent article on Mashable featuring compelling temperature visualizations.

If there’s a temperature visualization you love – including simple – simple is good – please share it with us by leaving a comment. Thanks.

Update: (1) fresh updated graphs and more at Ed Hawkins’ Climate Lab Book (or follow on Twitter: @ed_hawkins), and (2) check out Antti Lipponen’s temperature anomalies by country since 1900 – as animation or small multiples – showing variation and warming trends, with visual insight into continents.

 

>>>Go to the blog Masterpost or the CRESCYNT website or NSF EarthCube.<<<