In lmitchell4/earthquake: Functions for Working with Earthquake Data Set

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title: "Overview of Earthquake Data" author: "L. Mitchell" date: "r Sys.Date()" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Overview of Earthquake Data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

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library(earthquake)
library(knitr)
library(magrittr)

The earthquake R package was built to facilitate using data on "significant" earthquakes compiled by the National Geophysical Data Center/World Data Service*. The data are available on the National Oceanic and Atmospheric Administration (NOAA) website: https://www.ngdc.noaa.gov/nndc/struts/form?t=101650&s=1&d=1. The following is a description of the data taken directly from the NOAA website:

"The Significant Earthquake Database contains information on destructive earthquakes from 2150 B.C. to the present that meet at least one of the following criteria: Moderate damage (approximately $1 million or more), 10 or more deaths, Magnitude 7.5 or greater, Modified Mercalli Intensity X or greater, or the earthquake generated a tsunami."

Reading in the data

A copy of the entire data set (downloaded in October 2017) is provided with the package in a file called NOAA_earthquakes.txt. It can be read into R using the function eq_read_data. Simply pass in the file name as the only argument:

eqDataRaw <- eq_read_data(filename="NOAA_earthquakes.txt")
knitr::kable(head(eqDataRaw[ ,1:9], 10))  

Cleaning the data

The function eq_clean_data reads in the file and attempts to clean up some of the fields. In particular, it creates a DATE field reflecting the date of each earthquake. For simplicity, any missing months are filled in with January and any missing days are filled in with 1; these imputations should be taken into consideration when analyzing earthquake dates. Dates with negative years (i.e. B.C. dates) are converted to R Date objects by (1) calculating the number of days between the date "0000-01-01" and the corresponding date with a positive year, then (2) passing this number to as.Date() with the argument origin = "0000-01-01". This can result in mismatches between the original (negative year) date and the new Date object, so some care should also be taken when working with B.C. dates in the DATE field. An additional field, AD, is also created reflecting whether the original date fell into the A.D. period (TRUE) or B.C. period (FALSE). This field can be used to filter out one period or the other.

The function eq_clean_data also attempts to clean up some of the strings describing earthquake locations. For example, this function creates the field Country, which is a version of the field COUNTRY in the raw data file formatted in title case rather than uppercase (e.g., JAPAN in the field COUNTRY becomes Japan in the field Country). A new field LocalLocation is also created; this is a cleaned up and sometime abbreviated version of the raw field LOCATION_NAME.

The syntax for eq_clean_data is similar to that of eq_read_data:

eqDataClean <- eq_clean_data("NOAA_earthquakes.txt")
knitr::kable(head(eqDataClean[ ,c("DATE","Country","LocalLocation",
                                  "EQ_PRIMARY","Longitude","Latitude")], 10))

eq_clean_data uses the function clean_local_location to help clean up the LocalLocation field, which can consist of multiple locations separated by commas. Here is an example of how it would transform a given string:

clean_local_location("CUZCO,COLLAO,LIMA")

References

*National Geophysical Data Center / World Data Service (NGDC/WDS): Significant Earthquake Database. National Geophysical Data Center, NOAA. doi:10.7289/V5TD9V7K

lmitchell4/earthquake documentation built on May 29, 2019, 3:42 a.m.