# Practice drawing vector maps

library(tidyverse)
library(sf)
library(tidycensus)
library(viridis)

# useful on MacOS to speed up rendering of geom_sf() objects
if (!identical(getOption("bitmapType"), "cairo") && isTRUE(capabilities()[["cairo"]])) {
options(bitmapType = "cairo")
}

options(digits = 3)
set.seed(1234)
theme_set(theme_minimal())


## American Community Survey

The U.S. Census Bureau conducts the American Community Survey which gathers detailed information on topics such as demographics, employment, educational attainment, etc. They make a vast portion of their data available through an application programming interface (API), which can be accessed intuitively through R via the tidycensus package. We previously discussed how to use this package to obtain statistical data from the decennial census. However the Census Bureau also has detailed information on political and geographic boundaries which we can combine with their statistical measures to easily construct geospatial visualizations.

If you have not already, obtain an API key and store it securely on your computer.

## Exercise: Visualize income data

1. Obtain information on median household income in 2017 for Cook County, IL at the tract-level using the ACS. To retrieve the geographic features for each tract, set geometry = TRUE in your function.

You can use load_variables(year = 2017, dataset = &quot;acs5&quot;) to retrieve the list of variables available and search to find the correct variable name.



Click for the solution
cook_inc <- get_acs(
state = "IL",
county = "Cook",
geography = "tract",
variables = c(medincome = "B19013_001"),
year = 2017,
geometry = TRUE
)
cook_inc
## Simple feature collection with 1319 features and 5 fields (with 1 geometry empty)
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -88.3 ymin: 41.5 xmax: -87.5 ymax: 42.2
## Geodetic CRS:  NAD83
## First 10 features:
##          GEOID                                       NAME  variable estimate
## 1  17031010201 Census Tract 102.01, Cook County, Illinois medincome    40841
## 2  17031030200    Census Tract 302, Cook County, Illinois medincome    64089
## 3  17031031700    Census Tract 317, Cook County, Illinois medincome    44555
## 4  17031031900    Census Tract 319, Cook County, Illinois medincome    61211
## 5  17031050200    Census Tract 502, Cook County, Illinois medincome    74375
## 6  17031051300    Census Tract 513, Cook County, Illinois medincome   149271
## 7  17031061500    Census Tract 615, Cook County, Illinois medincome   117656
## 8  17031062600    Census Tract 626, Cook County, Illinois medincome   144211
## 9  17031063400    Census Tract 634, Cook County, Illinois medincome    95488
## 10 17031070600    Census Tract 706, Cook County, Illinois medincome   151250
##      moe                       geometry
## 1   7069 MULTIPOLYGON (((-87.7 42, -...
## 2  12931 MULTIPOLYGON (((-87.7 42, -...
## 3  12220 MULTIPOLYGON (((-87.7 42, -...
## 4   6343 MULTIPOLYGON (((-87.7 42, -...
## 5  18773 MULTIPOLYGON (((-87.7 42, -...
## 6  26389 MULTIPOLYGON (((-87.7 41.9,...
## 7  11416 MULTIPOLYGON (((-87.7 41.9,...
## 8  22537 MULTIPOLYGON (((-87.7 41.9,...
## 9   4904 MULTIPOLYGON (((-87.6 41.9,...
## 10 47800 MULTIPOLYGON (((-87.7 41.9,...

1. Draw a choropleth using the median household income data. Use a continuous color gradient to identify each tract’s median household income.

Click for the solution

ggplot(data = cook_inc) +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = estimate, color = estimate)) +
# increase interpretability of graph
scale_color_continuous(labels = scales::dollar) +
scale_fill_continuous(labels = scales::dollar) +
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


## Exercise: Customize your maps

1. Draw the same choropleth for Cook County, but convert median household income into a discrete variable with 6 levels.

Click for the solution

* Using cut_interval():

cook_inc %>%
mutate(inc_cut = cut_interval(estimate, n = 6)) %>%
ggplot() +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = inc_cut, color = inc_cut)) +
# increase interpretability of graph
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


* Using cut_number():

cook_inc %>%
mutate(inc_cut = cut_number(estimate, n = 6)) %>%
ggplot() +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = inc_cut, color = inc_cut)) +
# increase interpretability of graph
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


2. Draw the same choropleth for Cook County using the discrete variable, but select an appropriate color palette using Color Brewer.

Click for the solution

* Using cut_interval() and the Blue-Green palette:

cook_inc %>%
mutate(inc_cut = cut_interval(estimate, n = 6)) %>%
ggplot() +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = inc_cut, color = inc_cut)) +
scale_fill_brewer(type = "seq", palette = "BuGn") +
scale_color_brewer(type = "seq", palette = "BuGn") +
# increase interpretability of graph
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


* Using cut_number() and the Blue-Green palette:

cook_inc %>%
mutate(inc_cut = cut_number(estimate, n = 6)) %>%
ggplot() +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = inc_cut, color = inc_cut)) +
scale_fill_brewer(type = "seq", palette = "BuGn") +
scale_color_brewer(type = "seq", palette = "BuGn") +
# increase interpretability of graph
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


You can choose any palette that is for sequential data.


3. Use the viridis color palette for the Cook County map drawn using the continuous measure.

Click for the solution

ggplot(data = cook_inc) +
# use fill and color to avoid gray boundary lines
geom_sf(aes(fill = estimate, color = estimate)) +
# increase interpretability of graph
scale_color_viridis(labels = scales::dollar) +
scale_fill_viridis(labels = scales::dollar) +
labs(
title = "Median household income in Cook County, IL",
subtitle = "In 2017",
color = NULL,
fill = NULL,
caption = "Source: American Community Survey"
)


### Session Info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.0.4 (2021-02-15)
##  os       macOS Big Sur 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/Chicago
##  date     2021-05-25
##
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date       lib source
##  assertthat    0.2.1   2019-03-21 [1] CRAN (R 4.0.0)
##  backports     1.2.1   2020-12-09 [1] CRAN (R 4.0.2)
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##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library