Is there any way to download multiple year data at one time

[waholulu]

I intend to make a plot of how stats change over the years. But when I set year variable to be a sequence of the year. it will have the error like
Error in is.url(url) : length(url) == 1 is not TRUE

How to solve this issue. Thanks

[mfherman]

One approach to get multiple years is to loop over a set of years with the same get_acs()call. I like using the map() family of functions from the purrr package for this.

Note, this approach is similar to the discussion in #121 and #129 in which you can loop over multiple county/state combinations. The main difference is that I loop over a named list of years. This allows me to use the names of the list as the id variable when I bind the the rows into one data frame.

library(tidyverse)
library(tidycensus)

years <- lst(2015, 2016, 2017)

map_dfr(
  years,
  ~ get_acs(
    geography = "county",
    variables = "B01003_001",
    state = "ME",
    year = .x,
    survey = "acs1"
    ),
  .id = "year"
  )
#> Getting data from the 2015 1-year ACS
#> The one-year ACS provides data for geographies with populations of 65,000 and greater.
#> Getting data from the 2016 1-year ACS
#> The one-year ACS provides data for geographies with populations of 65,000 and greater.
#> Getting data from the 2017 1-year ACS
#> The one-year ACS provides data for geographies with populations of 65,000 and greater.
#> # A tibble: 18 x 6
#>    year  GEOID NAME                       variable   estimate   moe
#>    <chr> <chr> <chr>                      <chr>         <dbl> <dbl>
#>  1 2015  23001 Androscoggin County, Maine B01003_001   107233    NA
#>  2 2015  23003 Aroostook County, Maine    B01003_001    68628    NA
#>  3 2015  23005 Cumberland County, Maine   B01003_001   289977    NA
#>  4 2015  23011 Kennebec County, Maine     B01003_001   119980    NA
#>  5 2015  23019 Penobscot County, Maine    B01003_001   152692    NA
#>  6 2015  23031 York County, Maine         B01003_001   201169    NA
#>  7 2016  23001 Androscoggin County, Maine B01003_001   107319    NA
#>  8 2016  23003 Aroostook County, Maine    B01003_001    67959    NA
#>  9 2016  23005 Cumberland County, Maine   B01003_001   292041    NA
#> 10 2016  23011 Kennebec County, Maine     B01003_001   120569    NA
#> 11 2016  23019 Penobscot County, Maine    B01003_001   151806    NA
#> 12 2016  23031 York County, Maine         B01003_001   202343    NA
#> 13 2017  23001 Androscoggin County, Maine B01003_001   107651    NA
#> 14 2017  23003 Aroostook County, Maine    B01003_001    67653    NA
#> 15 2017  23005 Cumberland County, Maine   B01003_001   292500    NA
#> 16 2017  23011 Kennebec County, Maine     B01003_001   121821    NA
#> 17 2017  23019 Penobscot County, Maine    B01003_001   151957    NA
#> 18 2017  23031 York County, Maine         B01003_001   204191    NA

^{Created on 2018-11-12 by the reprex package (v0.2.1)}

[waholulu]

Thanks @mfherman
But this method will remove the geometry in the dataset due to forcing merge the data. But I need to plot this data, so I have to keep to geometry. Is there any other option?

[mfherman]

You have to do a couple extra steps to get the geometry because bind_rows() currently doesn't work on sf objects (see r-spatial/mapedit#46).

The basic approach is the same, but instead of map_dfr() I use map() to keep the results in a list, then add the id column for each year and finally combine them using rbind().

library(tidyverse)
library(tidycensus)

years <- lst(2015, 2016, 2017)

multi_year <- map(
  years,
  ~ get_acs(
    geography = "county",
    variables = "B01003_001",
    state = "ME",
    year = .x,
    survey = "acs1",
    geometry = TRUE
    )
  ) %>% 
  map2(years, ~ mutate(.x, id = .y))

reduce(multi_year, rbind)
#> Simple feature collection with 18 features and 6 fields
#> geometry type:  MULTIPOLYGON
#> dimension:      XY
#> bbox:           xmin: -70.98904 ymin: 42.97776 xmax: -67.75042 ymax: 47.45969
#> epsg (SRID):    4269
#> proj4string:    +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs
#> First 10 features:
#>    GEOID                       NAME   variable estimate moe   id
#> 1  23001 Androscoggin County, Maine B01003_001   107233  NA 2015
#> 2  23003    Aroostook County, Maine B01003_001    68628  NA 2015
#> 3  23005   Cumberland County, Maine B01003_001   289977  NA 2015
#> 4  23011     Kennebec County, Maine B01003_001   119980  NA 2015
#> 5  23019    Penobscot County, Maine B01003_001   152692  NA 2015
#> 6  23031         York County, Maine B01003_001   201169  NA 2015
#> 7  23001 Androscoggin County, Maine B01003_001   107319  NA 2016
#> 8  23003    Aroostook County, Maine B01003_001    67959  NA 2016
#> 9  23005   Cumberland County, Maine B01003_001   292041  NA 2016
#> 10 23011     Kennebec County, Maine B01003_001   120569  NA 2016
#>                          geometry
#> 1  MULTIPOLYGON (((-70.48529 4...
#> 2  MULTIPOLYGON (((-70.01975 4...
#> 3  MULTIPOLYGON (((-69.94153 4...
#> 4  MULTIPOLYGON (((-70.13259 4...
#> 5  MULTIPOLYGON (((-69.35567 4...
#> 6  MULTIPOLYGON (((-70.61725 4...
#> 7  MULTIPOLYGON (((-70.48529 4...
#> 8  MULTIPOLYGON (((-70.01975 4...
#> 9  MULTIPOLYGON (((-69.94153 4...
#> 10 MULTIPOLYGON (((-70.13259 4...

^{Created on 2018-11-12 by the reprex package (v0.2.1)}

[waholulu]

Thanks @mfherman
But seems like still something wrong keep from plotting the df.

library(tidyverse)
library(tidycensus)
years <- lst(2013,2014,2015)
multi_year <- map(years,  ~ get_acs(geography = "county",   variables = "B01003_001",   state = "NC",  year = .x,  geometry = TRUE)) %>% 
  map2(years, ~ mutate(.x, id = .y))
nc <- reduce(multi_year, rbind)
ggplot(nc) + geom_sf(aes(fill = estimate), color = NA) +coord_sf(datum = NA) + facet_wrap(~id)

> ggplot(nc) + geom_sf(aes(fill = estimate), color = NA) +coord_sf(datum = NA)   +  facet_wrap(~id)
Error in st_sfc(NextMethod(), crs = st_crs(old), precision = st_precision(old)) : 
  found multiple dimensions: XY XYZ

[mfherman]

Hmm...It's working on my end. Maybe try updating to the latest versions of ggplot2 and sf?

Aside from the code, another issue is that you probably shouldn't be comparing overlapping 5-year datasets (see https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html). In the code you posted, you are getting data from 2009-2013, 2010-2014, and 2011-2015. To compare across these years, you would need to use the ACS 1-year estimates. If you want to use 5-year estimates, you could compare say 2007-2011 to 2012-2016.

library(tidyverse)
library(tidycensus)

years <- lst(2013, 2014, 2015)

multi_year <-
  map(
    years,
    ~ get_acs(
      geography = "county",
      variables = "B01003_001",
      state = "NC",
      year = .x,
      geometry = TRUE
      )
  ) %>%
  map2(years, ~ mutate(.x, id = .y))

nc <- reduce(multi_year, rbind)

ggplot(nc) +
  geom_sf(aes(fill = estimate), color = NA) +
  coord_sf(datum = NA) +
  facet_wrap(~ id)

^{Created on 2018-11-12 by the reprex package (v0.2.1)}