In bibagitbub/capstone: Package for Capstone Project

This project provides functions for the cleaning and visualization of the NOOA Dataset. In particular, it provides:

Functions for cleaning data, parsing strings, converting fields in the appropriate format
Functions for visualizing data over time
Functions for visualizing data over space

NOOA Dataset info

This capstone project will be centered around a dataset obtained from the U.S. National Oceanographic and Atmospheric Administration (NOAA) on significant earthquakes around the world. This dataset contains information about 5,933 earthquakes over an approximately 4,000 year time span.

Parsing data

eq_clean_data converts time information to Date class, it converts the longitude and latitude information to numeric and it applies the eq_location_clean function to LOCATION_NAME field.

earthquakes <- capstone::eq_clean_data( readr::read_delim("signif.txt",delim="\t") )

earthquakes <- capstone::eq_clean_data( readr::read_delim("signif.txt",delim="\t") )

Visualization over time

geom_timeline visualizes the times at which earthquakes occur within certain countries. In addition, it displays the magnitudes (i.e. Richter scale value) and the number of deaths associated with each earthquake. geom_timeline_label adds labels of the earthquakes on a Timeline representation.

x <- as.Date("2000-01-01")
xmax <- as.Date("2017-01-01")
countries <- c("ITALY","USA")
n_max <- 10

to_plot <- earthquakes
to_plot <- dplyr::filter(to_plot, date >= x & date <=xmax & (COUNTRY %in% countries))
to_plot <- dplyr::filter(to_plot, !is.na(INTENSITY) & !is.na(DEATHS))
to_plot <- dplyr::mutate(to_plot, COUNTRY = factor(COUNTRY, levels = unique(COUNTRY)))

to_plot2 <- to_plot[order(to_plot$INTENSITY,decreasing = TRUE),]
to_plot2 <- to_plot2[1:min(n_max,nrow(to_plot2)),]

ggplot2::ggplot(data = to_plot) +
  ggplot2::geom_segment(ggplot2::aes(x = x, xend = xmax, y = COUNTRY, yend = COUNTRY),
               alpha = 0.5, color = "gray") +
  capstone::geom_timeline(ggplot2::aes(x = date, y = COUNTRY, i = INTENSITY, d = DEATHS)) +
  ggplot2::geom_segment(data = to_plot2, ggplot2::aes(x = date, xend = date, y = COUNTRY, yend = as.numeric(COUNTRY) + 0.25),
               alpha = 0.5, color = "gray") +
  capstone::geom_timeline_label(data = to_plot2, ggplot2::aes(x = date, y = as.numeric(COUNTRY) + 0.4, label = LOCATION_NAME)) +
  ggplot2::theme_minimal()

Visualization over space

eq_map maps the epicenters (LATITUDE/LONGITUDE) and annotates each point in a pop up window containing annotation data stored in a column of the data frame. eq_create_labelcreates an HTML label for the leaflet map containing the location cleaned by the eq_location_clean function, the magnitude (EQ_PRIMARY), and the total number of deaths (TOTAL_DEATHS).

out <- readr::read_delim("signif.txt", delim = "\t")

out <- readr::read_delim("signif.txt", delim = "\t")

out <- capstone::eq_clean_data(out)
out <- dplyr::filter(out, COUNTRY == "MEXICO" & lubridate::year(date) >= 2000)
out <- dplyr::mutate(out, popup_text = capstone::eq_create_label(out))
capstone::eq_map(out, annot_col = "popup_text")