choroplethr (version 1.3.0)

bind_df_to_map: Bind a data.frame to a map

Description

Given a data.frame which contains (region, value) pairs, bind it to either a state, county or ZIP code map. The result can then be rendered by calling render_choropleth. The value column is copied to a column named value.bak to facilitate user experimentation with various visualization strategies.

Usage

bind_df_to_map(df, lod)

Arguments

df

A data.frame with one column named "region" and one column named "value".

lod

A string representing the level of detail of the map you want. Must be one of "state", "county" or "zip".

Value

• A data.frame.

See Also

get_acs_df and render_choropleth.

Examples

library(Hmisc) # for cut2
# States with greater than 1M residents
df     = get_acs_df("B01003", "state") # population
df.map = bind_df_to_map(df, "state")
df.map$value = cut2(df.map$value, cuts=c(0,1000000,Inf))
render_choropleth(df.map, "state", "States with a population over 1M", "Population")

# Counties with greater than or greater than 1M residents
df     = get_acs_df("B01003", "county") # population
df.map = bind_df_to_map(df, "county")
df.map$value = cut2(df.map$value, cuts=c(0,1000000,Inf))
render_choropleth(df.map, "county", "Counties with a population over 1M", "Population")

# ZIP codes in California where median age is between 20 and 30
df       = get_acs_df("B01002", "zip") # median age
df       = df[df$value >= 20 & df$value <= 30, ]
df$value = cut2(df$value, g=3) # 3 equally-sized groups
df.map   = bind_df_to_map(df, "zip")
render_choropleth(df.map,
                 "zip",
                 title = "CA Zip Codes by Age",
                 scaleName = "Median Age",
                 states = "CA")