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February 21, 2022 20:07
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American Community Survey, Multiple Years, Example using purrr and tidycensus, journey-to-work
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| ######################################################################## | |
| # ACS_AllYears_Journey2Work_purrr.r | |
| # Use the purrr function map_dfr to iterate tidycensus call for 1-year, 5-year ACS data. | |
| # Data on Means of Transportation to Work Variables, All ACS years | |
| # Nation and US States and counties and places (added 1/28/22) | |
| # | |
| # American Community Survey, 1-year, 2005-2019 data | |
| # ACS 5-year data: 2005-2009, 2010-2014, 2015-2019 | |
| # ACS 5-year data: 2006-2010, 2010-2014, 2015-2019 (this works for B08006....) | |
| ## ACS 5-year data: 2006-2010, 2011-2015, 2016-2020 @@ should work after 3/17/22. | |
| # | |
| # Data is meant to be "stacked" with one row/record for each geography/year combination | |
| # -- January 18, 2022 -- | |
| ## -- January 28, 2022 -- (Add County and Place!!!) | |
| ## -- February 7, 2022 -- "skip-a-year" is working. And new sets of | |
| ## nonoverlapping 5-yrs when 2016-2020 is released 3/17/22 | |
| ######################################################################## | |
| # These R packages were installed in previous sessions, so no need to re-install | |
| # install.packages("tidyverse") | |
| # install.packages("tidycensus") | |
| # install.packages("janitor") | |
| # install.packages("purrr") | |
| # Load relevant libraries into R-session. | |
| library(purrr) | |
| library(tidyverse) | |
| library(tidycensus) | |
| library(janitor) | |
| library(plyr) | |
| library(dplyr) | |
| library(xlsx) | |
| # Census API Key was installed in previous sessions, so no need to re-install | |
| # census_api_key("fortycharacterkeysentbyCensusBureau",install=TRUE) | |
| Sys.getenv("CENSUS_API_KEY") | |
| # Set a list of variables to extract in each iteration of get_acs | |
| # Total Population is problematic for 2005-09 since GQ pop wasn't collected in 2005.... | |
| # selvars2 was a previous "best of" variables for introductory analyses.... | |
| selvars2 <- c(# TotalPop_ = "B06001_001", # Total Population | |
| Med_HHInc_ = "B19013_001", # Median Household Income | |
| Agg_HHInc_ = "B19025_001", # Aggregate Household Income | |
| HHldPop_ = "B11002_001", # Population in Households | |
| Househlds_ = "B25003_001", # Total Households | |
| Owner_OccDU_= "B25003_002", # Owner-Occupied Dwelling Units | |
| Rent_OccDU_ = "B25003_003", # Renter-Occupied Dwelling Units | |
| Med_HHVal_ = "B25077_001") | |
| selvars <- c(total_pop_ = "B06001_001", # Total Population | |
| hh_pop_ = "B11002_001", # Population in Households | |
| households_ = "B25003_001", # Total Households | |
| agg_commtime_ = "B08013_001", # Aggregate Travel Time to Work | |
| agg_vehicle_ = "B08015_001", # Aggregate Vehicles Used in Journey-to-Work | |
| total_worker_ = "B08006_001", | |
| drove_alone_ = "B08006_003", | |
| carpool_2_ = "B08006_005", | |
| carpool_3_ = "B08006_006", | |
| carpool_4p_ = "B08006_007", | |
| transit_ = "B08006_008", | |
| bicycle_ = "B08006_014", | |
| walked_ = "B08006_015", | |
| other3_ = "B08006_016", # taxicab, motorcycle, other means | |
| at_home_ = "B08006_017", | |
| bus_ = "B08006_009", | |
| subway_ = "B08006_010", | |
| railroad_ = "B08006_011", | |
| lightrail_ = "B08006_012", | |
| ferry_ = "B08006_013") | |
| ################################################################### | |
| # Iterate on ACS 1-year data, using the purrr function "map_dfr" | |
| ################################################################### | |
| test1 <- get_acs(survey="acs1",geography="place",state="CA",variables=selvars, | |
| year=2019,output='wide') | |
| # This doesn't work for 2005 since the ACS 2005 B08006 table | |
| # had extra records for Carpools: carpool 4, carpool 5-6, and carpool 7+ | |
| # years <- 2005:2019 | |
| years <- 2006:2019 | |
| # Testing skip-a-year to see what how we'll handle 2020-X data..... | |
| years <- c(2009:2017,2019) | |
| names(years) <- years | |
| # United States, 2006-2019, single year ACS | |
| zeta1 <- map_dfr(years, ~{ get_acs(survey = "acs1", | |
| geography = "us", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta2 <- zeta1[order(zeta1$GEOID,zeta1$year),] | |
| # All US States, 2006-2019, single year ACS | |
| zeta3 <- map_dfr(years, ~{ get_acs(survey = "acs1", | |
| geography = "state", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta4 <- zeta3[order(zeta3$GEOID,zeta3$year),] | |
| # Large US Counties, 2006-2019, single year ACS (Population 65K+) | |
| zeta5 <- map_dfr(years, ~{ get_acs(survey = "acs1", | |
| geography = "county", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta6 <- zeta5[order(zeta5$GEOID,zeta5$year),] | |
| # Large US Places, 2006-2019, single year ACS (Population 65K+) | |
| zeta7 <- map_dfr(years, ~{ get_acs(survey = "acs1", | |
| geography = "place", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta8 <- zeta7[order(zeta7$GEOID,zeta7$year),] | |
| ######################################################################## | |
| ## Output the ACS single year databases.... | |
| ######################################################################## | |
| # note that my working directory (wd) is for a Mac computer. | |
| setwd("~/Desktop/tidycensus_work/output") | |
| # Export the data frames to CSV files, for importing to Excel | |
| write.csv(zeta2,"ACS_AllYears_US_meansoftransportation_2006_2019.csv") | |
| write.csv(zeta4,"ACS_AllYears_states_meansoftransportation_2006_2019.csv") | |
| write.csv(zeta6,"ACS_AllYears_counties_meansoftransportation_2006_2019.csv") | |
| write.csv(zeta8,"ACS_AllYears_places_meansoftransportation_2006_2019.csv") | |
| # just write them in a master XLSX workbook..... | |
| # Grrrr... this doesn't work... java heap space error, whatever that is..... | |
| write.xlsx2(zeta2, "ACS_AllYears_journey_to_work_master_2006_2019.xlsx", | |
| sheetName="nation", append=FALSE) | |
| write.xlsx2(zeta4, "ACS_AllYears_journey_to_work_master_2006_2019.xlsx", | |
| sheetName="states", append=TRUE) | |
| write.xlsx2(zeta6, "ACS_AllYears_journey_to_work_master_2006_2019.xlsx", | |
| sheetName="counties", append=TRUE) | |
| write.xlsx2(zeta8, "ACS_AllYears_journey_to_work_master_2006_2019.xlsx", | |
| sheetName="places", append=TRUE) | |
| #################################################################################### | |
| ################################################################### | |
| # Iterate on ACS 5-year data, using the purrr function "map_dfr" | |
| ################################################################### | |
| # this doesn't work.... | |
| test5 <- get_acs(survey="acs5",year=2009, | |
| geography="us", variables="B08006001", output="wide") | |
| # with years 2010, 2014 and 2019, this is a one-year overlap between | |
| # the first two periods.... 2009 --- bombed for some reason..... | |
| # The new 2016-2020 ACS 5-year data is scheduled for release 3/17/22..... | |
| # Data for 2006-2010, 2011-2015, 2016-2020: | |
| # years5 <- c(2010,2015,2020) | |
| years5 <- c(2010,2014,2019) | |
| names(years5) <- years5 | |
| # This code is choking on selvars (means of transportation to work) | |
| # getting an "unknown variable 'B08006_001E'" error??? :( | |
| # re-ran... it works on 2006-2010; 2010-2014; 2015-2019..... weird!! | |
| # National, ACS 5-years (2006-2010, 2010-2014, 2015-2019) | |
| zeta9 <- map_dfr(years5, ~{ get_acs(survey = "acs5", | |
| geography = "us", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta10 <- zeta9[order(zeta9$GEOID,zeta9$year),] | |
| # State, ACS 5-years (2006-2010, 2010-2014, 2015-2019) | |
| zeta11 <- map_dfr(years5, ~{ get_acs(survey = "acs5", | |
| geography = "state", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta12 <- zeta11[order(zeta11$GEOID,zeta11$year),] | |
| # County, ACS 5-years (2006-2010, 2010-2014, 2015-2019) | |
| zeta13 <- map_dfr(years5, ~{ get_acs(survey = "acs5", | |
| geography = "county", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta14 <- zeta13[order(zeta13$GEOID,zeta13$year),] | |
| # Place, ACS 5-years (2006-2010, 2010-2014, 2015-2019) | |
| zeta15 <- map_dfr(years5, ~{ get_acs(survey = "acs5", | |
| geography = "place", # state = "CA", | |
| variables = selvars, output='wide', | |
| year = .x)}, .id = "year") | |
| # Sort the Results by GEOID and then by year | |
| zeta16 <- zeta15[order(zeta15$GEOID,zeta15$year),] | |
| # This last dataframe, zeta16, has 88,636 records... pretty big! | |
| ######################################################################## | |
| ## Output the ACS five year databases.... | |
| ######################################################################## | |
| setwd("~/Desktop/tidycensus_work/output") | |
| # Export the data frames to CSV files, for importing to Excel | |
| write.csv(zeta10,"ACS_5Years_US_meansoftransportation_2006_2014_2019.csv") | |
| write.csv(zeta12,"ACS_5Years_states_meansoftransportation_2006_2014_2019.csv") | |
| write.csv(zeta14,"ACS_5Years_counties_meansoftransportation_2006_2014_2019.csv") | |
| write.csv(zeta16,"ACS_5Years_places_meansoftransportation_2006_2014_2019.csv") | |
| ######################################################################## |
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