I use data from the US Census Bureau’s American Community Survey all of the time. I also use R all of the time. Naturally, this means that I often use ACS data in R – which is pertinent given last week’s release of the new 2010-2014 ACS estimates. I wanted easy access to the data to facilitate my on-going research on demographic trends in US metros, and work at the TCU Center for Urban Studies; as such, I wrote a small R package to provide quick access to the data, acs14lite (https://github.com/walkerke/acs14lite). This is not intended to be comparable to, or a replacement for, the existing ACS package in R; it is more for my personal convenience, but I thought it might be useful to others as well. This is mostly going to be a side project for me, so I don’t have plans for a CRAN submission at this time.

Install from GitHub with the following command in R:

devtools::install_github('walkerke/acs14lite')

Accessing the US Census Bureau’s API requires an API key, which you can get from here: http://api.census.gov/data/key_signup.html. You can then set it globally in your acs14lite session:

library(acs14lite)

set_api_key('your API key here')

There is one main function in the package: acs14. From here, you can request data for the following geographies: the entire US, regions, divisions, states, counties, Census tracts, and Census block groups. These are the geographies that I generally use, and I don’t have plans at the moment to add more; I would welcome pull requests, however.

The acs14 function has the following parameters:

• api_key: If you’ve set your API key already with set_api_key, you don’t need to provide this.
  
• geography: One of ‘us’ (the default), ‘region’, ‘division’, ‘state’, ‘county’, ‘tract’, or ‘block group’.
  
• variable: A character string representing the Census variable name you want, or a vector of multiple variable names. Defaults to ‘B01001_001E’, which is total population. You can use the ACS package to look for variable names with its acs.lookup function; remember to add E for estimate and M for margin of error to the end of your variable name.
  
• state: The name of the state for which you want data; applicable to counties, tracts, and block groups.
  
• county: The name of the county for which you want data: applicable to tracts and block groups.

The function returns an R data frame with the data you want for your requested geography.

Additionally, I’ve written a few functions to help users work with margins of error in the ACS. Margins of error for the raw data are provided from the API; however, we often calculate new variables based on the ACS estimates, which in turn will have their own respective margins of error. I’ve used the guidelines in Appendix 3 here: https://www.census.gov/content/dam/Census/library/publications/2008/acs/ACSGeneralHandbook.pdf to write the following functions:

• moe_sum: calculates a margin of error for a derived sum of ACS estimates
• moe_prop: calculates a margin of error for a proportion
• moe_ratio: calculates a margin of error for a ratio
• moe_product: calculates a margin of error for a product

Below, I provide a couple examples of how you can use the package.

library(ggplot2)
library(plotly)
library(dplyr)

wy_income <- acs14(geography = 'county', variable = c('B19013_001E', 'B19013_001M'), state = 'WY')

wy2 <- wy_income %>%
  mutate(name = gsub(" County, Wyoming", "", wy_income$NAME),
         low = B19013_001E - B19013_001M,
         high = B19013_001E + B19013_001M) %>%
  select(name, low, high, estimate = B19013_001E) %>%
  arrange(desc(estimate))

g <- ggplot(wy2, aes(x = estimate, y = reorder(name, estimate))) +
  geom_point() +
  geom_errorbarh(aes(xmin = low, xmax = high)) +
  xlab("Median household income, 2010-2014 ACS estimate") +
  ylab("")


ggplotly(g) %>% layout(margin = list(l = 120))

library(tigris)
library(CartoDB) # devtools::install_github("becarioprecario/cartodb-r/CartoDB", dep = TRUE)
library(rgdal)

la_poverty <- acs14(geography = 'tract', state = 'CA', county = 'Los Angeles',
                    variable = c('B17001_001E', 'B17001_001M', 'B17001_002E', 'B17001_002M'))

la2 <- la_poverty %>%
  mutate(geoid = paste0(state, county, tract),
         pctpov = round(100 * (B17001_002E / B17001_001E), 1),
         moepov = round(100 * (moe_prop(B17001_002E, B17001_001E, B17001_002M, B17001_001M)), 1)) %>%
  select(geoid, pctpov, moepov)

cdb_name <- 'your CartoDB username here'
cdb_key <- 'your CartoDB API key here'

cartodb(cdb_name, cdb_key)

la_tracts <- tracts('CA', 'Los Angeles', cb = TRUE)

la_tracts2 <- geo_join(la_tracts, la2, "GEOID", "geoid")

r2cartodb(la_tracts2, 'la_poverty')

# Now, head to your CartoDB account to style your map!