The data this week comes from The Smithsonian Institution.
Axios put together a lovely plot of volcano eruptions since Krakatoa (after 1883) by elevation and type.
For more information about volcanoes check out the below Wikipedia article or specifically about VEI (Volcano Explosivity Index) see the Wikipedia article here. Lastly, Google Earth has an interactive site on "10,000 Years of Volcanoes"!
A volcano is a rupture in the crust of a planetary-mass object, such as Earth, that allows hot lava, volcanic ash, and gases to escape from a magma chamber below the surface.
Earth's volcanoes occur because its crust is broken into 17 major, rigid tectonic plates that float on a hotter, softer layer in its mantle. Therefore, on Earth, volcanoes are generally found where tectonic plates are diverging or converging, and most are found underwater.
Erupting volcanoes can pose many hazards, not only in the immediate vicinity of the eruption. One such hazard is that volcanic ash can be a threat to aircraft, in particular those with jet engines where ash particles can be melted by the high operating temperature; the melted particles then adhere to the turbine blades and alter their shape, disrupting the operation of the turbine. Large eruptions can affect temperature as ash and droplets of sulfuric acid obscure the sun and cool the Earth's lower atmosphere (or troposphere); however, they also absorb heat radiated from the Earth, thereby warming the upper atmosphere (or stratosphere). Historically, volcanic winters have caused catastrophic famines.
Volcano eruptions also can affect the global climate, a Nature Article has open-access data for a specific time-period of eruptions along with temperature anomalies and tree growth. More details can be found from NASA and the UCAR. A summary of the pay-walled Nature article can be found via the Smithsonian.
The researchers detected 238 eruptions from the past 2,500 years, they report today in Nature. About half were in the mid- to high-latitudes in the northern hemisphere, while 81 were in the tropics. (Because of the rotation of the Earth, material from tropical volcanoes ends up in both Greenland and Antarctica, while material from northern volcanoes tends to stay in the north.) The exact sources of most of the eruptions are as yet unknown, but the team was able to match their effects on climate to the tree ring records.
The analysis not only reinforces evidence that volcanoes can have long-lasting global effects, but it also fleshes out historical accounts, including what happened in the sixth-century Roman Empire. The first eruption, in late 535 or early 536, injected large amounts of sulfate and ash into the atmosphere. According to historical accounts, the atmosphere had dimmed by March 536, and it stayed that way for another 18 months.
Tree rings, and people of the time, recorded cold temperatures in North America, Asia and Europe, where summer temperatures dropped by 2.9 to 4.5 degrees Fahrenheit below the average of the previous 30 years. Then, in 539 or 540, another volcano erupted. It spewed 10 percent more aerosols into the atmosphere than the huge eruption of Tambora in Indonesia in 1815, which caused the infamous “year without a summer”. More misery ensued, including the famines and pandemics. The same eruptions may have even contributed to a decline in the Maya empire, the authors say.
There are additional datasets from the Nature article available as Excel files, but they are a bit more complicated - feel free to explore at your own discretion! If you use any of the Nature data, please cite w/ DOI: https://doi.org/10.1038/nature14565.
TidyTuesday A weekly data project aimed at the R ecosystem. As this project was borne out of the
R4DS Online Learning Community and the
R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with
dplyr, and other tools in the
tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.
Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data. While the dataset will be “tamed”, it will not always be tidy!
We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.
The intent of Tidy Tuesday is to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions. While we understand that the two are related, the focus of this practice is purely on building skills with real-world data.
All data will be posted on the data sets page on Monday. It will include the link to the original article (for context) and to the data set.
We welcome all newcomers, enthusiasts, and experts to participate, but be mindful of a few things: 1. The data set comes from the source article or the source that the article credits. Be mindful that the data is what it is and Tidy Tuesday is designed to help you practice data visualization and basic data wrangling in R. 2. Again, the data is what it is! You are welcome to explore beyond the provided dataset, but the data is provided as a "toy" dataset to practice techniques on. 3. This is NOT about criticizing the original article or graph. Real people made the graphs, collected or acquired the data! Focus on the provided dataset, learning, and improving your techniques in R. 4. This is NOT about criticizing or tearing down your fellow #RStats practitioners or their code! Be supportive and kind to each other! Like other's posts and help promote the #RStats community! 5. Use the hashtag #TidyTuesday on Twitter if you create your own version and would like to share it. 6. Include a picture of the visualisation when you post to Twitter. 7. Include a copy of the code used to create your visualization when you post to Twitter. Comment your code wherever possible to help yourself and others understand your process! 8. Focus on improving your craft, even if you end up with something simple! 9. Give credit to the original data source whenever possible.