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library(plotly)
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library(lubridate)library(scales)
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library(tidycensus) # gets census data that we can use to create maps
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library(sf) # helper package for mapping
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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) # interactive mapping package
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library(usdata) # this package has a conversion utility for state abbreviations to full names
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Life Expectancy of Each State
Firstly, we will obtain state-by-state data from the tidycensus package.
states <-get_acs(geography ="state", # gets state by state datavariables ="B01003_001", # this is state populationgeometry =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.
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The dataset I am using is the “Life Expectancy at Birth, by State” dataset from the Stats of the States website. This dataset should show the average life expectancy of each state. The following code imports the dataset and selects only the year 2020:
states |>inner_join(expect2020) |>ggplot() +geom_sf(aes(fill = RATE)) +coord_sf(datum =NA) +theme_minimal() +scale_fill_viridis_c(labels =label_comma()) +labs(title ="2020 Life Expectancy")
Joining with `by = join_by(NAME)`
Looks like a majority of Southern states have a lower life expectancy compared to a majority of Northern states, with the exception being Hawaii. The following histogram shows the average life expectancy of all states:
expect2020 |>ggplot(aes(x = RATE)) +geom_histogram() +scale_x_continuous(labels = comma) +theme_minimal() +xlab("2020 Life Expectancy in All States")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
I personally prefer using the leaflet() command, as you can personally look at each states’ data. Here is a different choropleth of the same dataset using leaflet:
states_leaflet <-get_acs(geography ="state", # gets state by state datavariables ="B19013_001", # this is state incomegeometry =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)`.
Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'
Lastly, this is a plotly histogram to accompany the data:
expect2020 |>plot_ly(x =~RATE) |>add_histogram()
Police Shootings in Montana
The following dataset was obtained from The Washington Post’s Fatal Police Shootings dataset. Using this dataset, we will specifically look at all cases in Montana.
Assuming "longitude" and "latitude" are longitude and latitude, respectively
Warning in validateCoords(lng, lat, funcName): Data contains 11 rows with
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If you hover over each case, it should tell you the threat that police was facing at the time, including the suspect directly attacking the officer (attack). Here is a count of how many threats of each category were shot down:
