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README.Rmd
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README.Rmd
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---
output: github_document
---
<!--
README.md is generated from README.Rmd, so you should edit that file.
-->
```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE,
comment = ">",
fig.path = "man/figures/README-",
fig.align = "center",
fig.height = 6,
fig.width = 9,
message = FALSE, warning = FALSE)
```
# rnoaa <img src="man/figures/logo.png" align="right" width="120"/>
[](https://travis-ci.org/Cesar-Urteaga/rnoaa)
[](https://ci.appveyor.com/project/Cesar-Urteaga/rnoaa)
[](https://codecov.io/gh/Cesar-Urteaga/rnoaa)
## Table of Contents
* [Overview](#overview)
* [Installation](#installation)
* [Usage](#usage)
* [Documentation](#documentation)
* [Unit Testing](#unit-testing)
* [Development Workflow](#development-workflow)
## Overview
`rnoaa` is an [R](https://www.r-project.org/) package with a set of functions that makes easier to analyze the earthquake data provided by the [U.S. National Oceanic and Atmospheric Administration (NOAA)](http://www.noaa.gov/).
## Installation
```{r, eval = FALSE}
# Install the package from GitHub without the vignette:
devtools::install_github("Cesar-Urteaga/rnoaa")
# Or you can include it:
devtools::install_github("Cesar-Urteaga/rnoaa", build_vignettes = TRUE)
```
## Usage
This package allows you to get and clean the latest earthquake data from [the NOAA's Webpage](https://www.ngdc.noaa.gov/nndc/struts/form?t=101650&s=1&d=1) so as to prepare it for analysis:
```{r}
library(rnoaa)
library(dplyr)
# GETTING THE DATA
# In case you do not have internet access, you can use the get_earthquake_data
# function, which is a snapshot of the quake's data on September 10, 2017:
# raw_data <- get_earthquake_data()
raw_data <- download_earthquake_data()
# TIDYING THE DATA UP
# Before the data has been processed:
set.seed(48)
raw_data %>%
select(YEAR, MONTH, DAY, COUNTRY, LOCATION_NAME) %>%
sample_n(6)
# We use the two rnoaa's functions to clean the data.
clean_data <- raw_data %>%
eq_clean_data() %>%
eq_location_clean()
# After the data has been processed (note that the DATE variable has been
# created and the country has been removed for the LOCATION_NAME variable):
set.seed(48)
clean_data %>%
select(YEAR, MONTH, DAY, DATE, COUNTRY, LOCATION_NAME) %>%
sample_n(6)
# N.B.: When the month or/and day is/are missing, the date is approximated
# at the midpoint of the period.
```
Once the data was tidied, `rnoaa` includes two ggplot2's geoms to visualize the timeline in which the quakes have ocurred and label the ones with the greatest magnitude:
```{r TimelineGeom}
library(ggplot2)
clean_data %>%
filter(COUNTRY %in% c("CANADA", "USA", "MEXICO",
"CHINA", "JAPAN", "INDIA"),
!is.na(EQ_PRIMARY),
YEAR %in% 2000:2016) %>%
ggplot(mapping = aes(x = DATE,
y = COUNTRY,
size = EQ_PRIMARY,
color = TOTAL_DEATHS / 1000,
label = LOCATION_NAME)
) +
geom_timeline() +
geom_timeline_label(# We want to show the label for at most the two highest
# earthquakes by size.
n_max = 2,
line_height = 1 / 4,
angle = 10,
fontsize = 2.5) +
labs(size = "Richter scale value",
color = "# deaths in thousands",
y = "") +
guides(size = FALSE) +
theme_timeline()
```
Furthermore, it provides with functions to display the epicenters in an interactive R leaflet map:
```{r, eval = FALSE}
clean_data %>%
dplyr::filter(COUNTRY == "JAPAN" & lubridate::year(DATE) >= 2000) %>%
eq_map(annot_col = "DATE")
```
Also, it assists you to display the quake's traits from the data using popup text labels:
```{r, eval = FALSE}
clean_data %>%
dplyr::filter(COUNTRY == "MEXICO" & lubridate::year(DATE) == 2017) %>%
dplyr::mutate(popup_text = eq_create_label(.)) %>%
eq_map(annot_col = "popup_text")
```
## Documentation
Please check the [package's vignette](./vignettes/using-rnoaa.Rmd) to see how the package works or review the examples given in the package's documentation (use `?function_name`); you can run them with the function `example` (e.g., `example("geom_timeline_label")`).
## Unit Testing
In order to increase the quality of the package, a [test suite](./tests/testthat) was carried out for each function using the `testthat` R package. I have added a [code coverage](https://en.wikipedia.org/wiki/Code_coverage) measure to the package's repository using [codecov](https://codecov.io).
## Development Workflow
The workflow that was used to develop this package is described [here](https://github.com/Cesar-Urteaga/rfars#workflow).