tidyDisasters
The goal of tidyDisasters is to create a queryable data
set that unites information from the Centre for Research on the
Epidemiology of Disasters (Belgium) EMDAT, the National Consortium for the
Study of Terrorism and Responses to Terrorism (United States of America)
GTD, and the Federal Emergency
Management Agency (United States of America) FEMA;
three sources that complement each other. Standard information about the
types and classes of disasters is from the United Nation’s 2020 Hazard
Definition and Classification Review (UN Hazards). Whereas FEMA reports
the county-level location of a natural event, EMDAT estimates the number
of killed and wounded of that natural event, the GTD contains the
terrorism events, and the UN Hazards table contextualizes each disaster
by class.
Installation
Our package is currently being revised by CRAN. The development
version of tidyDisasters:: can be installed from this
GitHub repository by
library(devtools)
install_github("ccani007/tidyDisasters")Please note that using compiled code from GitHub may require your computer to have additional software (Rtools for Windows or Xcode for Mac). Also note that installing this development version may result in some errors. If you find problems, please submit a bug ticket.
Examples
This is a basic example which shows how to search for a disaster event. This code finds Hurricane Harvey and shows how it affected Texas in 2017.
library(tidyDisasters)
library(lubridate)
library(tidyverse)
data("disastDates_df")
data("disastCasualties_df")
data("disastLocations_df")
data("disastTypes_df")
disastTypes_df %>%
left_join(disastDates_df, by = "eventKey") %>%
left_join(disastCasualties_df, by = "eventKey") %>%
left_join(disastLocations_df, by = "eventKey") %>%
mutate(Year = year(eventStart)) %>%
filter(Year == 2017 & state == "TX" & incident_type == "Hurricane") %>%
distinct() %>%
rmarkdown::paged_table()This is another example that shows the number of counties affected by fires since the 90s: we observe the 2000-2001 Western United States wildfires.
fires_df <-
disastLocations_df %>%
left_join(disastTypes_df, by = "eventKey") %>%
left_join(disastDates_df, by = "eventKey") %>%
mutate(Year = year(eventStart)) %>%
filter(hazard_cluster == "Environmental degradation (Forestry)") %>%
group_by(state, county, Year) %>%
summarise(Fire = n() >= 1L, .groups = "keep") %>%
group_by(Year) %>%
summarise(Count = sum(Fire))
ggplot(fires_df) +
theme_classic() +
theme(axis.text.x = element_text(size = 10, angle = 90)) +
aes(x = Year, y = Count) +
labs(
title = "Number of Counties Affected by Fires Since the 90s",
caption = "Data from the tidyDisasters R Package",
y = "No. Counties affected by fires"
) +
scale_x_continuous(breaks = 1990:2020) +
scale_y_continuous(breaks = seq(0, 1000, by = 100)) +
geom_vline(xintercept = 2000) +
geom_vline(xintercept = 2001) +
geom_point(size = 2, color = "#DA3330")