Maps

library(plotly)
Loading required package: ggplot2

Attaching package: 'plotly'
The following object is masked from 'package:ggplot2':

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library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks plotly::filter(), stats::filter()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(lubridate)
library(scales)

Attaching package: 'scales'

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library(tidycensus)      # gets census data that we can use to create maps
library(sf)              # helper package for mapping
Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
library(leaflet)         # interactive mapping package
library(usdata)          # this package has a conversion utility for state abbreviations to full names
states <- get_acs(geography = "state",               # gets state by state data
                  variables = "B01003_001",          # this is state population
                  geometry = TRUE,                   # gets geometry (the maps)
                  shift_geo = T)                     # shifts Hawaii and Alaska
Getting data from the 2018-2022 5-year ACS
Warning: The `shift_geo` argument is deprecated and will be removed in a future
release. We recommend using `tigris::shift_geometry()` instead.
Using feature geometry obtained from the albersusa package
Please note: Alaska and Hawaii are being shifted and are not to scale.
old-style crs object detected; please recreate object with a recent sf::st_crs()
life_expectancy <-read_csv("life_expectancy.csv")
Rows: 150 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): STATE, URL
dbl (2): YEAR, RATE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
life_expectancy |> 
  filter(YEAR == 2020) |> 
  mutate(NAME = abbr2state(STATE)) -> life_expectancy_states

states |> 
  inner_join(life_expectancy_states) |> 
  ggplot() + 
  geom_sf(aes(fill = RATE)) + 
  coord_sf(datum = NA) +
  scale_fill_viridis_c(labels = label_comma()) +
  theme_minimal() +
  labs(title = "State Life Expectancy")
Joining with `by = join_by(NAME)`

life_expectancy_states |> 
  ggplot(aes(x = RATE)) +
  geom_histogram(binwidth = .5) +
  theme_minimal() +
  xlab("State Life Expectancy")

states_leaflet <- get_acs(geography = "state",       # gets state by state data
                  variables = "B01003_001",          # this is state income
                  geometry = TRUE,                   # gets geometry (the maps)
                  shift_geo = F)                    # we're not shifting Hawaii and Alaska!
Getting data from the 2018-2022 5-year ACS
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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expectancy_colors <- colorNumeric(palette = "viridis", domain = life_expectancy_states$RATE)

states_leaflet |>
  inner_join(life_expectancy_states) |> 
  leaflet() |>
  addTiles() |>
  addPolygons(weight = 1,
             fillColor = ~expectancy_colors(RATE),
            label = ~paste0(NAME, ", life expectancy = ", RATE), 
            highlight = highlightOptions(weight = 2)) |>
  setView(-95, 40, zoom = 4) |>
  addLegend(pal = expectancy_colors, values = ~RATE)
Joining with `by = join_by(NAME)`
Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'

Life Expectancy by State

life_expectancy_states |> 
  plot_ly(x = ~RATE) |> 
  add_histogram()
shootings <- read_csv("fatal-police-shootings-data.csv")
Rows: 9570 Columns: 19
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (12): threat_type, flee_status, armed_with, city, county, state, locati...
dbl   (4): id, latitude, longitude, age
lgl   (2): was_mental_illness_related, body_camera
date  (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
shootings |>
  filter(state == "HI") -> HI_shootings
HI_shootings |> 
  leaflet() |> 
  addTiles() |>
  addCircleMarkers(stroke = F,
                   fillOpacity = .6,
                   radius = 5,
                   label = ~threat_type)
Assuming "longitude" and "latitude" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName): Data contains 6 rows with either
missing or invalid lat/lon values and will be ignored

Police Shootings in Hawaii from 2015 to 2024

HI_shootings |>
  count(threat_type, body_camera)
# A tibble: 10 × 3
   threat_type body_camera     n
   <chr>       <lgl>       <int>
 1 attack      FALSE           8
 2 attack      TRUE            5
 3 flee        FALSE           1
 4 flee        TRUE            1
 5 move        FALSE           3
 6 point       FALSE           6
 7 shoot       FALSE           6
 8 shoot       TRUE            3
 9 threat      FALSE          10
10 threat      TRUE            2