What analysis programs drive conservation science?

[This article was first published on Bluecology blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

What analysis programs drive conservation science?

With the International Congress for Conservation Biology on at the end of July I was wondering, what analysis programs are supporting conservation science? And, what programs support spatial analysis and mapping?

I ran a quick poll on my blog (you can take it here) to find out. Here are the results (as of 30th July 2017). Voters are allowed to pick multiple categories.

I think the results are informative, particularly if you are a scientist in training and are wondering what programs to learn.

barplot of most popular programs

Programs with Graphical User Interfaces (“GUIs”, like Excel) were less popular for analysis than programming languages, like R. This speaks to how skilled modern conservation scientists are becoming with computations and programming.

The R program tops the list of most popular analysis programs. Not a surprising result, given its predominance in the biological, ecological and social sciences that conservation science draws from.

It is a bit of a shame to see Python so far down the list. Python has a reputation of being much easier to learn than R. Some groups even teach it to primary school kids, such as the Raspberry Pi foundation. I suspect that most conservation scientists don’t have training in computer science (why would they), so you might expect greater uptake of Python.

Python still has many very handy packages for analysis, and has the advantage of being much faster to process than R for many types of programs. This can be really important for processing large data-sets, such as the analysis we conducted on global gaps in marine species protection. We used Python to process overlap between ~17000 species ranges and the boundaries of marine protected areas.

Somewhat surprisingly to me was that R also topped the question about GIS, though the free GUI program – QGIS – was a close second.

It is exciting to see so many of my colleagues using R for GIS and spatial analysis.

It was a a surprise to me that R was so popular, because it is not that easy to do spatial analysis and mapping in R (if you want to learn, try my short course). R can be used as a fully functional GIS, but you need to install a bunch of additional packages. Further, you need to know a lot more about the theory behind geographical sciences, and the storage of spatial data, to operate R as a GIS.

Once again, Python is a fair way down the list, which comes as a surprise to me. They are a bunch of great tools for spatial analysis in Python too.

I hope you found this quick survey interesting. I love to hear about how people are using R or other programs to support conservation science. Join me on Twitter if you want to continue the conversation.

To leave a comment for the author, please follow the link and comment on their blog: Bluecology blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)