Download summary files from Census Bureau and extract data of decennial censuses and American Community Surveys from your local computer.
2/7/2019: Version 0.6.1 is on CRAN.
read_acs5year()run faster in the new version.
totalcensus v0.5.1 is on CRAN. It includes
# from CRAN install.packages("totalcensus") # development version devtools::install_github("GL-Li/totalcensus")
This package requires downloading census data and you need to create a folder to store the downloaded data. Let's call the folder
my_census_data and assume the full path to this folder is
xxxxx/my_census_data. Run the function below to set the path for the package.
The census API offers most data in decennial censuses and ACS estimates for download and API-based packages such as
acs make the downloading very convenient in R. So why we need another package?
One advantage is that once you downloaded the summary files, you do not need internet anymore and everything is on your own computer. You do not need to worry about internet interruption or government shutdown. You have total control of the data.
Another benefit of using package
totalcensus is that it makes census data extraction more flexible. It is particularly convenient to extract high resolution data at census tract, block group, and block level for a large area.
Here is an example of how we extract the median home values in all block groups in the United States from 2011-2015 ACS 5-year survey with this package. You simply need to call the function
read_acs5year(). It takes 15 seconds for my 4-years old laptop to return the data of all 217,739 block groups. In addition to the table contents we request, we also get the population and coordinate of each block group.
library(totalcensus) home_national <- read_acs5year( year = 2015, states = states_DC, # all 50 states plus DC table_contents = "home_value = B25077_001", summary_level = "block group" )
With the coordinates, we can visualize the data on US map with
ggmap. Each data point in the figure below corresponds to a a block group, colored by median home value and sized by population. This plot not only displays the median home values, but also tells population densities on the map.
There are additional benefits of using this package:
The package has three functions to read decennial census, ACS 5-year survey, and ACS 1-year survey:
read_acs1year(). They are similar but as these datasets are so different, we prefer to keep three separate functions, one for each.
The function arguments serve as filters to select the data you want:
c("male = B01001_002", "female = B01001_026").
c("New York metro", "PLACE = UT62360", "Salt Lake City city, UT").
read_acs5year() have additional argument:
There are a family of
search_xxx() functions to help find table contents, geoheaders, summary levels, geocomponents, FIPS codes and CBSA codes.
The following examples demonstrate how to use these
A property management company wants to know the most recent rents in major cities in the US. How to get the data?
We first need to determine which survey to read. For most recent survey data, we want to read 2016 ACS 1-year estimates, which provide data for geographic areas with population over 65000.
We also need to determine which data files to read. We know summary level of cities is "160" or "place". Browsing with
search_summarylevels("acs1"), we see that this summary level is only in state files of ACS 1-year estimates. So we will read all the state files.
Then we need to check if 2016 ACS 1-year estimate has the rent data. We run
search_tablecontents("acs1") to open the dataset with
View() in RStudio. You can provide keywords to search in the function but it is better to do the search in RStudio with filters. There are so many tables that contains string "rent". It takes some time to find the right one if you are not familiar with ACS tables. After some struggle, we think B25064_001 is what we want.
We do not need to specify
geo_headers as we are extracting all geographic areas matches the conditions.
Below is the code that gives what we want. The first time you use
read_xxxx() functions to read data files, you will be asked to download data generated from decennial census 2010 and summary files required for this function call, in this case, 2016 ACS 1-year summary files. Choose 1 to continue.
rent <- read_acs1year( year = 2016, states = states_DC, table_contents = "rent = B25064_001", summary_level = "place" ) # Fisrt 5 rows # GEOID NAME STUSAB population rent GEOCOMP SUMLEV lon lat # 1: 16000US0203000 Anchorage municipality, Alaska AK 298192 1296 total 160 -149.27435 61.17755 # 2: 16000US0107000 Birmingham city, Alabama AL 213434 777 total 160 -86.79905 33.52744 # 3: 16000US0121184 Dothan city, Alabama AL 67714 720 total 160 -85.40682 31.23370 # 4: 16000US0135896 Hoover city, Alabama AL 84943 1021 total 160 -86.80558 33.37695 # 5: 16000US0137000 Huntsville city, Alabama AL 196225 766 total 160 -86.53900 34.78427
It is always nice to visualize them on US map.
library(ggplot2) # ggmap requires library(ggmap) # You need to use your own google clound API key register_google("your_google_api_key") us_map <- get_map("united states", zoom = 4, color = "bw") ggmap(us_map) + geom_point( data = rent[order(-population)], aes(lon, lat, size = population/1e3, color = rent) ) + ylim(25, 50) + scale_size_area(breaks = c(100, 200, 500, 1000, 2000, 5000)) + scale_color_continuous(low = "green", high = "red") + labs( color = "monthly\nrent ($)", size = "total\npopulation\n(thousand)", title = "Monthly rent in cities over 65000 population", caption = "Source: 2016 ACS 1-year estimate" ) + theme( panel.background = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), title = element_text(size = 14) )
Only the decennial census has data down to block level. The most recent one is Census 2010.
Knowing names of a city, county, metro area, or town, we can feed them directly to argument
areas. The returned data.table contains the table contents we want as well as population and coordinates. The reading takes a few seconds.
# read data of black population in each block black_popul <- read_decennial( year = 2010, states = "IN", table_contents = "black_popul = P0030003", areas = "South Bend city, IN", summary_level = "block" ) # first 5 rows of black_popul: # area lon lat state population black_popul GEOCOMP SUMLEV # 1: South Bend city, IN -86.21864 41.63613 IN 28 10 all 100 # 2: South Bend city, IN -86.21659 41.63670 IN 0 0 all 100 # 3: South Bend city, IN -86.22172 41.63573 IN 52 16 all 100 # 4: South Bend city, IN -86.22022 41.63182 IN 279 21 all 100 # 5: South Bend city, IN -86.22093 41.63367 IN 42 1 all 100
It is better to separate data manipulation from reading to save reading time as you usually need to try multiple manipulations. Data manipulation can be done with
library(magrittr) # remove blocks where no people lives in and add a column of black percentage. black <- black_popul %>% .[population != 0] %>% # percentage of black population in each block .[, black_pct := round(100 * black_popul / population, 2)]
Again we visualize percentage of black population on map with
south_bend <- get_map("south bend, IN", zoom = 13, color = "bw") ggmap(south_bend) + geom_point( data = black, aes(lon, lat, size = population, color = black_pct) ) + scale_size_area(breaks = c(10, 100, 200, 500)) + scale_color_continuous(low = "green", high = "red") + labs( color = "% black", size = "total\npopulation", title = "Black communities in South Bend city at block level", caption = "Source: Census 2010" ) + theme( panel.background = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), title = element_text(size = 14) )
This package requires downloading census data to your local computer. You will be asked to download data when you call
read_xxxx functions. The downloaded data will be extracted automatically to the folder
A set of data generated from Census 2010 will also be downloaded, which is used to fill missing geographic header records in ACS data.
The census data can be found on Census Bureau's website but you do not need to download them manually. Use the function above.
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