In Valentin-Konoshenko/Earthquakes4Coursera: Visualization of the earthquakes from the NOAA database

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(Earthquakes4Coursera)

This vignette describes input data and the functions which prepare them for being visualized.

Input data

Input data are downloaded from NOAA Significant Earthquake Database and embedded in the project.

file_read() - Reads erthquake data

file_read() is used to make reading input data more convenient. Although it can accept a fully qualified file name as an input parameter, by default it reads the data file embedded in the project:

NOAA <- file_read()

eq_get_date() - Creates a date from its numeric representation -

eq_get_date() could be easily replaced with other functions like ISOdate() for AD dates but it is much more difficult for BC ones. That is where eq_get_date() does its job. It has a problem with producing BC leap days (actually the core problem is in R, which believes that it was 0-th year which started AD era) but there are no such dates in the data set. Let me show how it works:

eq_get_date(2018, 05, 20);
eq_get_date(-2070, 07, 12);
eq_get_date(2000);

It returns the object of the POSIXct class

lubridate::year(eq_get_date(-2070, 07, 12))

eq_location_clean() - Cleans the LOCATION_NAME column

This function helps to clean the LOCATION_NAME column by stripping out the country name (including the colon) and converts names to title case (as opposed to all caps). This will be needed later for annotating visualizations.

eq_location_clean("TURKEY:  IZMIR, EFES")

eq_clean_data() - Cleans NOAA data

This function takes takes raw NOAA data frame and returns a clean data frame. Cleaning means the following:

1. Creates the DATE column by uniting the year, month, day and converts it to the Date class (with the help of eq_get_date())
2. Converts the LATITUDE and LONGITUDE columns to numeric class
3. Cleans the LOCATION_NAME column by stripping out the country name (including the colon) and converts names to title case (with the help of eq_location_clean())

NOAAC <- eq_clean_data(NOAA)
knitr::kable(head(NOAAC, 5))

Now the data are clean and ready for being vizualized

Valentin-Konoshenko/Earthquakes4Coursera documentation built on May 23, 2019, 1:08 p.m.