In lemonad/coursera-mastering-r-capstone: Earthquake visualization functions for Coursera's R Specialization Capstone

library(ggplot2)
library(magrittr)
library(leaflet)
library(lubridate)
library(earthquakecapstone)
knitr::opts_chunk$set(
  echo = TRUE,
  fig.align = "center",
  fig_width = 8,
  fig_height = 5
)

This package is the capstone project for Coursera's Mastering Software Development in R specialization consisting of the courses

• The R Programming Environment,
• Advanced R Programming,
• Building R Packages,
• Building Data Visualization Tools,
• Mastering Software Development in R Capstone

The package provides functions to clean and visualize data from the NOAA Significant Earthquake Database.

Installation

Install the development version of this package using

devtools::install_github("lemonad/coursera-mastering-r-capstone")

Dataset

The NOAA Significant Earthquake database can be made available by executing the following

api_uri <- "https://www.ngdc.noaa.gov/hazel/hazard-service/api/v1/earthquakes"
f <- jsonlite::read_json(api_uri, simplify = TRUE)
earthquakes <- dplyr::as_tibble(f$items)

Cleaning

The API data has some quirks and can be made easier to work with by using the function eq_clean_data which returns a dataframe that can be subsequently filtered, etc.

df <- earthquakes %>%
  eq_clean_data() %>%
  dplyr::filter(
    country %in% c("USA", "Mexico") & lubridate::year(date) >= 2000
  )

Note that the BCE events are removed from the results per the requirements of using the class Date for the dates. This could be remedied by using the package gregorian.

Timeline

Having cleaned the data, the events can now be plotted on a timeline with optional label annotation. Below, the timeline is plotted with a separate axis for each country and events are colored according to the total number of deaths. The five highest magnitude events are annotated with their corresponding location.

df %>%
  ggplot2::ggplot(
    ggplot2::aes(x = date, y = country, colour = deathsTotal),
    alpha = 
  ) +
  geom_timeline() +
  geom_timeline_label(
    ggplot2::aes(label = locationName, magnitude = eqMagnitude),
    n_max = 5
  )

Map

The sites of the events can also be plotted on an interactive map. The sites will show a tooltip containing details when clicked. Here, the helper function eq_create_label is used to create the underlying html for the tooltip.

df %>%
  dplyr::mutate(popup_text = eq_create_label(.)) %>%
  eq_map(annot_col = "popup_text")

lemonad/coursera-mastering-r-capstone documentation built on Dec. 21, 2021, 9:51 a.m.