In order to make maps, we first need some data.2


We need the tidyverse and tidycensus packages.

Note that to use tidycensus, you’ll need a Census API key. You can request a key here. Once you have a key, use the census_api_key() function:

census_api_key("API KEY")  # You must replace API KEY with your actual key.

If you run the census_api_key() function with the option install = TRUE, it will save your API key in your .Renviron so you don’t have to run census_api_key() every time you want to get data from the Census. However, you should only do this once. You can examine/edit your .Renviron file directly with usethis::edit_r_environ().

There are two main sources we will be getting data from: the American Community Survey (ACS) and the Decennial Census. Both sources can be seen at

When you click on, you will be brought to a homepage with a search bar. You can then type the topic of interest.

For example, you could search for “resident population”, and it will bring you to a page of various sources. Under each bolded title, you will see Survey/Program , which tells you the data source. We will only be focusing on the ACS and Decennial Census sources.

To get data from the Decennial Census, use get_decennial(). The key arguments here are geography, variables, and year.

  • geography determines the unit of analysis (i.e. the “geography” of your data. For example, we could use “state”, but there are many other geographies you could use, such as “us” for the entire country, “county” for counties, and so on.

  • variables selects which Census variables you want. To know which variable you are dealing with, you have two options.

The first way: look at the website. In addition to Survey/Program, you will also see the bolded term Tables. It will tell you the name of the variable. However, the variable names are a little tricky. For example, the variable name for population below is “B01003”. Make sure that the ID is a 6 digit string. If it isn’t that is a Table ID and does not work.

This way is good if you already know the exact name of the variable that you want, but it’s not the most useful for finding a general description because it doesn’t provide any context for what the variable is. It also doesn’t work for most complex variables since they’re pretty hard to find.

The second way: look at the census API documentation for information about the variables. By visiting the API documentation, you can see the variables that the Census officially uses.

This allows you to see all of the variables that are used in the Census, and by pressing “Ctrl+F” or “Cmd+F” you can find the variables that have a certain word within them. However, you can’t do advanced search operations like multiple searches and you have to do it manually.

The third way: use the load_variables() function in tidycensus to generate a tibble of variable names (described here). The two key arguments to load_variables() are the year and dataset. For the year, you must use the year or end-year of the Decennial Census. For the dataset argument, you can either use “sf1” and “sf3”.

This way is the best for finding variables quickly within R, as you can use dbplyr commands like filter() to find the variables you want. That lets you do advanced search commands and quickly find the variables you want.

  • year is the last argument to get_decennial() that we will be using. get_decennial() can obtain data from the 1990, 2000, and 2010 Census. You can find the year for your variable under Years .

Now that we have a better understanding the arguments to the function get_decennial(), let’s practice using it!

Consider the following code.


pop <- get_decennial(geography = "state",
                     variables = "P001001",
                     year = 2010)

## Rows: 52
## Columns: 4
## $ GEOID    <chr> "01", "02", "04", "05", "06", "22", "21", "08", "09", "10", "…
## $ NAME     <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Lo…
## $ variable <chr> "P001001", "P001001", "P001001", "P001001", "P001001", "P0010…
## $ value    <dbl> 4.8e+06, 7.1e+05, 6.4e+06, 2.9e+06, 3.7e+07, 4.5e+06, 4.3e+06…

The output is a tibble with four columns:

  • GEOID is part of the FIPS code, which is short for Federal Information Processing Standard. It’s a standardized way to identify states, counties, census tracts, etc. In this instance it’s only two characters wide. The more specific you get into the Census boundaries, the longer the code becomes.
  • NAME is the generic name get_decennial() gives to the unit you selected with geography; here, they are state names. Note that there are 52 observations; the “state” geography includes the District of Columbia and Puerto Rico, along with the 50 states.
  • variable is the name of the variable you selected.
  • value is the value of the variable you selected (here, population).

By default, get_decennial() will stack all the variables on top of each other if you select more than one, identifying them with the variable column. So let’s say that you wanted to know the proportion of the population of each state that lives in rural areas. You would select two variables (total population and rural population) and would receive a tibble with 104 observations, with each state appearing once per variable. This may not be the most helpful way to receive the data, depending on your purposes. (When faceting, you may want the data in a long format like this, as we’ll see below.) You can request the data in wide format instead by using the option output = "wide".

rural <- get_decennial(geography = "state",
                       variables = c("P001001", "P002005"),
                       year = 2010,
                       output = "wide")
## Rows: 52
## Columns: 4
## $ GEOID   <chr> "01", "02", "04", "05", "06", "22", "21", "08", "09", "10", "1…
## $ NAME    <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Lou…
## $ P001001 <dbl> 4.8e+06, 7.1e+05, 6.4e+06, 2.9e+06, 3.7e+07, 4.5e+06, 4.3e+06,…
## $ P002005 <dbl> 1957932, 241338, 651358, 1278329, 1880350, 1215567, 1806024, 6…

Here, we created a tibble with states in the rows and total population (“P001001”) and rural population (“P002005”) in the columns. To plot the proportion of each state’s population that lives in rural areas is now a simple application of tidyverse functions we know and love. First, let’s create a variable for rural population proportion and order the states by that variable:

rural %>%
  mutate(prop_rural = P002005/P001001) %>% 
  ggplot(aes(x = prop_rural, y = fct_reorder(NAME, prop_rural))) +
    geom_point() +
    labs(title = "Rural Population in US States in 2010",
         subtitle = "Maine and Vermont are the most rural states",
         caption = "Source: US Census",
         x = "Rural Population Proportion",
         y = NULL)

Maine and Vermont are very rural while Washington D.C. is entirely urban. What if we wanted a sense of how the proportion of rural residents varied geographically? We need a map!

Conceptual introduction to mapping

There are two underlying important pieces of information for spatial data: the coordinates of the object and how the coordinates relate to a physical location on Earth, which is also known as a coordinate reference system or CRS.

The coordinates are familiar from geography. A CRS uses a three-dimensional model of the earth to define specific locations on the surface of the grid. An object can be defined in relation to longitude (East/West) and latitude (North/South).

Where this gets complicated is when attempting to create a projection. A projection is a translation of the three-dimensional grid onto a two-dimensional plane. The animation below demonstrates this process.

Thus, the CRS determines how a geometric object will look when displayed on your two-dimensional screen. We rarely need to specify a CRS when working with tidycensus, but it is good to know about the concept if you ever work with other spatial data.

Vector versus spatial data

Spatial data with a defined CRS can either be vector or raster data. Vector data is based on points that can be connected to form lines and polygons. It is located within a coordinate reference system. An example is a road map.

Raster data, however, are values within a grid system, such as satellite imagery. In this Primer, we will only be dealing with vector data, which is the format in which we get data from the tidycensus package.

sf vs sp

An older package, sp, lets a user handle both vector and raster data. This book will focus on vector data and the sf package. The main differences between the sp and sf packages are how they store CRS information. While sp uses spatial sub classes, sf stores data in data frames, allowing it to interact with dplyr methods we’ve learned so far.


R can handle importing different kinds of file formats for spatial data, including KML and geojson. We’ll focus on shapefiles, which were created by Esri in the 1990s. Though we refer to a “shapefile” in the singular, it’s actually a collection of at least three basic files:

  • .shp - lists shape and vertices
  • .shx - has index with offsets
  • .dbf - relationship file between geometry and attributes (data)

All files must be present in the directory and named the same (except for the file extension) to import correctly. Thankfully, tidycensus will grab the geometric information from the Census shapefile for you.

Mapping with tidycensus and geom_sf()

In order to start mapping in R, we need to get a little more data from the tidycensus package. In particular, we need to set geometry = TRUE.

rural <- get_decennial(geography = "state",
                       variables = c("P001001", "P002005"),
                       year = 2010,
                       output = "wide",
                       geometry = TRUE) 

## Rows: 52
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## Columns: 5
## $ GEOID    <chr> "01", "02", "04", "05", "06", "22", "21", "08", "09", "10", "…
## $ NAME     <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Lo…
## $ P001001  <dbl> 4.8e+06, 7.1e+05, 6.4e+06, 2.9e+06, 3.7e+07, 4.5e+06, 4.3e+06…
## $ P002005  <dbl> 1957932, 241338, 651358, 1278329, 1880350, 1215567, 1806024, …
## $ geometry <MULTIPOLYGON [°]> MULTIPOLYGON (((-85 31, -85..., MULTIPOLYGON (((…

This is similar to the tibble we created before. However, there are two key differences. First, we now have this funky “multipolygon” column called geometry. This is a list column containing all the information ggplot() needs to create a map. Second, rural is no longer a tibble.

## [1] "sf"         "tbl_df"     "tbl"        "data.frame"

class() is a function which tells us the, uh, “class” of an object. rural is of class “sf”, which is a special kind of tibble which includes information for plotting. For this reason, you should never use as_tibble() on an object of class “sf” since doing so strips the object of the key attributes it needs to make plotting easier.

In order to create a map using ggplot(), we need a new geom: geom_sf(). This works much like the geoms we have seen before, such as geom_point() and geom_line(), except with spatial data. In particular, it is designed to work with objects of class “sf.” Example:

rural %>%
  ggplot() +
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()

We have the boundaries of each state, including Alaska and Hawaii. But there are some problems. ggplot2 is doing its best to fit everything on one image, which is taxing on the system. We can’t see any particular state very well, because the map is zoomed far out. Also, there are no colors because we didn’t fill it with our data.

So, let’s create a new map with geom_sf() and fill it with prop_rural. And we’ll filter out Alaska, Hawaii, and Puerto Rico for now.

rural %>%
  filter(! NAME %in% c("Alaska", "Hawaii", "Puerto Rico")) %>%
  ggplot(aes(fill = P002005 / P001001)) +
## old-style crs object detected; please recreate object with a recent sf::st_crs()

Now we have something usable! This already has a lot of what we’d want from a map—most notably, the states are shaded based on our variable of interest, helping us to see some patterns in the data. But it could use a bit of a makeover, which we’ll give it in the next section.

Making maps pretty

There are a few ways we can aesthetically improve this map:

  • Make the fill colors easier to distinguish
  • Make it so that darker colors map onto higher values of prop_rural
  • Remove the gray background
  • Give the legend an informative title and add a title and caption

A great function for providing the fill colors for maps is scale_fill_viridis_c(). This has a few different color palettes that can be selected with the option argument, all of which are easily distinguishable both when displayed in black and white and for people with common forms of colorblindness. You can also reverse the default order of the colors with the direction = -1 option. This function is for continuous variables such as prop_rural; if you have a discrete variable, you can use the analogous scale_fill_viridis_d().

We’ll also use theme_void(), a great theme for maps that gets rid of the gray background. Finally, we’ll use labs() to give the legend the title “Percent Rural” (and multiply the values of the variable by 100) and add an overall title and caption.

rural %>%
  filter(! NAME %in% c("Alaska", "Hawaii", "Puerto Rico")) %>%
  ggplot(aes(fill = 100 * P002005 / P001001)) +
    geom_sf() + 
    scale_fill_viridis_c(option = "plasma",
                         direction = -1) +
    labs(title = "Rural geography of the United States",
         caption = "Source: Census 2010",
         fill = "Percent Rural") +
## old-style crs object detected; please recreate object with a recent sf::st_crs()

With this map, it is clear that the more rural states are concentrated in the Great Plains, the South, and parts of New England, while the (South)west and Northeast are less rural.

Adding back Alaska and Hawaii

But what about Alaska and Hawaii? If you want to display those on your map without having to zoom out, you can take advantage of an argument in get_decennial(), shift_geo = TRUE:

rural_shifted <- get_decennial(geography = "state",
                               variables = c("P001001", "P002005"),
                               year = 2010,
                               output = "wide",
                               geometry = TRUE,
                               shift_geo = TRUE) %>%
  rename(state = NAME) %>%
  mutate(prop_rural = P002005/P001001)
rural_shifted %>%
  ggplot(aes(fill = prop_rural * 100)) +
  geom_sf() + 
  scale_fill_viridis_c(option = "plasma",
                       direction = -1) +
  labs(title = "Rural geography of the United States",
       caption = "Source: Census 2010",
       fill = "Percent Rural") +
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()

Now, Alaska and Hawaii can be displayed near the lower 48 states. This option removes Puerto Rico from your tibble altogether, so it is not a good choice if you want to show data from Puerto Rico.

Faceting maps

A powerful tool in ggplot2 to use with maps is faceting. Let’s grab data from the ACS on the population in Harris County, Texas census tracts by race:

racevars <- c(White = "B02001_002", 
              Black = "B02001_003", 
              Asian = "B02001_005",
              Hispanic = "B03003_003")
harris <- get_acs(geography = "tract",
                  variables = racevars, 
                  year = 2018,
                  state = "TX",
                  county = "Harris County",
                  geometry = TRUE,
                  summary_var = "B02001_001") 

This code is very similar to what we’ve used before, except here we are retrieving the data from the American Community Survey using get_acs() instead of from the decennial census. Some new features worth pointing out:

  • The year for get_acs() is the last year of a five year sample. Thus, our data will be from 2014–2018. You can choose years from 2009–2018.
  • Since our geography is “tract”, we are further specifying the state and county.
  • We are obtaining the data in a long format, which makes faceting easier.
  • We added a summary_var, “B02001_001”, which is the total population. As we’ll see, this appears as a separate column, which is helpful to us. (As an exercise, try going back to the code that created rural and see how you would do that in a long format with summary_var.)

Let’s take a look at harris:

## Rows: 3,144
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## Columns: 8
## $ GEOID       <chr> "48201100000", "48201100000", "48201100000", "48201100000"…
## $ NAME        <chr> "Census Tract 1000, Harris County, Texas", "Census Tract 1…
## $ variable    <chr> "White", "Black", "Asian", "Hispanic", "White", "Black", "…
## $ estimate    <dbl> 3426, 1045, 230, 892, 2936, 3591, 7, 2119, 2973, 885, 0, 3…
## $ moe         <dbl> 390, 308, 106, 241, 1358, 2196, 14, 1013, 430, 242, 13, 47…
## $ summary_est <dbl> 5063, 5063, 5063, 5063, 6820, 6820, 6820, 6820, 4403, 4403…
## $ summary_moe <dbl> 478, 478, 478, 478, 3685, 3685, 3685, 3685, 502, 502, 502,…
## $ geometry    <MULTIPOLYGON [°]> MULTIPOLYGON (((-95 30, -95..., MULTIPOLYGON …

These are similar to what we’ve seen before. Note that we now have moe and summary_moe columns, which stand for “margin of error.” This is because, unlike the decennial census, the ACS is a survey and thus the values we get are estimates of the true value.3

Transforming and mapping the data

Now we can use facet_wrap() to look at our race variables side-by-side:

harris %>%
  mutate(Percent = 100 * (estimate / summary_est)) %>%
  ggplot(aes(fill = Percent, color = Percent)) +
  facet_wrap(~ variable) +
  geom_sf() +
  scale_fill_viridis_c(direction = -1) +
  scale_color_viridis_c(direction = -1) +
  labs(title = "Racial geography of Harris County, Texas",
       caption = "Source: American Community Survey 2014-2018") +
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()
## old-style crs object detected; please recreate object with a recent sf::st_crs()

Note how easy it was to create the percentages using summary_est. We also used color = Percent and scale_color_viridis_c() to avoid having annoying borders around each of the census tracts. Otherwise, this doesn’t differ much from our code before, yet it is much easier to make comparisons across variables. Faceting is a powerful tool to use with maps.

Working with big data

Instead of a census tract map for just one city, let’s do a “big data” project involving every census track in the country, plotting the percentage of people who are two or more races.

Start by finding the correct variable in the American Community Survey by using the load_variables() function. This function takes two required arguments: the year of the Census or endyear of the ACS sample, and the specific dataset. Example:

acs2018 <- load_variables(2018, "acs5")


In the 2018 ACS, the variable we’re looking for is called "B02001_008". We also need total population ("B02001_001") to calculate a percentage. There are a total of 74,134 census tracts in the US. Note that the vector included base R, includes the names of every state in the US.

In this case, we use to make continental, a vector of every state in the US other than Alaska and Hawaii.

continental <-[! %in% c("Alaska", "Hawaii")]

races <- get_acs(geography = "tract",
                 state = continental,
                 variables = "B02001_008", 
                 year = 2018,
                 summary_var = "B02001_001",
                 geometry = TRUE)

## Simple feature collection with 72359 features and 7 fields (with 196 geometries empty)
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -120 ymin: 25 xmax: -67 ymax: 49
## Geodetic CRS:  NAD83
## # A tibble: 72,359 × 8
##    GEOID       NAME              variable estimate   moe summary_est summary_moe
##    <chr>       <chr>             <chr>       <dbl> <dbl>       <dbl>       <dbl>
##  1 01117030317 Census Tract 303… B02001_…      223   117        4100         301
##  2 01119011500 Census Tract 115… B02001_…       11    20        4380         441
##  3 01121010900 Census Tract 109… B02001_…       55    69        3102         389
##  4 01125010102 Census Tract 101… B02001_…       16    27        3026         348
##  5 01089000701 Census Tract 7.0… B02001_…       90    65        2617         389
##  6 01089002200 Census Tract 22,… B02001_…       76    48        1921         197
##  7 01089003000 Census Tract 30,… B02001_…       27    35        2589         345
##  8 01095030902 Census Tract 309… B02001_…      122    86        4504         379
##  9 01097000401 Census Tract 4.0… B02001_…        0    12        1068         177
## 10 01097000402 Census Tract 4.0… B02001_…        0    12         833         113
## # … with 72,349 more rows, and 1 more variable: geometry <MULTIPOLYGON [°]>

We set size to 0.003 to create thin outlines around our census tracts, any larger and they would make it hard to see our tracts. We also use the inferno option of scale_fill_viridis() for a different aesthetic from our previous plots. We add theme_void() and some labs() .

races  %>%
  mutate(Percent = 100 * (estimate / summary_est)) %>%
  ggplot(aes(fill = Percent)) +
  geom_sf(size = 0.003) +
  scale_fill_viridis_c(direction = -1, option = "inferno") +
  labs(title = "Percent of People Who are Two or More Races by Census Tract",
       caption = "Source: American Community Survey 2014-2018") +


Census microdata, often referred to as ‘Public Use Microdata Samples’ or PUMS, contains advanced census data on individual people. PUMS contains data for roughly 1% of the US population. To access PUMS, use the get_PUMS() function, which works in a very similar way to get_decennial() or get_acs().

If you’re having trouble finding which PUMS variables represents what, the tidycensus dataset pums_variables can be helpful.

## Rows: 37,021
## Columns: 12
## $ survey     <chr> "acs1", "acs1", "acs1", "acs1", "acs1", "acs1", "acs1", "ac…
## $ year       <chr> "2017", "2017", "2017", "2017", "2017", "2017", "2017", "20…
## $ var_code   <chr> "SERIALNO", "DIVISION", "DIVISION", "DIVISION", "DIVISION",…
## $ var_label  <chr> "Housing unit/GQ person serial number", "Division code base…
## $ data_type  <chr> "chr", "chr", "chr", "chr", "chr", "chr", "chr", "chr", "ch…
## $ level      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ val_min    <chr> "2017000000001", "0", "1", "2", "3", "4", "5", "6", "7", "8…
## $ val_max    <chr> "2017999999999", "0", "1", "2", "3", "4", "5", "6", "7", "8…
## $ val_label  <chr> "Unique identifier", "Puerto Rico", "New England (Northeast…
## $ recode     <lgl> FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
## $ val_length <int> 13, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 2, 2, 2…
## $ val_na     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Here we make US_pums contain age, sex, and income data for every single person in PUMS. AGEP is age, FINCP is income, and SEX is sex.

US_pums <- get_pums(variables = c("AGEP", "FINCP", "SEX"),
                    state =,
                    recode = TRUE,
                    survey = "acs1")

Because we are using every single individual in PUMS to make US_pums, we get an enormous tibble with over 3 million rows! This might look intimidating at first, but we can use the same basic dplyr functions as usual.

One trade-off with using PUMS data as compared to aggregated data is that you only get the state and public use microdata area (PUMA) of each individual in the microdata. PUMAs are Census geographies that contain at least 100,000 people and are entirely within a single state. They are built from census tracts and counties and may or may not be similar to other recognized geographic boundaries. In New York City, for instance, PUMAs are closely aligned to Community Districts. So, if you are interested in pulling data about block groups, census tracts, or other small areas, you can’t use PUMS data. get_pums() will always return SERIALNO, SPORDER, WGTP, PWGTP, and ST. SERIALNO and SPORDER are the variables that uniquely identify observations, WGTP and PWGTP are the housing-unit and person weights, and ST is the state code.

By setting recode = TRUE, we create new _label columns to recode these numerical values into what they represent.

Plotting with PUMS

But why is PUMS useful? Well, it allows us to create new interesting variables.

Let’s try making a map with PUMS. Let’s say we wanted to make a map of the percentage of the population of states in the Northwest that are seniors, which we will define as the percent of the population that is 65 or above. Although there’s no variable that will tell us this directly, we can use PUMS to construct it. We want our data to be as detailed as possible, so we will map our data by PUMAs. We define states in the northwest as Washington, Idaho, and Oregon.

First, we want to put your custom PUMS estimates on a map. To do this, let’s use the tigris package to download PUMA boundaries for the states in the northwest as an sf object.

nw_states <- c("OR", "WA", "ID")
nw_pumas <- map(nw_states, 
                class = "sf", 
                cb = TRUE) %>%

Now, we use get_pums() to get our data. We select PUMA and AGEP, which stand for PUMA and age respectively.

nw_pums <- get_pums(variables = c("PUMA", "AGEP"),
                    state = nw_states,
                    recode = TRUE,
                    survey = "acs1",
                    year = 2018)

Now, we use dplyr:group_by() and dplyr:summarize() to make a modified version of nw_pums with new variables. total_pop represents the total population of a PUMA, and pct_Senior represents the fraction of the population in that PUMA that’s 65 or over (>64).

nw_Senior <- nw_pums %>%
    group_by(ST, PUMA) %>%
    summarize(total_pop = sum(PWGTP), 
              pct_Senior = sum(PWGTP[AGEP > 64]) / total_pop,
              .groups = "drop")

Working with PUMS data can be little tricky, so before we get to mapping we have to left_join() nw_Senior back into nw_pums, mapping "STATEFP10" to ST, and "PUMACE10" to "PUMA". This is a confusing quirk of PUMS data that you do not need to worry about.

nw_final <- nw_pumas %>%
    left_join(nw_Senior, by = c("STATEFP10" = "ST", "PUMACE10" = "PUMA")) 

Then, we use geom_sf() to make our plot. We use scale_fill_viridis_b(option = "magma") and theme_void() to customize the look of our map, and use labels = scales::label_percent(1)) as a handy trick to convert pct_Senior’s fractions into percentages. Add some labs() and we’re done!

nw_final %>% 
  ggplot(aes(fill = pct_Senior)) +
    geom_sf() +
    scale_fill_viridis_b(name = NULL,
        option = "magma",
        labels = scales::label_percent(1)) +
    labs(title = "Percentage of population that are Seniors",
         caption = "Source: American Community Survey 2014-2018") +

Want to explore further?

  • Take a look at the tidycensus website.
  • If you have shapefiles from a place other than tidycensus, you can read them in using st_read() in the sf package, join them with other data using dplyr functions, and then map them with geom_sf() as we have shown above.
    • You may have to look into using coord_sf() if you have trouble displaying your data.
  • Want to add interactivity to your maps? Check out the leaflet package. Here’s a good introduction to using leaflet with tidycensus.
  • Practice your skills with Andrew Tran’s case study slides, where you can replicate a graphic from the Washington Post. Note: this involves some packages we haven’t shown you in this book, but if you follow along step by step you will be able to see how they are used.

Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set options(tigris_use_cache = TRUE).