library(tidyverse)

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library(lubridate)
library(scales)

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Attaching package: 'scales'

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library(tidycensus)      

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library(sf)              

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Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

library(leaflet)         

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library(usdata)   

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library(plotly)

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Attaching package: 'plotly'

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library(tigris)

Warning: package 'tigris' was built under R version 4.2.3

To enable caching of data, set `options(tigris_use_cache = TRUE)`
in your R script or .Rprofile.

PART ONE

states <- get_acs(geography = "state",               
                  variables = "B01003_001",          
                  geometry = TRUE,                   
                  shift_geo = T)                     

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()

homicide <- read.csv("data-table.csv")

homicide_state <- homicide |> 
  filter(YEAR == 2021) |> 
  mutate(NAME = abbr2state(STATE)) 

states |> 
  inner_join(homicide_state) |> 
  ggplot() + 
  geom_sf(aes(fill = RATE)) + 
  coord_sf(datum = NA)+
  scale_fill_viridis_c(labels = label_comma())+
  theme_minimal()+
  labs(title = "2021 Homicide Rates Per 100,000 By State")

Joining with `by = join_by(NAME)`

states |> 
  inner_join(homicide_state) %>% 
  ggplot(aes(x = estimate)) +
  geom_histogram(binwidth = 5000000)+
  scale_x_continuous(labels = comma)+
  theme_minimal()+
  xlab("Homicide Rates")

Joining with `by = join_by(NAME)`

options(tigris_use_cache = TRUE)
states_leaflet <- get_acs(geography = "state",       
                  variables = "B01003_001",          
                  geometry = TRUE,
                  shift_geo = F) 

Getting data from the 2018-2022 5-year ACS

homicide_colors <- colorNumeric(palette = "viridis", domain = homicide_state$RATE)

states_leaflet %>% 
  inner_join(homicide_state) |>
  leaflet() %>% 
  addTiles() %>% 
  addPolygons(weight = 1, 
              fillColor = ~homicide_colors(RATE), 
              label = ~paste0(NAME, ", Homicide Rate = ", RATE), 
              highlight = highlightOptions(weight = 2)) %>%
  setView(-95, 40, zoom = 4) %>% 
  addLegend(pal = homicide_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'

homicide_state |> 
  plot_ly(x = ~RATE) |> 
  add_histogram()

PART TWO

crime_open_database <- readRDS("crime_open_database_core_all_2022.Rds")

chicago_crime <- crime_open_database %>% 
  filter (city_name == "Chicago") %>% 
  filter (location_type == "gas station") %>% 
  filter (offense_against == "society")

chicago_crime %>% 
  leaflet() %>% 
  addTiles() %>% 
  addCircleMarkers(stroke = F, fillOpacity = .6, radius = 5, label = ~offense_type)

Assuming "longitude" and "latitude" are longitude and latitude, respectively

chicago_crime %>% 
  count(offense_type, offense_group) %>% 
  arrange(-n)

# A tibble: 6 × 3
  offense_type                                               offense_group     n
  <chr>                                                      <chr>         <int>
1 weapon law violations                                      weapon law v…   139
2 trespass of real property                                  trespass of …    94
3 drug/narcotic violations                                   drug/narcoti…    65
4 liquor law violations (except driving under the influence… liquor law v…     1
5 operating/promoting/assisting gambling                     gambling off…     1
6 prostitution                                               prostitution…     1