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2560 discussions

icymi... LEHD LODES employment data for 2018

by Graham, Todd

icymi... Census Bureau announced on their twitter that they've released #LEHD Origin-Destination Employment Statistics (#LODES) Version 7.5, which adds data for 2018. Also backfills past years data that had been missing for federal workers, and jobs in South Dakota. The data is published here https://lehd.ces.census.gov/data/#lodes Cheers. [EMAILLOGO.png] Todd Graham Principal Forecaster | Metropolitan Council | Regional Policy and Research todd.graham(a)metc.state.mn.us<mailto:todd.graham@metc.state.mn.us> Office Phone 651.602.1322 390 North Robert Street | St. Paul, MN 55101 | metrocouncil.org/data<http://www.metrocouncil.org/data> [ConWUs][FaceBook_32x32]<http://www.facebook.com/MetropolitanCouncil>[Twitter_32x32]<http://twitter.com/#!/metcouncilnews>[linkedin]<http://www.linkedin.com/in/toddgraham>[Youtube_32x32]<http://www.youtube.com/user/MetropolitanCouncil> [EmailSU] <https://public.govdelivery.com/accounts/MNORGMETC/subscribers/new?preferenc…>

4 years

Question about 2000 TAZ Data in Massachusetts

by Cook, Cliff

We have been trying to use the CTPP data tool at: http://data5.ctpp.transportation.org/ctpp2000/Browse/browsetables.aspx to look for TAZ data for Middlesex County, Massachusetts from the 2000 CTPP. However, the tool seems to only include data for Berkshire County. When we look at TAZs on the map tab we can see TAZ geography for some other Massachusetts counties but we do not seem the boundaries for any of the counties in the Boston region, or much of central or western Massachusetts either for that matter. Does anyone have a suggestion for how we might gain access to data from tables covering these missing TAZs? Thanks. Cliff Cook Senior Planning Information Manager Cambridge Community Development Department 344 Broadway, Cambridge, MA 02139

4 years, 1 month

FW: Inviting Applications for ACS Data Users Group Steering Committee

by Weinberger, Penelope

Attention! From: American Community Survey Data Users Group <noreply(a)prb.org> Sent: Friday, November 13, 2020 9:08 AM To: Weinberger, Penelope <pweinberger(a)aashto.org> Subject: Inviting Applications for ACS Data Users Group Steering Committee [cid:ACS+DUS+logo+from+Test+Site-jpg_2D00_150x0-jpg@prb.org] Update from American Community Survey Data Users Group Inviting Applications for ACS Data Users Group Steering Committee In partnership with the U.S. Census Bureau, PRB is soliciting applications for membership in an ACS Data Users Group (DUG) Steering Committee. The DUG Steering Committee’s main responsibility will be to improve data users' understanding of the value and utility of ACS data. In addition, the DUG Steering Committee will inform PRB and Census Bureau staff about the concerns of ACS data users and will provide feedback on ACS-related activities, data products, dissemination channels, and/or methodology. To learn more and to apply, visit the ACS Online Community website: https://acsdatacommunity.prb.org/p/acs_steering_committee_announcement<https://linkprotect.cudasvc.com/url?a=https%3a%2f%2facsdatacommunity.prb.or…> The deadline for applications is Friday, December 11 at 12:00 PM (EST). You were sent this email because an administrator sent it to all users in the Everyone role on American Community Survey Data Users Group.

4 years, 1 month

FW: 2021 C2ER / LMI Forum: Call for Sessions, proposals due Nov. 6

by Graham, Todd

Forwarding this for C2ER. Their next annual conference (June) will be virtual. [cid:image002.png@01D6A5FE.5CE38860] C2ER 61st Annual Conference & LMI Institute Annual Forum 2021 Call for Sessions The Council for Community and Economic Research (C2ER) and the Labor Market Information (LMI) Institute are currently soliciting proposals for the next annual conference and forum, which will be held virtually June 7 - 11, 2021. The theme this year is The Great Pivot: Reinvention for Recovery and Resilience. We are interested to learn how your organization and community have and continue to respond to the impacts and implications of the COVID-19 pandemic, what that means for your local businesses, and how we can ready ourselves to better weather future events. Content and presentations from practitioners are a key component to the success of our conference. This enables our attendees to share, learn, converse, and interact with one another on topics of mutual interest and compare research methods, data collection practices, visualization tools, and lessons learned in the field. We invite members and non-members alike to submit session proposals. View full call for sessions details<https://crecstorage.blob.core.windows.net/c2er/2020/10/2021-Conference-Call…> Click here to start your proposal<https://www.surveymonkey.com/r/2021CallforProposals> Session proposals are due by 5:00 PM EST on Friday, November 6, 2020 Who is Our Audience? The C2ER Annual Conference/LMI Institute Annual Forum attracts researchers from state labor market information, economic development, and workforce development offices; community colleges; workforce boards; and other organizations in the economic and workforce development fields. The conference includes a variety of keynote and plenary speakers, and roundtables to offer world class networking and the opportunity to see community and economic development in action. What is the Structure and Format of the Conference? [cid:image003.jpg@01D6A5FE.5CE38860] Freepik.com Sessions will be a combination of pre-recorded and live-streaming content. We are seeking to create unique learning opportunities for our attendees and encourage you to submit new and innovative ways to use this time to share new knowledge and approaches, facilitate panel discussions, and/or engage attendees in active conversation. A virtual environment allows us to offer a variety of session formats, including traditional sessions (individual presentations; traditional panels of three 15-20-minute presentations; facilitated roundtable discussions), but we also welcome proposals that utilize the unique advantages of virtual meetings (such as on-site visits to activities or developments of interest in your community that you bring to us virtually). [cid:image004.jpg@01D6A5FE.5CE38860] Freepik.com This year we will continue to include skill building workshops. If you host a FREE data tool, website, or have a skillset that you would like to share with business and community researchers, we want to hear from you! Please note that the session is expected to offer an introduction to your data tool or website and a "hands-on" experience to allow participants to gain practical experience with the data, walk away with new skills, and possibly access a new tool for their work going forward. Workshops should be a minimum of 60 minutes long and a maximum of two hours long. [cid:image005.jpg@01D6A5FE.5CE38860] Freepik.com We are interested in a variety of viewpoints, we encourage researchers and analysts from different racial and ethnic backgrounds to share their experiences. If you are recruiting participants for a panel or developing a topic to share, please keep our mutual commitment in mind. We also want to hear from our younger researchers. This could be your chance to make a mark on the community and economic research profession for years to come! We're always looking for new leaders and a fresh set of contributors. [cid:image006.jpg@01D6A5FE.5CE38860] Freepik.com Become a sponsor city! Typically, we use the annual convening as an opportunity to learn more about our host city through local research keynotes, sponsors, off-site tours. With a virtual meeting, however, we have a chance to hear and see different communities across the U.S. and have various cities serve as "sponsors" by submitting speaker ideas, remote tours to showcase an area in development, or anything that provides local flavor. A key underlying theme is diversity, equity, and inclusion so we'd love to have different viewpoints and see how research is helping communities of color prosper. Proposals may address one or more of the following ideas as they relate to the theme of The Great Pivot: Reinvention for Recovery and Resilience. See the sub-topics below and related research questions and ideas. Please do not feel limited to the session focus areas listed below * Diversity, Inclusion, Equity * Response, Recovery, Resilience, Readiness, Reinvention * Future of Work * The Virtual Economy - Power of Remote Work * High Frequency Data - innovative real time data, unemployment insurance data, etc. * Technical Skills Workshops * Sponsor City (see details in Structure & Format here<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcrecstora…>) For more information on content, please contact Jennie Allison<mailto:jallison@crec.net>. Instructions and Guidelines Click here for the full list of proposal instructions and guidelines<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcrecstora…> C2ER/LMI Institute will provide a response to all submitted proposals no later than January 15, 2021. JOIN US ON SOCIAL MEDIA [Facebook]<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.faceb…>[Twitter]<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.c…>[Instagram]<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.insta…>[LinkedIn]<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.linke…> The Council for Community and Economic Research<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.c2er.…> (C2ER) is a membership organization working to improve data availability, enhance data quality, and foster learning about regional economic analytic methods. The Labor Market Information Institute<https://gcc02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.lmiont…> (LMI Institute) is the pre-eminent resource for supporting the development, interpretation, and use of labor market information. C2ER and the LMI Institute are managed by the Center for Regional Economic Competitiveness<https://gcc02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fcrec.net%2…> (CREC).

4 years, 2 months

Fwd: Webinar: Proposed Format Change to Simplify the ACS Summary File

by Ed Christopher

This may be of interest to some. -------- Forwarded Message -------- Subject: Webinar: Proposed Format Change to Simplify the ACS Summary File Date: 7 Oct 2020 20:23:40 +0000 From: American Community Survey Data Users Group <noreply(a)prb.org> To: [Population Reference Bureau] Update from American Community Survey Data Users Group <https://acsdatacommunity.prb.org/> *Webinar: Proposed Format Change to Simplify the ACS Summary File* **Join us for a webinar on October 28, 2020 from 2:00-3:00 PM EDT. Register now! <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fattendee.gotowebinar.co…> The Census Bureau's American Community Survey (ACS) Office is currently testing a new format for the ACS Summary File. The ACS Summary File is a comma-delimited text file that contains all the Detailed Tables for the ACS. We are inviting current ACS Summary File data users to review this new format and send in their feedback. Join this webinar to learn what is changing, see a live demonstration of the new format, and receive the necessary resources to review this format. This webinar will be recorded for those who are unable to participate in the live event. *Presenters:* *Caleb Hopler*, Survey Statistician, American Community Survey Office *Matthew Key*, IT Specialist, American Community Survey Office *Bonan Ren*, IT Specialist, American Community Survey Office After registering, you will receive a confirmation email containing information about joining the webinar. View System Requirements <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2flink.gotowebinar.com%2f…> You were sent this email because an administrator sent it to all users in the Everyone role on American Community Survey Data Users Group.

4 years, 2 months

RFP for Project Management/Technical Oversight for API/UI Development

by Benjamin Gruswitz

The CTPP Program is seeking a consultant to provide project management and technical oversight for a future contract to develop an API and web-based UI built upon the API. Please see the PDF posted at https://ctpp.transportation.org/whats-new/ Working from Home | 301.655.3170 Ben Gruswitz, AICP | Manager, Socioeconomic & Land Use Analytics (Pronouns: he/him) Delaware Valley Regional Planning Commission 190 N Independence Mall West, 8th Floor Philadelphia, PA 19106-1520 <https://www.google.com/maps/place/Delaware+Valley+Regional+Planning+Commiss…> 215.238.2882 | www.dvrpc.org Subscribe <https://app.e2ma.net/app2/audience/signup/1808352/1403728/> | Facebook <http://www.facebook.com/DVRPC> | Twitter <http://www.twitter.com/DVRPC> | Instagram <http://www.instagram.com/DVRPC> | LinkedIn <http://www.linkedin.com/company/delaware-valley-regional-planning-commission> | YouTube <https://www.youtube.com/channel/UCEU8UI5_iGkVypHP93b5jLA> [image: DVRPC] <https://www.dvrpc.org/>

4 years, 2 months

CTPP Seeks Proposals on Commuting in America Briefs

by Weinberger, Penelope

Good Morning, Continuing the series of briefs that constitute Commuting in America, the CTPP program today released three requests for proposals on the following topics: Understanding geographic and demographic variation in mode choice and travel behavior Explore methods and measurement of emerging modes Evolution of households The RFPs can be found at: ctpp.transportation.org/home/commuting-in-america-request-for-research-prop…<https://ctpp.transportation.org/home/commuting-in-america-request-for-resea…> And proposals are due October 23, 2020 Please share widely! Penelope Weinberger CTPP Program Manager AASHTO Ctpp.transportation.org

4 years, 2 months

Single Year 2019 ACS Data to be Released to Public on September 17, 2020

by Charles Purvis

(I’ve been having outgoing mail issues, as well…) Just a reminder to all that the single-year 2019 American Community Survey data was released to the public on Thursday, September 17, 2020. This is the fifteenth year of single-year ACS data (2005 to 2019). (Reminder that group quarters data collection didn’t start until 2006.) The data is actually under “embargo” (since 9/15/20), and only accessible to the media for analysis. But that’s just a short two day window of opportunity to get the scoop, so to speak. Here’s a nice summary article, on health insurance coverage, by the nonpartisan Center on Budget and Policy Priorities: https://www.cbpp.org/research/health/uninsured-rate-rose-again-in-2019-furt… <https://www.cbpp.org/research/health/uninsured-rate-rose-again-in-2019-furt…> The bad news is that the share of the population without health insurance is up to 9.2 percent in 2019, compared to a historic low of 8.6 percent in 2016 and a historic high of 15.5 percent in 2010. (Health insurance was first collected in the ACS in 2008.) And here’s the link to a 9/15/20 20-page report by the US Census Bureau on Health Insurance Coverage in the US based on data from the Current Population Survey (CPS) and the American Community Survey (ACS). https://www.census.gov/content/dam/Census/library/publications/2020/demo/p6… <https://www.census.gov/content/dam/Census/library/publications/2020/demo/p6…> https://www.census.gov/library/publications/2020/demo/p60-271.html <https://www.census.gov/library/publications/2020/demo/p60-271.html> Note that the 2019 CPS data was collected February through April 2020, just at the start of our ongoing pandemic. I’d recommend reading both the CBPP and Census Bureau reports!! If any of the national or local media has released any 2019 ACS results on transportation-related topics, that would be useful to post here! Stay safe! Chuck Purvis, Hayward, California formerly of the Metropolitan Transportation Commission (San Francisco, California).

4 years, 3 months

Finding Places within Counties for your State and Region using R

by Charles Purvis

Can I get a list of places within my multi-county region? Yes, but it takes a little work! Finding Places within Counties for Your State and Region. This is an “R” script which uses readily downloadable files from the decennial census to build a file of places within counties within states. This can then be subsetted to extract lists of places within a one-county or multi-county region. The following examples used 2010 Census data for the states of California, Texas, and New York, and can hopefully be easily adapted once the 2020 Census data becomes available spring 2021. The standard Census geography hierarchy diagram shows that “places” are below “states.” “Places” are NOT below “County” geographic levels. This is because there are some “places” in the United States that straddle two-or-more counties! This is an inconvenience if the analyst is interested in, say, the population characteristics of all places (cities, census designated places) within a one-county or multi-county region. A simple, creative way to find places-within-counties is to use GIS software to layer both county boundaries and place boundaries to select your places of interest. But this process doesn’t say anything about the population characteristics of places that potentially straddle two-or-more counties! But there are two Census “summary levels” that can be used to clearly identify places-within-counties: summary level 160 (state-place); and summary level 155 (state-place-county). The #160 summary level is the more commonly used of the two. The #155 summary level is pretty much a secret summary level only used by curious census data wonks. The two summary levels, combined, provide an accurate database of places-within-counties, including lists of places that straddle two-or-more counties. The simplest approach is to download the available PL 94-171 “Redistricting File” for the US state of interest. Here’s the link to the Census 2010 Redistricting Files: https://www2.census.gov/census_2010/redistricting_file--pl_94-171/ <https://www2.census.gov/census_2010/redistricting_file--pl_94-171/> What is important is the “geographic header” file that includes all geographic summary levels of interest, including summary levels 155 and 160. The following example extracts summary level 155 and 160 data for places in California, from Summary File #1 (SF1), though the PL 94-171 geographic header file is identical! The filename “cageo2010.sf1” means “California” + “Geographic” + “2010” from “SF1” This is a good example of building an “R” data frame from a fixed-format data file. ########################################################################### ## Extract the California Place Names and their Counties from the ## 2010 Decennial Census master geographic file, California, SF #1 ########################################################################### # install.packages("tidyverse") # install.packages("dplyr") library(dplyr) setwd("~/Desktop/tidycensus_work/output") setwd("~/Desktop/Census/2010_Census/ca2010.sf1") # setwd("~/Desktop/ca2010.sf1") x <- readLines(con="cageo2010.sf1") # Very large fixed format file, 843,287 observations for Calif CalGeo <- data.frame(# fileid = substr(x, 1, 6), # stusab = substr(x, 7, 8), sumlev = substr(x, 9, 11), # geocomp= substr(x, 12, 13), state = substr(x, 28, 29), county = substr(x, 30, 32), place = substr(x, 46, 50), # tract = substr(x, 55, 60), # blkgrp = substr(x, 61, 61), # block = substr(x, 62, 65), arealand=substr(x, 199, 212), areawatr=substr(x, 213, 226), name = substr(x, 227, 316), pop100 = substr(x, 319, 327), hu100 = substr(x, 328, 336)) CalGeo$GEOID <- paste(CalGeo$state,CalGeo$place,sep="") The following statements extract all summary level 155 and 160 records from the master geo-header file. The two files are then merge by the variables “state” and “place” and variables are renamed to something more recognizable. Note that if the variable names are the same in the merged data frames, “R” uses a convention of “variablename.x” for the first data frame and “variablename.y” for the second data frame used in the merge. sumlev155 <- subset(CalGeo, sumlev == 155) # state-place-county summary level sumlev160 <- subset(CalGeo, sumlev == 160) # state-place summary level coplace1 <- merge(sumlev155, sumlev160, by = c('state','place'), all=TRUE) coplace2 <- dplyr::rename(coplace1, county_name = name.x, # name is eg "Contra Costa County (part)" place_name = name.y, # name is eg "Acalanes Ridge CDP" county = county.x, GEOID = GEOID.x) The following statements are intended to identify places that straddle two-or-more counties in the state. In California (in 2010) we had four places that each straddle two counties, for a total of eight place-county records! These four place are Aromas (Monterey/San Benito Counties), Kingvale (Nevada/Place Counties), Kirkwood (Alpine, Amador Counties) and Tahoma (El Dorado/Place Counties). # Extra places that straddle two-or-more counties. # pop100.x = 2010 population count for, perhaps, part of the place (sumlev=155) # pop100.y = 2010 population count for the FULL place (sumlev=160) # This yields 4 places that straddle 2 counties, each, for 8 records in this file. splittown <- subset(coplace2, pop100.x < pop100.y) View(splittown) And lastly, I wanted to extract the places within the nine-county San Francisco Bay Area. This is probably the simplest script for this extraction. # Subset the Bay Area places from the SUMLEV=155/160 file BayArea <- subset(coplace2, county== "001" | county=="013" | county=="041" | county=="055" | county=="075" | county=="081" | county=="085" | county=="095" | county=="097" ) # c(1,13,41,55,75,81,85,95,97) Of course, write out the data frames to CSV files for further analysis. setwd("~/Desktop/tidycensus_work/output") write.csv(BayArea,"Census2010_BayArea_Places.csv") write.csv(coplace2,"Census2010_California_Places.csv") Let’s check Texas! Just to check this procedure, I downloaded the PL 94-171 data files for the State of Texas. The geo-header file for Texas was even larger than California (n=1,158,742 records in Texas, n=843,287 records in California!) Texas has 1,752 places (sumlev=160) and 1,934 place-county parts (sumlev=155). Upon inspection, there are places in Texas (Dallas!) that straddle five counties. That caught me by surprise! The population-based split procedure for California (subset(coplace2, pop100.x < pop100.y)) didn’t work for Texas since there are a few place-county parts in Texas with zero population. I found that “arealand” works just fine for Texas. The following code works for Texas: # Extra places that straddle two-or-more counties. # pop100.x = 2010 population count for, perhaps, part of the place (sumlev=155) # pop100.y = 2010 population count for the FULL place (sumlev=160) # Find the Texas places straddling two-or-more counties splittown <- subset(coplace2, pop100.x < pop100.y) View(splittown) # This works better for Texas, since there are a few place-county parts with zero population, # and 100 percent of population in the other place-county part. splittown2 <- subset(coplace2, arealand.x < arealand.y) View(splittown2) That’s as far as I’ve carried the Texas example, since I’m not on the lookout for a master national list of places split by county boundaries! Maybe this is something that the Census Bureau Geography Division has ready access to? Let’s check New York! This process also works for the State of New York. It’s still best to use the “AREALAND” differences, sumlev=155 versus sumlev=160, to find places that straddle two-or-more-counties (or boroughs in the case of New York City). Yes, the City of New York straddles/encompasses five boroughs/counties. And there are 13 other places in New York State that straddle two counties: Almond, Attica, Brewerton, Bridgeport, Deposit, Dodgeville, Earlville, Geneva, Gowanda, Keeseville, Peach Lake, Rushville, and Saranac Lake. These are “R” scripts that don’t use “tidycensus” but are clean methods for answering such a simple question as “what are the census places within my multi-county region?”

4 years, 3 months

Beyond AmericanFactFinder - TidyCensus Example Set 2

by Charles Purvis

I’ve been having troubles sending my example set #2 for my introduction to tidycensus. Hopefully this time through it’ll get through. Chuck Purvis, Hayward, California Example #2. More Complex Tidycensus examples: multiple years, multiple geographies, multiple variables. This is a more complex example of a script to “pull” select variables from the ACS using my Census API key and the R package tidycensus. Step #0. Always need to load the relevant packages/libraries when starting up a new R-session. Otherwise, it won’t work. # Step 0: Load relevant libraries into each R-session. library(tidyverse) library(tidycensus) In this set of examples, I’m extracting single year ACS variables (2005-2018) for all large (65,000+ population) places in the State of California. # The get_acs function is run for each year of the single-year ACS data, from 2005 to 2018. # Note that group quarters data was not collected in 2005, but started in 2006. # Note the "_05_" included in the variable name in the first data "pull". That's a # # mnemonic device that tells us it's for the year 2005. # Example 2.1 through 2.14: Run get_acs for large California Places, 2005-2018 # Example 2.15: Merge together data frames into a VERY wide database...lots of columns! # Example 2.16: Merge in a file of Large San Francisco Bay Area places, and subset file. #------------------------------------------------------------------------------- place05 <- get_acs(survey="acs1", year=2005, geography = "place", state = "CA", show_call = TRUE, output="wide", variables = c(TotalPop_05_ = "B06001_001", # Total Population Med_HHInc_05_ = "B19013_001", # Median Household Income Agg_HHInc_05_ = "B19025_001", # Aggregate Household Income HHldPop_05_ = "B11002_001", # Population in Households Househlds_05_ = "B25003_001", # Total Households Owner_OccDU_05_= "B25003_002", # Owner-Occupied Dwelling Units Rent_OccDU_05_ = "B25003_003", # Renter-Occupied Dwelling Units Med_HHVal_05_ = "B25077_001")) # Median Value of Owner-Occ DUs place05$Avg_HHSize_05 <- place05$HHldPop_05_E / place05$Househlds_05_E place05$MeanHHInc_05 <- place05$Agg_HHInc_05_E / place05$Househlds_05_E #------------------------------------------------------------------------------------ place06 <- get_acs(survey="acs1", year=2006, geography = "place", state = "CA", show_call = TRUE, output="wide", variables = c(TotalPop_06_ = "B06001_001", # Total Population Med_HHInc_06_ = "B19013_001", # Median Household Income Agg_HHInc_06_ = "B19025_001", # Aggregate Household Income HHldPop_06_ = "B11002_001", # Population in Households Househlds_06_ = "B25003_001", # Total Households Owner_OccDU_06_= "B25003_002", # Owner-Occupied Dwelling Units Rent_OccDU_06_ = "B25003_003", # Renter-Occupied Dwelling Units Med_HHVal_06_ = "B25077_001")) # Median Value of Owner-Occ DUs place06$Avg_HHSize_06 <- place06$HHldPop_06_E / place06$Househlds_06_E place06$MeanHHInc_06 <- place06$Agg_HHInc_06_E / place06$Househlds_06_E #------------------------------------------------------------------------------------ These sets of codes are repeated for each single year ACS of interest, say for 2005 through 2018. Smarter “R” programmers will be able to tell me about “do loops” to make this process more efficient with magical wild cards. The following step merges the data frames using the GEOID/NAME variables. This create a very “wide” database. One record per geography, and each column representing the variable/year combinations. The “merge” function in “R” allows only two data frames to be joined by common columns at a time. I have yet to find a “R” function that allows me to merge all of the data frames at once. ##################################################################################### # Example 2.15: Merge together data frames into a VERY wide database...lots of columns! # Merge the dataframes, adding a year in each step. All=TRUE is needed if # of places is different. # # (R-language newbie script...There are probably more terse/exotic ways of doing this!) place0506 <- merge(place05, place06, by = c('GEOID','NAME'), all=TRUE) place0507 <- merge(place0506,place07, by = c('GEOID','NAME'), all=TRUE) place0508 <- merge(place0507,place08, by = c('GEOID','NAME'), all=TRUE) place0509 <- merge(place0508,place09, by = c('GEOID','NAME'), all=TRUE) place0510 <- merge(place0509,place10, by = c('GEOID','NAME'), all=TRUE) place0511 <- merge(place0510,place11, by = c('GEOID','NAME'), all=TRUE) place0512 <- merge(place0511,place12, by = c('GEOID','NAME'), all=TRUE) place0513 <- merge(place0512,place13, by = c('GEOID','NAME'), all=TRUE) place0514 <- merge(place0513,place14, by = c('GEOID','NAME'), all=TRUE) place0515 <- merge(place0514,place15, by = c('GEOID','NAME'), all=TRUE) place0516 <- merge(place0515,place16, by = c('GEOID','NAME'), all=TRUE) place0517 <- merge(place0516,place17, by = c('GEOID','NAME'), all=TRUE) place0518 <- merge(place0517,place18, by = c('GEOID','NAME'), all=TRUE) place_all <- place0518 View(place_all) Sometimes you want to create smaller data frames with just a select number of columns. Here’s a good approach for that. # The following functions output useful lists to the R-studio console which can then be edited names(place_all) dput(names(place_all)) # most useful for subsetting variables # The purpose here is to re-order and select variables into a much more compact # database, for eventual exporting into a CSV file, and then into Excel for finishing touches. selvars <- c("GEOID", "NAME", "TotalPop_05_E", "TotalPop_06_E", "TotalPop_07_E", "TotalPop_08_E", "TotalPop_09_E", "TotalPop_10_E", "TotalPop_11_E", "TotalPop_12_E", "TotalPop_13_E", "TotalPop_14_E", "TotalPop_15_E", "TotalPop_16_E", "TotalPop_17_E", "TotalPop_18_E") # note the brackets for outputing new data frame from previous data frame.... place_all2 <- place_all[selvars] # View the Selected Variables Table View(place_all2) # Set directory for exported data files, MacOS directory style setwd("~/Desktop/tidycensus_work/output") # Export the data frames to CSV files, for importing to Excel, and applying finishing touches write.csv(place_all2,"ACS_AllYears_TotalPop_Calif_Places.csv") write.csv(place_all, "ACS_AllYears_BaseVar_Calif_Places.csv") In this last example, I’m reading in a file of large places in the Bay Area (manually derived from the CSV file created previous) in order to subset Bay Area “large places” from State of California “large places”. ##################################################################################### # Example 2.16: Merge in a file of Large San Francisco Bay Area places, and subset file. # Read in a file with the Large SF Bay Area Places, > 65,000 population # and merge with the All Large California Places bayplace <- read.csv("BayArea_Places_65K.csv") Bayplace1 <- merge(bayplace,place_all, by = c('NAME')) Bayplace2 <- merge(bayplace,place_all2, by = c('NAME')) write.csv(Bayplace1,"ACS_AllYears_BaseVar_BayArea_Places.csv") write.csv(Bayplace2,"ACS_AllYears_TotalPop_BayArea_Places.csv") This concludes Example #2: “multiple geographies / multiple years/ multiple variables” with only one record (row) per each geography.

4 years, 3 months

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