Free course: AriLamstein.com/free-course

Mapping Census Data in R

Ari LamsteinAriLamstein.comLamstein Consulting LLC

Getting Started❖ Choropleth Maps

❖ “choroplethr” package

❖ Any shapefile, any data

❖ States, Counties, ZIP Codes

❖ American Community Survey (ACS)

❖ Estimates

❖ acs package

❖ Exploratory Data Analysis

Package Basics

Geography Function Package Location Sample Data Zoom

State state_choropleth choroplethr, choroplethrMaps CRAN

df_pop_state,df_state_demographics state

County county_choroplethchoroplethr,

choroplethrMaps CRANdf_pop_county,

df_county_demographics state, county

ZIP (ZCTA) zip_choropleth choroplethrZip github df_pop_zip, df_zip_demographics

state, county, zip, msa

Mapmaking: Basics

library(choroplethr)library(choroplethrMaps)

data(df_pop_state)state_choropleth(df_pop_state, title = "2012 State Population Estimates", legend = "Population")

state_choropleth(df_pop_state, title = "2012 State Population Estimates", legend = "Population", num_colors = 1)

data(df_pop_county)

county_choropleth(df_pop_county, title = "2012 County Population Estimates", legend = "Population")

county_choropleth(df_pop_county, title = "2012 County Population Estimates", legend = "Population", num_colors = 1)

Demographics: Basics

data(df_county_demographics)

df_county_demographics$value = df_county_demographics$percent_whitecounty_choropleth(df_county_demographics,

title = "2013 County Percent White Estimates", legend = "Percent White")

df_county_demographics$value = df_county_demographics$percent_white

county_choropleth(df_county_demographics, title = "2013 County Percent White Estimates", legend = "Percent White", num_colors = 1)

df_county_demographics$value = df_county_demographics$percent_hispanic

county_choropleth(df_county_demographics, title = "2013 County Percent Hispanic Estimates",

legend = "Percent Hispanic”)

df_county_demographics$value = df_county_demographics$percent_hispanic

county_choropleth(df_county_demographics, title = "2013 County Percent Hispanic Estimates",

legend = "Percent Hispanic”, num_colors = 1)

df_county_demographics$value = df_county_demographics$percent_black

county_choropleth(df_county_demographics, title = "2013 County Percent Black Estimates", legend = "Percent Black")

df_county_demographics$value = df_county_demographics$percent_black

county_choropleth(df_county_demographics, title = "2013 County Percent Black Estimates", legend = "Percent Black”,

num_colors = 1)

df_county_demographics$value = df_county_demographics$per_capita_income

county_choropleth(df_county_demographics, title = "2013 County Income Estimates",

legend = “Per Capita Income")

df_county_demographics$value = df_county_demographics$per_capita_income

county_choropleth(df_county_demographics, title = "2013 County Income Estimates",

legend = “Per Capita Income”, num_colors = 1)

Customization

Zoomdf_county_demographics$value = df_county_demographics$median_rent

county_choropleth(df_county_demographics, title = "2013 California County Rent", legend = "Dollars", state_zoom = "california")

Reference Mapdf_county_demographics$value = df_county_demographics$median_rent

county_choropleth(df_county_demographics, title = "2013 California County Rent", legend = "Dollars", state_zoom = "california", reference_map = TRUE)

Case Study

How has North Dakota changed?

❖ Wrote Following Up on News Stories with Choroplethr and R

❖ Guest blog post in Revolution Analytics blog, 8/25/15

❖ Accessing historic data is easy

❖ Calculated percent change

Accessing Historic Data> df = get_state_demographics(endyear=2013, span=5)

> colnames(df)[1] "region" "total_population" [3] "percent_white" "percent_black" [5] "percent_asian" "percent_hispanic" [7] "per_capita_income" "median_rent" [9] "median_age"

Conclusion

❖ Census data is

❖ powerful and relevant

❖ easy to access and map with choroplethr

❖ Free course: arilamstein.com/free-course

❖ Contact: arilamstein.com/contact