Map Assignment

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
✔ ggplot2   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lubridate)
library(scales)

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

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()

OD_deaths <- read_csv("OD_deaths.csv")

Rows: 500 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): STATE, URL
dbl (2): YEAR, RATE
num (1): DEATHS

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.

od_states <- OD_deaths |> 
  filter(YEAR == 2021) |> 
  mutate(NAME = abbr2state(STATE)) 

states |> 
  inner_join(od_states) |> 
  ggplot() + 
  geom_sf(aes(fill = RATE)) + 
  coord_sf(datum = NA) + theme_minimal() +
  scale_fill_viridis_c(labels = label_comma()) +
  labs(title = "Overdose death rates in 2021")

Joining with `by = join_by(NAME)`

1. This choropleth shows overdose death rates by state from 2021.

od_states |> 
  ggplot(aes(x = DEATHS)) +
  geom_histogram(binwidth = 400) +
  scale_x_continuous(labels = comma) +
  theme_minimal() +
  xlab("Overdose deaths")

2. This histogram show the number of overdose deaths from 2021.

leaflet() |> 
  addTiles()

states_leaflet <- get_acs(geography = "state",       # gets state by state data
                  variables = "B19013_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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od_colors <- colorNumeric(palette = "viridis", domain = od_states$RATE)

states_leaflet |>
  inner_join(od_states) |>
  leaflet() |>
  addTiles() |>
  addPolygons(weight = 1,
              fillColor = ~od_colors(RATE),
              label = ~paste0(NAME, ", Overdose death rate = ", RATE), highlight = highlightOptions(weight = 2)) |>
  setView(-95, 40, zoom = 4) |>
  addLegend(pal = od_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'

3. This choropleth shows overdose death rate by state for 2021.

library(plotly)

Attaching package: 'plotly'

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

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

od_states |>
 plot_ly(x = ~RATE,
              type = "histogram") 

4. This histogram shows overdoes death rates from 2021.

shootings <- read.csv("fatal-police-shootings-data.csv")

shootings |>
  filter(state == "MT") -> MT_shootings
MT_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 11 rows with
either missing or invalid lat/lon values and will be ignored

4. This map shows fatal police shootings in Montana from 2015 to 2024 and the type of threat for each fatality.

MT_shootings |>
  count(armed_with, body_camera) |>
  arrange(-n)

     armed_with body_camera  n
1           gun       False 34
2         knife       False  7
3  undetermined       False  4
4       unknown       False  4
5       replica       False  3
6                     False  2
7           gun        True  2
8  blunt_object       False  1
9         knife        True  1
10      vehicle       False  1
11      vehicle        True  1

5. This table shows what the victim of the fatal police shooting was in possession of at the time of their death and if the law enforcement officer was using their body camera in Montana between 2015 and 2024.