New R package emdatr: Global Disaster Losses from the EM-DAT Database

[This article was first published on Nine Lives, 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.

The International Disaster Database EM-DAT from the Center for Research on the Epidemiology of Disasters (CRED, Belgium) is often used as a reference for losses on human life and property resulting from natural and man-made disasters. This database has over 20,000 country-level records from the early 1900s to the present. Data is available for free from EMDAT.

Some issues with EM-DAT data and reports:
  • Country names used by EMDAT are not always the same as those used by ISO 3166 convention. This issue is relevant when making spatial maps using R. 
  • Information such as GDP and population from the year of occurrence of the disaster have to be used to “normalize” or adjust monetary losses from the past. The EMDAT database does not provide such information. 
  • Annual reports published by EMDAT are not consistent with one another in terms of number of disasters per year or the total number of people affected/killed. For instance, number of diasters in 2012 were reported to be 428 in the ADSR 2012 report. But the same number in the 2011, 2010, 2009 and 2008 reports is 421, 421, 422 and 421, respectively!
R package emdatr:
  • comes with pre-processed and cleaned EMDAT data
  • includes above-mentioned additional country-level information
  • has functionality to extract desired subsets of the data
  • through the graphics and modeling capabilities provided by R, much more can be accomplished through this R package and the R language, than conventional spreadsheet analyses
  • by making the analysis transparent, the problems with the presentation of summary statistics could be addressed
The home page for the package presents some examples and graphics. Please see – https://github.com/RationShop/emdatr

Below are the relevant CRAN and GitHub sites:
Please let me know if you find any errors or if you have any comments or suggestions.

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

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)