This is the last tidycensus example that I���ve prepared. Hope this proves useful!

Stay safe!

Chuck Purvis,

Hayward, California

formerly of the Metropolitan Transportation Commission, San Francisco California.

Example #4. Explore the Geographies that Tidycensus can pull out.

 

This is an example of using tidycensus to extract 2014/18 ACS data for all available geographies for the entire USA. It���s just a test of capabilities.

 Some geographic levels (county subdivision, state upper and lower houses) don���t appear to be working.

 There is some extra code in the ���PUMA step��� to tally the number of PUMAs per US state, and the minimum and maximum total population levels for each PUMA in the US.

 

# Step 0: Load relevant libraries into each R-session.

 

library(tidyverse)

library(tidycensus)

library(janitor)

library(plyr) # This is needed for a function to concatenate a lot of files in one statement!

library(dplyr)

 

# Add the variable Geography_Name to each data frame. Maybe concatenate/pancake these dataframes?

 

selvars  <- c(TotalPop_   = "B06001_001", # Total Population

              SamplePop_  = "B00001_001",  # Unweighted Sample Count of Population

              HHUnits_    = "B25002_001", # Total Housing Units

              Househlds_  = "B25002_002", # Total Households

              SampleDU_   = "B00002_001") # Unweighted Sample Count of Dwelling Units

#------------------------------------------------------------------------------------

us  <- get_acs(survey="acs5", year=2018, geography = "us",

               show_call = TRUE,output="wide", variables = selvars)

us$Geography_Name <- "us"

#------------------------------------------------------------------------------------

region  <- get_acs(survey="acs5", year=2018, geography = "region",

                   show_call = TRUE,output="wide", variables = selvars)

region$Geography_Name <- "region"

#------------------------------------------------------------------------------------

division  <- get_acs(survey="acs5", year=2018, geography = "division",

                     show_call = TRUE,output="wide", variables = selvars)

division$Geography_Name <- "division"

#------------------------------------------------------------------------------------

state  <- get_acs(survey="acs5", year=2018, geography = "state",

                  show_call = TRUE,output="wide", variables = selvars)

state$Geography_Name <- "state"

 

setwd("~/Desktop/tidycensus_work/output")

 

write.csv(state,"ACS1418_USA_State_1.csv")

#------------------------------------------------------------------------------------

county  <- get_acs(survey="acs5", year=2018, geography = "county",

                   show_call = TRUE,output="wide", variables = selvars)

county$Geography_Name <- "county"

#------------------------------------------------------------------------------------

# County Subdivision isn't working ...returns an API error (unknown/unsupported geography)

countysubdiv  <- get_acs(survey="acs5", year=2018, geography = "county subdivision",

                         show_call = TRUE,output="wide", variables = selvars)

countysubdiv$Geography_Name <- "countysubdiv"

#------------------------------------------------------------------------------------

# Pull just the tracts in Alameda County, California

tract  <- get_acs(survey="acs5", year=2018, geography = "tract",  state="CA", 

            county="Alameda",show_call = TRUE,output="wide", variables = selvars)

tract$Geography_Name <- "tract"

#------------------------------------------------------------------------------------

# Pull just the block groups in Alameda County, California

blockgroup<- get_acs(survey="acs5", year=2018, geography = "block group", state="CA",       county="Alameda",show_call = TRUE,output="wide", variables = selvars)

blockgroup$Geography_Name <- "blockgroup"

#------------------------------------------------------------------------------------

place  <- get_acs(survey="acs5", year=2018, geography = "place",

                  show_call = TRUE,output="wide", variables = selvars)

place$Geography_Name <- "place"

#------------------------------------------------------------------------------------

urban  <- get_acs(survey="acs5", year=2018, geography = "urban area",

                  show_call = TRUE,output="wide", variables = selvars)

urban$Geography_Name <- "urban"

#------------------------------------------------------------------------------------

congdist  <- get_acs(survey="acs5", year=2018, geography = "congressional district",

                  show_call = TRUE,output="wide", variables = selvars)

congdist$Geography_Name <- "congdist"

#------------------------------------------------------------------------------------

puma  <- get_acs(survey="acs5", year=2018, geography = "public use microdata area",

                  show_call = TRUE,output="wide", variables = selvars)

 

puma$TotalPop2      <- puma$TotalPop_E * 1.0

puma$Tally          <- 1.0

puma$State          <- substr(puma$GEOID,1,2)

puma$Geography_Name <- "puma"

pumas <- puma[order(puma$State,puma$GEOID),]

summary(pumas)

 

sum1 <- aggregate(pumas[,3:12],

          by = list(pumas$State),

          FUN = sum, na.rm=TRUE)

 

min1 <- aggregate(pumas[,3:12],

                  by = list(pumas$State),

                  FUN = min, na.rm=TRUE)

 

max1 <- aggregate(pumas[,3:12],

                  by = list(pumas$State),

                  FUN = max, na.rm=TRUE)

 

setwd("~/Desktop/tidycensus_work/output")

 

write.csv(sum1,"ACS1418_USA_PUMA_sum_by_State_1.csv")

write.csv(min1,"ACS1418_USA_PUMA_min_by_State_1.csv")

write.csv(max1,"ACS1418_USA_PUMA_max_by_State_1.csv")

 

write.csv(pumas,"ACS1418_USA_PUMA_All_1.csv")

 

sum2 <- pumas %>%

  group_by(State) %>%

  summarize_at("TotalPop_E",

               list(name=sum))

#------------------------------------------------------------------------------------

csa  <- get_acs(survey="acs5", year=2018, geography = "combined statistical area",

                 show_call = TRUE,output="wide", variables = selvars)

csa$Geography_Name <- "csa"

#------------------------------------------------------------------------------------

msamisa  <- get_acs(survey="acs5", year=2018, geography = "metropolitan statistical area/micropolitan statistical area",

                 show_call = TRUE,output="wide", variables = selvars)

msamisa$Geography_Name <- "msamisa"

#------------------------------------------------------------------------------------

zcta  <- get_acs(survey="acs5", year=2018, geography = "zcta",

                 show_call = TRUE,output="wide", variables = selvars)

zcta$Geography_Name <- "zcta"

#------------------------------------------------------------------------------------

# State Senate and House aren't working ...returns an API error (unknown/unsupported geography)

statesenate  <- get_acs(survey="acs5", year=2018, geography = "state legislative district (upper chamber)",

                show_call = TRUE,output="wide", variables = selvars)

statesenate$Geography_Name <- "statesenate"

#------------------------------------------------------------------------------------

statehouse   <- get_acs(survey="acs5", year=2018, geography = "state legislative district (lower chamber)",

                show_call = TRUE,output="wide", variables = selvars)

statehouse$Geography_Name <- "statehouse"

#------------------------------------------------------------------------------------

 

This concludes Example #4: ���exploring tidycensus geography.���