README.md

In raggaraluz/mastering_r_capstone:

earthquakes package

The earthquakes package is created for the Mastering Software Development in R capstone from coursera.

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.

The overall goal of the capstone project is to integrate the skills you have developed over the courses in this Specialization and to build a software package that can be used to work with the NOAA Significant Earthquakes dataset. This dataset has a substantial amount of information embedded in it that may not be immediately accessible to people without knowledge of the intimate details of the dataset or of R. Your job is to provide the tools for processing and visualizing the data so that others may extract some use out of the information embedded within.

The ultimate goal of the capstone is to build an R package that will contain features and will satisfy a number of requirements that will be laid out in the subsequent Modules. You may want to begin organizing your package and insert various features as you go through the capstone project.

Dataset

• NOAA Significant Earthquake Database

Installation

You can install earthquakes from github with:

# install.packages("devtools")
devtools::install_github("raggaraluz/mastering_r_capstone")

Example

Downloading data

Data can be downloaded from NOAA Significant Earthquake Database as TSV and converted to a data frame:

library(readr)

location <- 'https://www.ngdc.noaa.gov/nndc/struts/results?type_0=Exact&query_0=$ID&t=101650&s=13&d=189&dfn=signif.txt'
raw <- read_tsv(location)

Alternatively, a sample is stored into the package itself

raw <- earthquakes::earthquake_data_raw

Then, the dataframe can be cleaned using the earthquakes package functions

library(earthquakes)
library(dplyr)
clean <- eq_clean_data(earthquake_data_raw) %>%
  eq_location_clean()

knitr::kable(clean %>%
               select(DATE, LATITUDE, LONGITUDE, LOCATION_NAME, EQ_PRIMARY) %>%
               tail)

| DATE | LATITUDE | LONGITUDE | LOCATION_NAME | EQ_PRIMARY | | :--------- | -------: | --------: | :------------- | ----------: | | 2018-01-11 | 33.764 | 45.749 | Kermanshah | 5.5 | | 2018-01-14 | -15.776 | -74.744 | Arequipa | 7.1 | | 2018-01-23 | -7.196 | 105.918 | Java: Banten | 6.0 | | 2018-01-23 | 56.046 | -149.073 | Kodiak Island | 7.9 | | 2018-01-31 | 36.543 | 70.816 | Baluchistan | 6.1 | | 2018-02-06 | 24.174 | 121.653 | Hualien | 6.4 |

Alternatively, the clean dataset is stored in the repository as well

clean <- earthquake_data

Timeline geoms

In this package are available two geoms that can be used to plot earthquake data.

The first geom, geom_timeline() is used for plotting a time line of earthquakes ranging from xmin to xmax dates, with a point for each earthquake. Optional aesthetics include color, size and alpha. The x aesthetic is a date and an optional y aesthetic is a factor indicating some stratification for multiple lines.

library(ggplot2)
library(lubridate)
clean %>%
  filter(COUNTRY %in% c('USA', 'CHINA')) %>%

  ggplot(aes(x = DATE, y = COUNTRY, size=EQ_PRIMARY, fill = DEATHS)) +
    geom_timeline(alpha = 0.25, xmin = ymd('2000-01-01'), xmax = ymd('2015-12-31')) +
    theme_minimal() + theme(panel.grid.minor.x = element_blank(),
                            panel.grid.major.x = element_blank(),
                            axis.line.y = element_blank(),
                            axis.title.y = element_blank(),
                            axis.line.x = element_line(),
                            axis.ticks.x = element_line())

The second geom is called geom_timeline_label() and is used for adding annotations to the earthquake data. This geom adds a vertical line to each data point with a text annotation (e.g. the location of the earthquake) attached to each line. There should be an option to subset to n_max number of earthquakes, where we take the n_max largest (by magnitude) earthquakes. Aesthetics are x, which is the date of the earthquake and label which takes the column name from which annotations will be obtained.

data <- clean %>%
  filter(COUNTRY %in% c('USA', 'CHINA'))

labels <- data %>%
  top_earthquakes(10, ymd('2000-01-01'), ymd('2015-12-31'))


  ggplot(data, aes(x = DATE, y = COUNTRY, fill = DEATHS)) +
    geom_timeline(aes(size = EQ_PRIMARY), alpha = 0.25, xmin = ymd('2000-01-01'), xmax = ymd('2015-12-31')) +
    geom_timeline_label(data = labels, mapping = aes(x = DATE, y = COUNTRY, label = LOCATION_NAME)) +
    theme_minimal() + theme(panel.grid.minor.x = element_blank(),
                            panel.grid.major.x = element_blank(),
                            axis.line.y = element_blank(),
                            axis.title.y = element_blank(),
                            axis.line.x = element_line(),
                            axis.ticks.x = element_line(),
                            legend.position = 'bottom')

Maps

There is a function called eq_map() that takes an argument data containing the filtered data frame with earthquakes to visualize. The function maps the epicenters (LATITUDE/LONGITUDE) and annotates each point with in pop up window containing annotation data stored in a column of the data frame. The user should be able to choose which column is used for the annotation in the pop-up with a function argument named annot_col. Each earthquake should be shown with a circle, and the radius of the circle should be proportional to the earthquake’s magnitude (EQ_PRIMARY). One example of this function is:

clean %>%  
  filter(COUNTRY == "MEXICO" & year(DATE) >= 2000) %>% 
  eq_map(annot_col = "DATE")

In this case, the image is static, but it is a full interactive map in R.

Finally a function called eq_create_label() is available, so that it takes the dataset as an argument and creates an HTML label that can be used as the annotation text in the leaflet map. This function puts together a character string for each earthquake that will show the cleaned location (as cleaned by the eq_location_clean() function created in Module 1), the magnitude (EQ_PRIMARY), and the total number of deaths (TOTAL_DEATHS), with boldface labels for each (“Location”, “Total deaths”, and “Magnitude”). If an earthquake is missing values for any of these, both the label and the value should be skipped for that element of the tag. The resulting code is:

clean %>% 
  filter(COUNTRY == "MEXICO" & year(DATE) >= 2000) %>% 
  mutate(popup_text = eq_create_label(.)) %>% 
  eq_map(annot_col = "popup_text")

Again, the image is static, but it is a full interactive map in R.

Build status

raggaraluz/mastering_r_capstone documentation built on May 29, 2019, 6:53 p.m.