###################################################### # tidycensus_Example0.r # Example #0 # Installing and Loading Relevant R Packages, # Checking the Variable Lists in Various Datasets # Prepared by Chuck Purvis, Hayward, California ###################################################### # 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) # 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) # 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) # 2010 Decennial Census, SF1 is available. dec10sf1_variable_list <- load_variables(year = 2010, dataset = "sf1", cache = TRUE) # There is no SF3 for the 2010 Decennial Census. # dec10sf3_variable_list <- load_variables(year = 2010, dataset = "sf3", cache = TRUE) # 2000 Census SF1 and SF3 apparently are pullable? dec00sf1_variable_list <- load_variables(year = 2000, dataset = "sf1", cache = TRUE) dec00sf3_variable_list <- load_variables(year = 2000, dataset = "sf3", cache = TRUE) # 1990 Census SF1 and SF3 apparently are pullable? dec90sf1_variable_list <- load_variables(year = 1990, dataset = "sf1", cache = TRUE) dec90sf3_variable_list <- load_variables(year = 1990, dataset = "sf3", 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) B09 <- filter(acs18_variable_list, str_detect(name, "B09")) View(B09) B06 <- filter(acs18_variable_list, str_detect(name, "B06")) View(B06) #---------------------------------------------------------------------------------------