climateapi

The goal of library(climateapi) is to minimize repeated data cleaning and wrangling to enable project teams to devote more time to substantive analysis and inference-making. The package works toward this goal by creating a unified interface to common datasets and data manipulation tasks. Functions (will) support both climate-specific datasets as well as those that are climate-adjacent.

Installation

You can install the development version of climateapi from GitHub with:

# install.packages("renv")
renv::install("UI-Research/climateapi")

The climateapi package at work:

library(climateapi)
library(urbnindicators)
library(sf)
library(tidyverse)
library(urbnthemes)
set_urbn_defaults(style = "print")

county_disaster_declarations = get_fema_disaster_declarations_county(api = TRUE)

county_disaster_declarations %>%
  filter(stringr::str_detect(GEOID, "^01")) %>% ## Alabama
  group_by(year_declared) %>%
    summarize(annual_incidents = sum(incidents_all, na.rm = TRUE)) %>%
  ggplot() +
    geom_col(aes(x = year_declared, y = annual_incidents)) +
    labs(
      title = "COVID Results in a Spike of Counties with Disaster Declarations in 2020",
      subtitle = "Sum of major disaster declarations per Alabama county, by year",
      x = "",
      y = "") +
    theme_urbn_print()

wildfire_perimeters = get_current_fire_perimeters() %>%
  arrange(desc(incident_size_acres)) %>%
  slice(1) %>%
  st_transform(5070)

impacted_structures = get_structures(
  boundaries = wildfire_perimeters,
  geography = "tract")

us_tracts_sf = tigris::tracts(cb = TRUE, year = 2023, progress_bar = FALSE) %>%
  sf::st_transform(5070)

tracts_sf = us_tracts_sf %>%
  sf::st_filter(wildfire_perimeters %>% st_transform(5070) %>% st_buffer(10000)) %>%
  dplyr::select(GEOID) %>%
  dplyr::left_join(
    impacted_structures %>% 
      dplyr::filter(occupancy_class == "Residential") %>%
      dplyr::group_by(GEOID) %>%
      dplyr::summarize(residential_units = sum(count, na.rm = TRUE)), 
    by = "GEOID") %>%
  dplyr::mutate(county_fips = stringr::str_sub(GEOID, 1, 5)) %>%
  dplyr::left_join(
    tidycensus::fips_codes %>% 
      dplyr::mutate(county_fips = stringr::str_c(state_code, county_code)), 
    by = c("county_fips"))

ggplot() +
  geom_sf(data = tracts_sf, aes(fill = residential_units), linewidth = .6) +
    scale_fill_continuous(na.value = "darkgrey") +
  geom_sf(
    data = tracts_sf %>% group_by(county_fips) %>% summarize(), 
    fill = NA, 
    color = "black",
    linewidth = .75) +
  geom_sf(data = wildfire_perimeters, fill = NA, color = palette_urbn_magenta[5], linewidth = .75) +
  labs(
    title = "Estimated Residential Units within Wildfire Boundaries",
    subtitle = str_c(
      "Incident Name: ", wildfire_perimeters$incident_name, "\n",
      "State(s): ", paste(
        tracts_sf %>% 
          dplyr::filter(!is.na(residential_units)) %>% 
          dplyr::distinct(state_name) %>%
          dplyr::pull(), collapse = ", "), "\n",
      "Incident Size: ", round(wildfire_perimeters$incident_size_acres, 0), " acres", "\n",
      "Intersecting Tracts: ", nrow(tracts_sf %>% dplyr::filter(!is.na(residential_units)))),
    fill = "Residential units") +
  theme_urbn_map()