Here’s my first followup to my 7/16/2020 post on using tidycensus in a post-American Factfinder era. Attached to this e-mail is a short text file (“r” suffix) that can be edited for your use. Example #0. Setting up tidycensus. This is an introduction to the use of the R-package tidycensus in extracting data from the US Census Bureau’s American Community Survey. I’m adding snippets of R code from my R-scripts, and attaching the full r-script to this message. First things first: Acquaint yourself with the American Community Survey. What I would strongly recommend is to download and print out copies of the various ACS survey questionnaires. Know what was asked! Decennial Census questionnaires: https://www.census.gov/history/www/through_the_decades/questionnaires/ <https://www.census.gov/history/www/through_the_decades/questionnaires/> American Community Survey questionnaires: https://www.census.gov/programs-surveys/acs/methodology/questionnaire-archi… <https://www.census.gov/programs-surveys/acs/methodology/questionnaire-archive.html> Next, I would recommend downloading the “table shells” from the Census Bureau’s website, and not rely on just on the tidycensus “load_variables” function. Get the table shells for all of the years: the ACS does change ever so often, and so do the tables! I find it useful to have part of my computer screen opened with the table shells visible in Excel. ACS Table Shells: https://www.census.gov/programs-surveys/acs/technical-documentation/table-s… <https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html> I find it useful to have on hand a guide to the ACS table numbering scheme, so you know your “B” and “C” and “S” and “GCT” tables and the two-digit subject indicator (“08” – Journey-to-Work”). https://censusreporter.org/topics/table-codes/ <https://censusreporter.org/topics/table-codes/> Download and install the free software package R Studio. There are other YouTube videos you can watch about learning/installing R and R Studio, and I won’t cover those here. https://rstudio.com/products/rstudio/download/#download <https://rstudio.com/products/rstudio/download/#download> Launch R Studio. There are a few add-on packages that first need to be installed onto your computer, and then “loaded” into your working R session. # Step 1 Install R packages. If installed in previous sessions, there is no need to re-install. # You may need to install the packages "tidyr" and "sp" for "tidycensus" to be properly installed. install.packages("tidyverse") install.packages("tidycensus") install.packages("janitor") # Step 2: Load relevant libraries into each R-session. library(tidyverse) library(tidycensus) library(janitor) Acquire a Census API key from the Census Bureau. It’s free. It’s a 40 character string that identifies a unique API user and helps the Census Bureau improve their tools to access census data. They’ll e-mail you a key in no time at all. https://www.census.gov/data/developers/updates/new-discovery-tool.html <https://www.census.gov/data/developers/updates/new-discovery-tool.html> https://api.census.gov/data/key_signup.html <https://api.census.gov/data/key_signup.html> Install your 40-character API key into your R “environment.” Just one time and no need to concern yourself ever again about this key. # Step 3: Load the User's Census API Key. # Census API Key was installed in previous sessions, so no need to re-install # un-comment out the following statement with the user's API key. # census_api_key("fortycharacterkeysentbyCensusBureau",install=TRUE) The last section of this introduction relates to using the “load_variables” as a tool to assist in selecting various variables. I prefer to download the ACS Table Shells into Excel, and then have appropriate Table Shells opened, alongside R Studio, to aid me in variable selection and naming. # Step 4: Explore the Data Variables using the load_variables() function # Use the function load_variables() to view all of the possible variables for analysis # load_variables works for both decennial census and American Community Survey databases acs18_variable_list <- load_variables(year = 2018, dataset = "acs5", cache = TRUE) acs18p_variable_list <- load_variables(year = 2018, dataset = "acs5/profile", cache = TRUE) # Maybe write out the data frame to the desktop, for easier in use in Excel? write.csv(acs18_variable_list,'acs18_variable_list.csv', row.names=FALSE) View(acs18_variable_list) As of this summer 2020, tidycensus can be used to extract the “base” and “collapsed” tables for all years of the ACS, from 2005 through 2018 “single year” databases; the five-year ACS databases starting with 2005/09 through to 2014/18; and the decennial census files for 2010, 2000 and 1990. For the decennial censuses, databases include the SF1 (Summary File #1) for 1990, 2000 and 2010; and the SF3 (Summary File #3) for 1990 and 2000. (There was no long form census in the 2010 Census, so, thus no long-form-based SF3 data for 2010!) (I have yet to explore how to pull data from the decennial censuses using tidycensus, and would be grateful to hear news of successes/failures.) A word of warning: R is very case sensitive. Something like View(acs18_variable_list) will work okay, but view(acs18_variable_list) will not work!! That’s the end of Step #0… Setting up Tidycensus! Chuck Purvis, Hayward, California Retired Person (formerly of the Metropolitan Transportation Commission, San Francisco, California)\