# Using the Tidy Disaster Database

#### Catalina Cañizares, Mark J. Macgowan, Gabriel Odom

#### 2022-03-16

# 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")

# Conclusion

This package aims at uniting available heterogeneously dispersed data from three different sources to improve information accessibility and analysis. As can be seen in this previous examples, by uniting the datasets of interest questions exploring the impact of natural disasters can be easily solved. Another advantage of this package is that the data can be used to relate mass casualties to their short and long-term consequences beyond the damage itself. Using this comprehensive database, it is possible to relate natural and man-made disasters in the US to other datasets of interest, such as mental and physical health outcomes, economic and political metrics, or other data sets of interest, simply by matching on state, county, and date range.