CSSS508, Lecture 9
Mapping
Michael Pearce
(based on slides from Chuck Lanfear)
May 24, 2023
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Topics

Last time, we learned about,

Basics of Strings
Strings in Base R
Strings in stringr (Tidyverse)

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Topics

Last time, we learned about,

Basics of Strings
Strings in Base R
Strings in stringr (Tidyverse)

Today, we will cover,

Basic mapping:
- Simple ggplots with coordinates
- Density plots in ggmap
- Labeling points with ggrepel
Advanced mapping:
- GIS with sf
- tidycensus

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Mapping in R: A quick plug

Mapping can be complex! If you are interested in digging deeper, here are some resources:

If you are interested in mapping, GIS, and geospatial analysis in R, acquire this book.
RSpatial.org is a great resource for working with spatial data.
For more information on spatial statistics, consider taking Jon Wakefield's CSSS 554: Statistical Methods for Spatial Data in the winter quarter.

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1. Basic mapping with ggmap4 / 45

One Day of SPD Incidents

Today, we'll study data from the Seattle Police Department regarding incidents on just one day: March 25, 2016.

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One Day of SPD Incidents

Today, we'll study data from the Seattle Police Department regarding incidents on just one day: March 25, 2016.

The data can be downloaded from my predecessor's Github using the code below (code in the R companion file).

library(ggplot2)
library(readr)
library(dplyr)
library(tidyr)
library(stringr)

spd_raw <- read_csv("https://clanfear.github.io/CSSS508/Seattle_Police_Department_911_Incident_Response.csv")

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Taking a `glimpse()`

glimpse(spd_raw)

## Rows: 706
## Columns: 19
## $ `CAD CDW ID`                  <dbl> 1701856, 1701857, 1701853, 170…
## $ `CAD Event Number`            <dbl> 16000104006, 16000103970, 1600…
## $ `General Offense Number`      <dbl> 2016104006, 2016103970, 201610…
## $ `Event Clearance Code`        <chr> "063", "064", "161", "245", "2…
## $ `Event Clearance Description` <chr> "THEFT - CAR PROWL", "SHOPLIFT…
## $ `Event Clearance SubGroup`    <chr> "CAR PROWL", "THEFT", "TRESPAS…
## $ `Event Clearance Group`       <chr> "CAR PROWL", "SHOPLIFTING", "T…
## $ `Event Clearance Date`        <chr> "03/25/2016 11:58:30 PM", "03/…
## $ `Hundred Block Location`      <chr> "S KING ST / 8 AV S", "92XX BL…
## $ `District/Sector`             <chr> "K", "S", "D", "M", "M", "B", …
## $ `Zone/Beat`                   <chr> "K3", "S3", "D2", "M1", "M3", …
## $ `Census Tract`                <dbl> 9100.102, 11800.602, 7200.106,…
## $ Longitude                     <dbl> -122.3225, -122.2680, -122.342…
## $ Latitude                      <dbl> 47.59835, 47.51985, 47.61422, …
## $ `Incident Location`           <chr> "(47.598347, -122.32245)", "(4…
## $ `Initial Type Description`    <chr> "THEFT (DOES NOT INCLUDE SHOPL…
## $ `Initial Type Subgroup`       <chr> "OTHER PROPERTY", "SHOPLIFTING…
## $ `Initial Type Group`          <chr> "THEFT", "THEFT", "TRESPASS", …
## $ `At Scene Time`               <chr> "03/25/2016 10:25:51 PM", "03/…

What does each row represent? What are the variables and values?

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Simple Plotting: Regular `ggplots`

Coordinates, such as longitude and latitude, can be provided in aes() as x and y values.

This is ideal when you don't need to place points over some map for reference.

ggplot(spd_raw, 
       aes(Longitude, Latitude)) + 
  geom_point() + 
  coord_fixed() + # evenly spaces x and y
  ggtitle("Seattle Police Incidents",
          subtitle="March 25, 2016") +
  theme_classic()

Sometimes, however, we want to plot these points over existing maps.

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Better plots with `ggmap`

ggmap is a package that works with ggplot2 to plot spatial data directly on map images.

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Better plots with `ggmap`

ggmap is a package that works with ggplot2 to plot spatial data directly on map images.

What this package does for you:

Queries servers for a map at the location and scale you want
Plots the image as a ggplot object
Lets you add more ggplot layers like points, 2D density plots, text annotations
Additional functions for interacting with Google Maps (beyond this course)

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Installation

We can install ggmap like other packages:

install.packages("ggmap")

Note: If prompted to "install from sources the package which needs compilation", I find that typing "no" works best!

Because the map APIs it uses change frequently, sometimes you may need to get a newer development version of ggmap from the author's GitHub. This can be done using the remotes package.

if(!requireNamespace("remotes")){install.packages("remotes")}
remotes::install_github("dkahle/ggmap", ref = "tidyup")

Note, this may require compilation on your computer.

library(ggmap)

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Quick Maps with `qmplot()`

qmplot will automatically set the map region based on your data:

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude)

All I provided was numeric latitude and longitude, and it placed the data points correctly on a raster map of Seattle.

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`get_map()`

qmplot() internally uses the function get_map(), which retrieves a base map layer. Some options:

location= search query or numeric vector of longitude and latitude
zoom= a zoom level (3 = continent, 10 = city, 21 = building)
maptype=: "watercolor", "toner", "toner-background", "toner-lite"
color=: "color" or "bw"

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`get_map()`

qmplot() internally uses the function get_map(), which retrieves a base map layer. Some options:

location= search query or numeric vector of longitude and latitude
zoom= a zoom level (3 = continent, 10 = city, 21 = building)
maptype=: "watercolor", "toner", "toner-background", "toner-lite"
color=: "color" or "bw"

There are fancier options available, but many require a Google Maps API.

See the help page for gmplot() or get_map for more information!

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Nicer Example

Let's add color to our plot from before

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude,
       color=I("navy")
)

I() is used here to specify set (constant) rather than mapped values.

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Nicer Example

Add transparency

qmplot(data = spd_raw,
       x = Longitude, 
       y = Latitude,
       color=I("navy"),
       alpha=I(0.5)
)

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Density plots14 / 45

Density Layers

We can create a spatial density plot using the layer function stat_density_2d():

What to use when creating a "density" plot (where the observations occur)
Aesthetics of the density plot
Additional layer functions for customization

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Basic Map

Start with basic plot, remove points:

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude)

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Add Density Layer

Add stat_density_2d layer:

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude)+
  stat_density_2d(
    aes(fill = stat(level)),
    geom = "polygon")

stat(level) indicates we want fill= to be based on level values calculated by the layer (i.e., number of observations).

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Color Scale

Specify color gradient

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude)+
  stat_density_2d(
    aes(fill = stat(level)),
    geom = "polygon", 
  )+
  scale_fill_gradient2(
    low = "white",
    mid = "yellow",
    high = "red")

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Customize transparency and legend

qmplot(data = spd_raw,
       geom = "blank",
       x = Longitude, 
       y = Latitude,
       darken = 0.5)+
  stat_density_2d(
    aes(fill = stat(level)), 
    geom = "polygon", 
    alpha = .2,
  )+
  scale_fill_gradient2(
    "Incident\nConcentration",
    low = "white", 
    mid = "yellow", 
    high = "red")+
  theme(legend.position = "bottom")

Done!

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Labeling points with ggrepel20 / 45

Focus in on Downtown Seattle

First, let's filter our data to downtown based on values "eyeballed" from our earlier map:

downtown <- spd_raw %>%
  filter(Latitude > 47.58, Latitude < 47.64,
         Longitude > -122.36, Longitude < -122.31)

We'll plot just these values from now on!

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Focus in on Downtown Seattle

First, let's filter our data to downtown based on values "eyeballed" from our earlier map:

downtown <- spd_raw %>%
  filter(Latitude > 47.58, Latitude < 47.64,
         Longitude > -122.36, Longitude < -122.31)

We'll plot just these values from now on!

Let's also make a dataframe that includes just assaults and robberies downtown:

assaults <- downtown %>% 
  filter(`Event Clearance Group` %in%
                  c("ASSAULTS", "ROBBERY")) %>%
  mutate(assault_label = `Event Clearance Description`)

We'll label these observations!

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Labels

Now let's plot the events and label them with geom_label():

qmplot(data = downtown,
       x = Longitude,
       y = Latitude,
       maptype = "toner-lite",
       color = I("firebrick"),
       alpha = I(0.5)) +  
  geom_label(data = assaults,
       aes(label = assault_label),
       size=2)

Note that one dataframe is used for points (downtown) and another for labels (assaults)!!

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Fixing Overlapping Labels

The ggrepel package lets use fix or reduce overlapping labels using the function geom_label_repel().

library(ggrepel)
qmplot(data = 
    downtown,
    x = Longitude,
    y = Latitude,
    maptype = "toner-lite", 
    color = I("firebrick"), 
    alpha = I(0.5)) + 
  geom_label_repel(
    data = assaults,
    aes(label = assault_label), 
    size=2)

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2. Advanced mapping24 / 45

`sf`

Until recently, the main way to work with geospatial data in R was through the sp package. sp works well but does not store data the same way as most GIS packages and can be bulky and complicated.

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`sf`

The more recent sf package implements the GIS standard of Simple Features in R.

sf is also integrated into the tidyverse: e.g. geom_sf() in ggplot2.

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`sf`

The more recent sf package implements the GIS standard of Simple Features in R.

sf is also integrated into the tidyverse: e.g. geom_sf() in ggplot2.

The package is somewhat new but is expected to replace sp eventually. The principle authors and contributors to sf are the same authors as sp but with new developers from the tidyverse as well.

Because sf is the new standard, we will focus on it today.

library(sf)

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Simple Features

A Simple Feature is a single observation with some defined geospatial location(s). Features are stored in special data frames (class sf) with two properties:

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Simple Features

A Simple Feature is a single observation with some defined geospatial location(s). Features are stored in special data frames (class sf) with two properties:

Geometry: Properties describing a location (usually on Earth).
- Usually 2 dimensions, but support for up to 4.
- Stored in a single reserved list-column (geom, of class sfc).¹
- Contain a defined coordinate reference system.

[1] A list-column is the same length as all other columns in the data, but each element contains sub-elements (class sfg) with all the geometrical components.

List-columns require special functions to manipulate, including removing them.

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Simple Features

A Simple Feature is a single observation with some defined geospatial location(s). Features are stored in special data frames (class sf) with two properties:

Geometry: Properties describing a location (usually on Earth).
- Usually 2 dimensions, but support for up to 4.
- Stored in a single reserved list-column (geom, of class sfc).¹
- Contain a defined coordinate reference system.

[1] A list-column is the same length as all other columns in the data, but each element contains sub-elements (class sfg) with all the geometrical components.

List-columns require special functions to manipulate, including removing them.

Attributes: Characteristics of the location (such as population).
- These are non-spatial measures that describe a feature.
- Standard data frame columns.

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Coordinate Reference Systems

Coordinate reference systems (CRS) specify what location on Earth geometry coordinates are relative to (e.g. what location is (0,0) when plotting).

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Coordinate Reference Systems

Coordinate reference systems (CRS) specify what location on Earth geometry coordinates are relative to (e.g. what location is (0,0) when plotting).

The most commonly used is WGS84, the standard for Google Earth, the Department of Defense, and GPS satellites.

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Coordinate Reference Systems

Coordinate reference systems (CRS) specify what location on Earth geometry coordinates are relative to (e.g. what location is (0,0) when plotting).

The most commonly used is WGS84, the standard for Google Earth, the Department of Defense, and GPS satellites.

There are two common ways to define a CRS in sf:

EPSG codes (epsg in R)
- Numeric codes which refer to a predefined CRS
- Example: WGS84 is 4326

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Coordinate Reference Systems

Coordinate reference systems (CRS) specify what location on Earth geometry coordinates are relative to (e.g. what location is (0,0) when plotting).

The most commonly used is WGS84, the standard for Google Earth, the Department of Defense, and GPS satellites.

There are two common ways to define a CRS in sf:

EPSG codes (epsg in R)
- Numeric codes which refer to a predefined CRS
- Example: WGS84 is 4326
PROJ.4 strings (proj4string in R)
- Text strings of parameters that define a CRS
- Example: NAD83(NSRS2007) / Washington North

+proj=lcc +lat_1=48.73333333333333 +lat_2=47.5 +lat_0=47 
+lon_0=-120.8333333333333 +x_0=500000 +y_0=0 +ellps=GRS80 
+towgs84=0,0,0,0,0,0,0 +units=m +no_defs

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Shapefiles

Geospatial data is typically stored in shapefiles which store geometric data as vectors with associated attributes (variables)

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Shapefiles

Geospatial data is typically stored in shapefiles which store geometric data as vectors with associated attributes (variables)

Shapefiles actually consist of multiple individual files. There are usually at least three (but up to 10+):

.shp: The feature geometries
.shx: Shape positional index
.dbf: Attributes describing features¹

Often there will also be a .prj file defining the coordinate system.

[2] This is just a dBase IV file which is an ancient and common database storage file format.

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Using sf29 / 45

Selected `sf` Functions

sf is a huge, feature-rich package. Here is a sample of useful functions:

st_read(), st_write(): Read and write shapefiles.
geom_sf(): ggplot() layer for sf objects.
st_as_sf(): Convert a data frame into an sf object.
st_join(): Join data by spatial relationship.
st_transform(): Convert between CRS.
st_drop_geometry(): Remove geometry from a sf data frame.
st_relate(): Compute relationships between geometries (like neighbor matrices).
st_interpolate_aw(): Areal-weighted interpolation of polygons.¹

[1] I recommend the dedicated areal package for this though!

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Loading Data

We will work with the voting data from Homework 5. You can obtain a shape file of King County voting precincts from the county GIS data portal.

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Loading Data

We will work with the voting data from Homework 5. You can obtain a shape file of King County voting precincts from the county GIS data portal.

We can load the file using st_read().

precinct_shape <- st_read("./data/district/votdst.shp",
                          stringsAsFactors = F) %>% 
  select(Precinct=NAME, geometry)

## Reading layer `votdst' from data source 
##   `/Users/pearce790/CSSS508/Lectures/Lecture9/data/district/votdst.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 2592 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1220179 ymin: 31555.16 xmax: 1583562 ymax: 287678
## Projected CRS: NAD83(HARN) / Washington North (ftUS)

If following along, click here to download a zip of the shapefile.

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Voting Data: Processingprecincts_votes_sf <- 
  read_csv("./data/king_county_elections_2016.txt") %>%
  filter(Race=="US President & Vice President",
         str_detect(Precinct, "SEA ")) %>% 
  select(Precinct, CounterType, SumOfCount) %>%
  group_by(Precinct) %>%
  filter(CounterType %in% 
           c("Donald J. Trump & Michael R. Pence",
             "Hillary Clinton & Tim Kaine",
             "Registered Voters",
             "Times Counted")) %>%
  mutate(CounterType =
           recode(CounterType, 
                  `Donald J. Trump & Michael R. Pence` = "Trump",
                  `Hillary Clinton & Tim Kaine` = "Clinton",
                  `Registered Voters`="RegisteredVoters",
                  `Times Counted` = "TotalVotes")) %>%
  spread(CounterType, SumOfCount) %>%
  mutate(P_Dem = Clinton / TotalVotes, 
         P_Rep = Trump / TotalVotes, 
         Turnout = TotalVotes / RegisteredVoters) %>%
  select(Precinct, P_Dem, P_Rep, Turnout) %>% 
  filter(!is.na(P_Dem)) %>%
  left_join(precinct_shape) %>%
  st_as_sf() # Makes sure resulting object is an sf dataframe

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Taking a `glimpse()`

glimpse(precincts_votes_sf)

## Rows: 960
## Columns: 5
## Groups: Precinct [960]
## $ Precinct <chr> "SEA 11-1256", "SEA 11-1550", "SEA 11-1552", "SEA 1…
## $ P_Dem    <dbl> 0.7707510, 0.8168421, 0.7507987, 0.8376328, 0.83259…
## $ P_Rep    <dbl> 0.15612648, 0.07789474, 0.13418530, 0.08649469, 0.0…
## $ Turnout  <dbl> 0.6931507, 0.7274119, 0.7347418, 0.7522831, 0.75792…
## $ geometry <MULTIPOLYGON [US_survey_foot]> MULTIPOLYGON (((1273698 1…

Notice the geometry column and its unusual class: MULTIPOLYGON

A single observation (row) has a geometry which may consist of multiple polygons.

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Voting Map

We can plot sf geometry using geom_sf().

ggplot(precincts_votes_sf, 
       aes(fill=P_Dem)) +
  geom_sf(size=NA) +
  theme_void() +
  theme(legend.position = 
          "right")

fill=P_Dem maps color inside precincts to P_Dem.
size=NA removes precinct outlines.
theme_void() removes axes and background.

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tidycensus35 / 45

`tidycensus`

tidycensus can be used to search the American Community Survey (ACS) and Dicennial Census for variables, then download them and automatically format them as tidy dataframes.

These dataframes include geographical boundaries such as tracts!

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`tidycensus`

tidycensus can be used to search the American Community Survey (ACS) and Dicennial Census for variables, then download them and automatically format them as tidy dataframes.

These dataframes include geographical boundaries such as tracts!

This package utilizes the Census API, so you will need to obtain a Census API key.

Application Program Interface (API): A type of computer interface that exists as the "native" method of communication between computers, often via http (usable via httr package).

R packages that interface with websites and databases typically use APIs.
APIs make accessing data easy while allowing websites to control access.

See the developer's GitHub page for detailed instructions.

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Key `tidycensus` Functions

census_api_key() - Install a census api key.
- Note you will need to run this prior to using any tidycensus functions.
load_variables() - Load searchable variable lists.
- year =: Sets census year or endyear of 5-year ACS
- dataset =: Sets dataset (see ?load_variables)
get_decennial() - Load Census variables and geographical boundaries.
- variables =: Provide vector of variable IDs
- geography =: Sets unit of analysis (e.g. state, tract, block)
- year =: Census year (1990, 2000, or 2010)
- geometry = TRUE: Returns sf geometry
get_acs() - Load ACS variables and boundaries.

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Searching for Variables

library(tidycensus)
# census_api_key("PUT YOUR KEY HERE", install=TRUE)
acs_2015_vars <- load_variables(2015, "acs5")
acs_2015_vars[10:18,] %>% print()

## # A tibble: 9 × 4
##   name        label                                  concept geography
##   <chr>       <chr>                                  <chr>   <chr>    
## 1 B01001A_008 Estimate!!Total!!Male!!20 to 24 years  SEX BY… tract    
## 2 B01001A_009 Estimate!!Total!!Male!!25 to 29 years  SEX BY… tract    
## 3 B01001A_010 Estimate!!Total!!Male!!30 to 34 years  SEX BY… tract    
## 4 B01001A_011 Estimate!!Total!!Male!!35 to 44 years  SEX BY… tract    
## 5 B01001A_012 Estimate!!Total!!Male!!45 to 54 years  SEX BY… tract    
## 6 B01001A_013 Estimate!!Total!!Male!!55 to 64 years  SEX BY… tract    
## 7 B01001A_014 Estimate!!Total!!Male!!65 to 74 years  SEX BY… tract    
## 8 B01001A_015 Estimate!!Total!!Male!!75 to 84 years  SEX BY… tract    
## 9 B01001A_016 Estimate!!Total!!Male!!85 years and o… SEX BY… tract

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Getting Data

king_county <- get_acs(geography="tract", state="WA", 
                       county="King", geometry = TRUE,
                       variables=c("B02001_001E", 
                                   "B02009_001E"), 
                       output="wide")

What do these look like?

glimpse(king_county)

## Rows: 495
## Columns: 7
## $ GEOID       <chr> "53033010102", "53033005803", "53033004901", "53…
## $ NAME        <chr> "Census Tract 101.02, King County, Washington", …
## $ B02001_001E <dbl> 4539, 3268, 4126, 2574, 4032, 3274, 3421, 3652, …
## $ B02001_001M <dbl> 697, 394, 532, 473, 531, 534, 367, 306, 488, 455…
## $ B02009_001E <dbl> 876, 138, 143, 225, 1187, 229, 215, 276, 16, 101…
## $ B02009_001M <dbl> 445, 133, 95, 207, 382, 239, 144, 178, 16, 90, 4…
## $ geometry    <MULTIPOLYGON [°]> MULTIPOLYGON (((-122.291 47..., MUL…

With output="wide", estimates end in E and error margins in M.

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Processing Data

We can drop the margins of error, rename the estimates then, mutate() into a proportion Any Black measure.

king_county <-  king_county %>%
  select(-ends_with("M")) %>%
  rename(`Total Population`=B02001_001E,
         `Any Black`=B02009_001E) %>%
  mutate(`Any Black` = `Any Black` / `Total Population`)
glimpse(king_county)

## Rows: 495
## Columns: 5
## $ GEOID              <chr> "53033010102", "53033005803", "5303300490…
## $ NAME               <chr> "Census Tract 101.02, King County, Washin…
## $ `Total Population` <dbl> 4539, 3268, 4126, 2574, 4032, 3274, 3421,…
## $ `Any Black`        <dbl> 0.192994052, 0.042227662, 0.034658265, 0.…
## $ geometry           <MULTIPOLYGON [°]> MULTIPOLYGON (((-122.291 47.…

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Mapping Code

king_county %>% 
  ggplot(aes(fill = `Any Black`)) + 
  geom_sf(size = NA) + 
  coord_sf(crs = "+proj=longlat +datum=WGS84", datum=NA) + 
  scale_fill_continuous(name = "Any Black\n", 
                        low  = "#d4d5f9",
                        high = "#00025b") + 
  theme_minimal() + ggtitle("Proportion Any Black")

New functions:

geom_sf() draws Simple Features coordinate data.
- size = NA removes outlines
coord_sf() is used here with these arguments:
- crs: Modifies the coordinate reference system (CRS); WGS84 is possibly the most commonly used CRS.
- datum=NA: Removes graticule lines, which are geographical lines such as meridians and parallels.

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Removing Water

With a simple function and boundaries of water bodies in King County, we can replace water with empty space.

st_erase <- function(x, y) {
  st_difference(x, st_make_valid(st_union(st_combine(y))))
}
kc_water <- tigris::area_water("WA", "King", class = "sf")
kc_nowater <- king_county %>% 
  st_erase(kc_water)

st_combine() merges all geometries into one
st_union() resolves internal boundaries
st_difference() subtracts y geometry from x
st_make_valid() fixes geometry errors from subtraction
area_water() obtains sf geometry of water bodies

Then we can reproduce the same plot using kc_nowater...

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Lab/Homework

For the remainder of class, we'll work on Homework 8, which is due next week!

The first part is based on Lecture 8 (strings)
- This includes lab questions from last lecture!
The second part is based on Lecture 9 (mapping)
- This includes an analysis of the restaurant data from last week!

There is no lab next week due to Memorial Day!

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CSSS508, Lecture 9

Mapping

Michael Pearce(based on slides from Chuck Lanfear)

May 24, 2023

Topics

Topics

Mapping in R: A quick plug

1. Basic mapping with ggmap

One Day of SPD Incidents

One Day of SPD Incidents

Taking a glimpse()

Simple Plotting: Regular ggplots

Better plots with ggmap

Better plots with ggmap

Installation

Quick Maps with qmplot()

get_map()

get_map()

Nicer Example

Nicer Example

Density plots

Density Layers

Basic Map

Add Density Layer

Color Scale

Customize transparency and legend

Labeling points with ggrepel

Focus in on Downtown Seattle

Focus in on Downtown Seattle

Labels

Fixing Overlapping Labels

2. Advanced mapping

sf

sf

sf

Simple Features

Simple Features

Simple Features

Coordinate Reference Systems

Coordinate Reference Systems

Coordinate Reference Systems

Coordinate Reference Systems

Shapefiles

Shapefiles

Using sf

Selected sf Functions

Loading Data

Loading Data

Voting Data: Processing

Taking a glimpse()

Voting Map

tidycensus

tidycensus

tidycensus

Key tidycensus Functions

Searching for Variables

Getting Data

Processing Data

Mapping Code

Removing Water

Lab/Homework

Topics

Help

Michael Pearce
(based on slides from Chuck Lanfear)

1. Basic mapping with `ggmap`

Taking a `glimpse()`

Simple Plotting: Regular `ggplots`

Better plots with `ggmap`

Better plots with `ggmap`

Quick Maps with `qmplot()`

`get_map()`

`get_map()`

Labeling points with `ggrepel`

`sf`

`sf`

`sf`

Using `sf`

Selected `sf` Functions

Taking a `glimpse()`

`tidycensus`

`tidycensus`

`tidycensus`

Key `tidycensus` Functions