US State GDP Map

with leaflet

Author

Tony Duan

This document demonstrates how to create a choropleth map of the United States, where each state is colored based on its per capita GDP in 2022. We will use the leaflet package in R to create the interactive map.

1 Data Loading and Preparation

First, we load the necessary R libraries for data manipulation, visualization, and working with geospatial data.

{ r} # Load the tidyverse package for data manipulation and visualization. library(tidyverse) # Load the rvest package for web scraping. library(rvest) # Load the chromote package for controlling a headless Chrome browser. library(chromote) # Load the janitor package for data cleaning. library(janitor) # Load the tidygeocoder package for geocoding. library(tidygeocoder) # Load the leaflet package for creating interactive maps. library(leaflet) # Load the sf package for working with simple features. library(sf)

1.1 Loading Geospatial and GDP Data

We will load the US states geospatial data from a GeoJSON file and the GDP data from an Excel file.

{ r} # Load the US states map data from the maps package. library(maps) mapStates = map("state", fill = TRUE, plot = FALSE)

US location data: { r} # Read the US states GeoJSON file. json_data=read_sf("us-states.json")

{ r} # Convert the sf object to a data frame. map_df <- as.data.frame(json_data)

{ r} # Get a glimpse of the mapStates data. glimpse(mapStates)

US 2022 GDP data:

```{ r} # Load the openxlsx and readxl packages for reading Excel files. library(openxlsx) library(readxl)

2 Read the US state GDP data from an Excel file and clean up the state names.

per_gdp_usd=read_excel(‘US state gpd 2022.xlsx’) %>% mutate(names=str_replace(names,‘*’,’’) %>% str_trim() )


```{
r}
# Get a glimpse of the GDP data.
glimpse(per_gdp_usd)

2.1 Merging and Preparing the Data

We will merge the geospatial data with the GDP data.

{ r} # Merge the map data with the GDP data by state name. map_df002 <- merge(map_df, per_gdp_usd, by.x = "name", by.y = "names")

{ r} # Get a glimpse of the merged data. glimpse(map_df002)

{ r} # Convert the merged data frame back to an sf object. map_sf002 <- sf::st_as_sf(map_df002, sf_column_name = "geometry")

3 Creating the Choropleth Map

Now we can create the choropleth map of the US states.

```{ r} # Create a color palette for the map. pal <- colorNumeric( palette = “Blues”, domain = map_df002$per_gdp_usd )

4 Create a color bin palette for the map.

binpal <- colorBin(“Blues”,map_df002$per_gdp_usd, 20, pretty = FALSE)

5 Create the leaflet map.

m <- leaflet(map_sf002) %>% # Add default OpenStreetMap map tiles. addTiles() %>%
# Add polygons for the states. addPolygons(smoothFactor = 0.3, fillOpacity = 0.5,weight = 1, # Set the fill color based on the per capita GDP. color = ~binpal(per_2022_usd), # Create a popup with information about the state. popup = ~ paste0( “地区：”, name, “
”, “

“,”人均gpd：“, per_2022_usd,”（千美元）“,”
” ), # Create a label with more detailed information. label = lapply(paste0( “地区：”, “”, map_sf002$name, "</b>", "<br/>", "人均gpd 美元：", round(map_sf002$per_2022_usd/1000), “K
”, “总gpd 美元：”, round(map_sf002$total_2022_usd), “M
” ), htmltools::HTML) ) %>% # Add a legend to the map. addLegend( position = “bottomright”, title = “人均gpd(美元)”, pal = pal, values = ~per_2022_usd, opacity = 1.0 )

6 Display the map.

m

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render-id="cXVhcnRvLWJyZWFkY3J1bWJzLVVTLVN0YXRlLUdEUC1NYXA="} :::{.hidden .quarto-markdown-envelope-contents render-id="Zm9vdGVyLXJpZ2h0"} This website is built with ❤️ and [Quarto](https://quarto.org/). ::: ::: :::{#quarto-meta-markdown .hidden} [US State GDP Map – R Handbook]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLW1ldGF0aXRsZQ=="} [US State GDP Map – R Handbook]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLXR3aXR0ZXJjYXJkdGl0bGU="} [US State GDP Map – R Handbook]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLW9nY2FyZHRpdGxl"} [R Handbook]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLW1ldGFzaXRlbmFtZQ=="} [with leaflet]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLXR3aXR0ZXJjYXJkZGVzYw=="} [with leaflet]{.hidden .quarto-markdown-envelope-contents render-id="cXVhcnRvLW9nY2FyZGRkZXNj"} :::  ::: {.quarto-embedded-source-code} ```````````````````{.markdown shortcodes="false"} --- title: "US State GDP Map" subtitle: "with leaflet" author: "Tony Duan" execute: warning: false error: false format: html: toc: true toc-location: right code-fold: show code-tools: true number-sections: true code-block-bg: true code-block-border-left: "#31BAE9" code-copy: true --- This document demonstrates how to create a choropleth map of the United States, where each state is colored based on its per capita GDP in 2022. We will use the `leaflet` package in R to create the interactive map. # Data Loading and Preparation First, we load the necessary R libraries for data manipulation, visualization, and working with geospatial data. ```{ r} # Load the tidyverse package for data manipulation and visualization. library(tidyverse) # Load the rvest package for web scraping. library(rvest) # Load the chromote package for controlling a headless Chrome browser. library(chromote) # Load the janitor package for data cleaning. library(janitor) # Load the tidygeocoder package for geocoding. library(tidygeocoder) # Load the leaflet package for creating interactive maps. library(leaflet) # Load the sf package for working with simple features. library(sf)

6.1 Loading Geospatial and GDP Data

We will load the US states geospatial data from a GeoJSON file and the GDP data from an Excel file.

{ r} # Load the US states map data from the maps package. library(maps) mapStates = map("state", fill = TRUE, plot = FALSE)

US location data: { r} # Read the US states GeoJSON file. json_data=read_sf("us-states.json")

{ r} # Convert the sf object to a data frame. map_df <- as.data.frame(json_data)

{ r} # Get a glimpse of the mapStates data. glimpse(mapStates)

US 2022 GDP data:

```{ r} # Load the openxlsx and readxl packages for reading Excel files. library(openxlsx) library(readxl)

7 Read the US state GDP data from an Excel file and clean up the state names.

per_gdp_usd=read_excel(‘US state gpd 2022.xlsx’) %>% mutate(names=str_replace(names,‘*’,’’) %>% str_trim() )

```{ r} # Get a glimpse of the GDP data. glimpse(per_gdp_usd)

7.1 Merging and Preparing the Data

We will merge the geospatial data with the GDP data.

{ r} # Merge the map data with the GDP data by state name. map_df002 <- merge(map_df, per_gdp_usd, by.x = "name", by.y = "names")

{ r} # Get a glimpse of the merged data. glimpse(map_df002)

{ r} # Convert the merged data frame back to an sf object. map_sf002 <- sf::st_as_sf(map_df002, sf_column_name = "geometry")

8 Creating the Choropleth Map

Now we can create the choropleth map of the US states.

```{ r} # Create a color palette for the map. pal <- colorNumeric( palette = “Blues”, domain = map_df002$per_gdp_usd )

9 Create a color bin palette for the map.

binpal <- colorBin(“Blues”,map_df002$per_gdp_usd, 20, pretty = FALSE)

10 Create the leaflet map.
m <- leaflet(map_sf002) %>% # Add default OpenStreetMap map tiles. addTiles() %>%
# Add polygons for the states. addPolygons(smoothFactor = 0.3, fillOpacity = 0.5,weight = 1, # Set the fill color based on the per capita GDP. color = ~binpal(per_2022_usd), # Create a popup with information about the state. popup = ~ paste0( “地区：”, name, “
”, “

“,”人均gpd：“, per_2022_usd,”（千美元）“,”
” ), # Create a label with more detailed information. label = lapply(paste0( “地区：”, “”, map_sf002$name, "</b>", "<br/>", "人均gpd 美元：", round(map_sf002$per_2022_usd/1000), “K
”, “总gpd 美元：”, round(map_sf002$total_2022_usd), “M
” ), htmltools::HTML) ) %>% # Add a legend to the map. addLegend( position = “bottomright”, title = “人均gpd(美元)”, pal = pal, values = ~per_2022_usd, opacity = 1.0 )

11 Display the map.

m

# Resources Here are some useful resources for learning more about the data and tools used in this document. https://www.gunviolencearchive.org/reports/mass-shooting?page=8&year=2023

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