diff --git a/resources/Mapping.Rmd b/resources/Mapping.Rmd index 689e4a2f..191dafe2 100644 --- a/resources/Mapping.Rmd +++ b/resources/Mapping.Rmd @@ -22,6 +22,30 @@ Maps can be tricky in R! There are many packages to choose from. - Some require API keys (e.g., `ggmap`, `tidycensus`) - Some are interactive (e.g., `leaflet`) +## What does a map in R look like? + +```{r message=FALSE, warning=FALSE, echo=FALSE} +library(tidyverse) # `geom_sf()` from ggplot2 +library(tidycensus) # `get_acs()` function for American Community Survey data + +wa_income <- get_acs( + geography = "tract", + variables = "B19013_001", # Income key + state = "WA", + year = 2022, + geometry = TRUE +) + +ggplot(data = wa_income, aes(fill = estimate)) + + geom_sf() + + labs(title = "Median Household Income by Census Tract", + x = "Longitude", + y = "Latitude") + + theme_classic() + + scale_fill_viridis_c() + + theme(legend.title = element_blank()) +``` + ## Data formats - boundary data ```{r message=FALSE} @@ -53,10 +77,14 @@ These objects store geometric shapes (like points, lines, or polygons) along wit the `geom` column is a `MULTIPOLYGON` — a geometry type representing complex shapes, which may consist of multiple polygons (e.g., islands or non-contiguous regions). +Federal Information Processing System (FIPS) Codes for States and Counties are numbers which uniquely identify geographic areas. See [this codebook](https://transition.fcc.gov/oet/info/maps/census/fips/fips.txt). + ## `ggplot` has spatial functions{.codesmall} `geom_polygon()` works with boundary data +Let's plot county outlines and major cities. + ```{r message=FALSE} library(tidyverse) # `map_data()` from ggplot2 library(maps) # `us.cities` data @@ -101,6 +129,8 @@ title(main = "Washington State Cities") ## `usmap` is compatible with ggplot +Let's fill each county based on its population. + ```{r message=FALSE} library(tidyverse) library(usmap) # `countypop` data and the `plot_usmap()` function @@ -170,13 +200,15 @@ plot_4 Use `geom_sf()` function with SF data. +Let's fill each census tract by median household income. + ```{r message=FALSE, warning=FALSE} library(tidyverse) # `geom_sf()` from ggplot2 library(tidycensus) # `get_acs()` function for American Community Survey data wa_income <- get_acs( geography = "tract", - variables = "B19013_001", # Income key + variables = "B19013_001", # Median income code state = "WA", year = 2022, geometry = TRUE diff --git a/resources/Mapping.html b/resources/Mapping.html index 783d89ed..29c33015 100644 --- a/resources/Mapping.html +++ b/resources/Mapping.html @@ -3270,6 +3270,12 @@

  • Some are interactive (e.g., leaflet)
    • +

    What does a map in R look like?

    + +
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    + +

    +

    Data formats - boundary data

    library(tidyverse)
@@ -3322,10 +3328,14 @@ 
    
 
 
    the geom column is a MULTIPOLYGON — a geometry type representing complex shapes, which may consist of multiple polygons (e.g., islands or non-contiguous regions).
    
 
+
    Federal Information Processing System (FIPS) Codes for States and Counties are numbers which uniquely identify geographic areas. See this codebook.
    
+

    ggplot has spatial functions

    geom_polygon() works with boundary data

    +

    Let’s plot county outlines and major cities.

    + 
    library(tidyverse) # `map_data()` from ggplot2
 library(maps) # `us.cities` data
 
@@ -3358,6 +3368,8 @@

    usmap is compatible with ggplot

    +

    Let’s fill each county based on its population.

    + 
    library(tidyverse)
 library(usmap) # `countypop` data and the `plot_usmap()` function
 
@@ -3417,19 +3429,19 @@ 
    
 
 
    Use geom_sf() function with SF data.
    
 
+
    Let’s fill each census tract by median household income.
    
+
 
    library(tidyverse) # `geom_sf()` from ggplot2
 library(tidycensus) # `get_acs()` function for American Community Survey data
 
 wa_income <- get_acs(
   geography = "tract", 
-  variables = "B19013_001", # Income key 
+  variables = "B19013_001", # Median income code
   state = "WA", 
   year = 2022,
   geometry = TRUE
 )
    
 
-
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-

    tidycensus is helpful for tract level

    ggplot(data = wa_income, aes(fill = estimate)) + 
diff --git a/resources/Mapping.pdf b/resources/Mapping.pdf
index dd86fdb5..e8441a90 100644
Binary files a/resources/Mapping.pdf and b/resources/Mapping.pdf differ