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Indexing Iterations with set_names()

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As mentioned last time, we often want to build up a data frame iteratively. The map() family of functions in purrr can help with this. Here I’ll show a handy pattern for keeping track of what you’ve added to the data frame you’re making.

The map_dfr() function will take a vector, apply a function to each element of it, and then return the results as a data frame bound row-by-row. For example, the U.S. Census Bureau provides annual estimates of median household income for areas in the United States. We can get them using Kyle Walker’s tidycensus package.

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library(tidyverse)
library(tidycensus)


out <- get_acs(geography = "county", 
                    variables = "B19013_001",
                    state = "NY", 
                    county = "New York", 
                    survey = "acs1",
                    year = 2005)

out

## # A tibble: 1 × 5
##   GEOID NAME                      variable   estimate   moe
##   <chr> <chr>                     <chr>         <dbl> <dbl>
## 1 36061 New York County, New York B19013_001    55973  1462

We could get all the counties in New York state (and in the country) if we wanted this way, for any particular year.

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out <- get_acs(geography = "county", 
                    variables = "B19013_001",
                    state = "NY", 
                    survey = "acs1",
                    year = 2005)

out

## # A tibble: 38 × 5
##    GEOID NAME                         variable   estimate   moe
##    <chr> <chr>                        <chr>         <dbl> <dbl>
##  1 36001 Albany County, New York      B19013_001    50054  2030
##  2 36005 Bronx County, New York       B19013_001    29228   853
##  3 36007 Broome County, New York      B19013_001    36394  2340
##  4 36009 Cattaraugus County, New York B19013_001    37580  2282
##  5 36011 Cayuga County, New York      B19013_001    42057  2406
##  6 36013 Chautauqua County, New York  B19013_001    35495  2077
##  7 36015 Chemung County, New York     B19013_001    37418  3143
##  8 36019 Clinton County, New York     B19013_001    44757  3500
##  9 36027 Dutchess County, New York    B19013_001    61889  2431
## 10 36029 Erie County, New York        B19013_001    41967  1231
## # … with 28 more rows

What if we want the results for every available year? As with the GSS, we could write an explicit loop to download each piece and then bind them all together into a data frame. But it’s better to use map_dfr() to do this for us. In the process, we’ll take advantage of a nice way to make sure our rows are properly indexed by year.

There’s a set_names() function that can be used to attach labels to the elements of a vector:

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x <- c(1:10)

x

##  [1]  1  2  3  4  5  6  7  8  9 10

x <- set_names(x, nm = letters[1:10])

x

##  a  b  c  d  e  f  g  h  i  j 
##  1  2  3  4  5  6  7  8  9 10

By default, set_names() will label a vector with that vector’s values:

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c(1:10) %>% 
  set_names()

##  1  2  3  4  5  6  7  8  9 10 
##  1  2  3  4  5  6  7  8  9 10

Meanwhile, map_dfr() has an .id argument that lets you add a row-identifier to whatever you are binding. Like this:

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df <- 2005:2019 %>% 
  map_dfr(~ get_acs(geography = "county", 
                    variables = "B19013_001",
                    state = "NY", 
                    county = "New York", 
                    survey = "acs1",
                    year = .x), 
        .id = "id")

df

## # A tibble: 15 × 6
##    id    GEOID NAME                      variable   estimate   moe
##    <chr> <chr> <chr>                     <chr>         <dbl> <dbl>
##  1 1     36061 New York County, New York B19013_001    55973  1462
##  2 2     36061 New York County, New York B19013_001    60017  1603
##  3 3     36061 New York County, New York B19013_001    64217  2002
##  4 4     36061 New York County, New York B19013_001    69017  1943
##  5 5     36061 New York County, New York B19013_001    68706  1471
##  6 6     36061 New York County, New York B19013_001    63832  2125
##  7 7     36061 New York County, New York B19013_001    66299  1783
##  8 8     36061 New York County, New York B19013_001    67099  1640
##  9 9     36061 New York County, New York B19013_001    72190  2200
## 10 10    36061 New York County, New York B19013_001    76089  2016
## 11 11    36061 New York County, New York B19013_001    75575  2566
## 12 12    36061 New York County, New York B19013_001    77559  2469
## 13 13    36061 New York County, New York B19013_001    85071  3520
## 14 14    36061 New York County, New York B19013_001    85066  3480
## 15 15    36061 New York County, New York B19013_001    93651  3322

Our id column tracks the year. But we’d like it to be the year. So, we use set_names(), and also name it year when we create it:

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df <- 2005:2019 %>% 
  set_names() %>% 
  map_dfr(~ get_acs(geography = "county", 
                    variables = "B19013_001",
                    state = "NY", 
                    county = "New York", 
                    survey = "acs1",
                    year = .x), 
        .id = "year") %>% 
  mutate(year = as.integer(year))

df

## # A tibble: 15 × 6
##     year GEOID NAME                      variable   estimate   moe
##    <int> <chr> <chr>                     <chr>         <dbl> <dbl>
##  1  2005 36061 New York County, New York B19013_001    55973  1462
##  2  2006 36061 New York County, New York B19013_001    60017  1603
##  3  2007 36061 New York County, New York B19013_001    64217  2002
##  4  2008 36061 New York County, New York B19013_001    69017  1943
##  5  2009 36061 New York County, New York B19013_001    68706  1471
##  6  2010 36061 New York County, New York B19013_001    63832  2125
##  7  2011 36061 New York County, New York B19013_001    66299  1783
##  8  2012 36061 New York County, New York B19013_001    67099  1640
##  9  2013 36061 New York County, New York B19013_001    72190  2200
## 10  2014 36061 New York County, New York B19013_001    76089  2016
## 11  2015 36061 New York County, New York B19013_001    75575  2566
## 12  2016 36061 New York County, New York B19013_001    77559  2469
## 13  2017 36061 New York County, New York B19013_001    85071  3520
## 14  2018 36061 New York County, New York B19013_001    85066  3480
## 15  2019 36061 New York County, New York B19013_001    93651  3322

Now year is just the year. The year column will be created as a character vector, so we convert it back to an integer again at the end. (Thanks to Dan Hicks for reminding me about this.)

The result is that we can now do something like this:

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## Every county this time.
2005:2019 %>% 
  set_names() %>% 
  map_dfr(~ get_acs(geography = "county", 
                    variables = "B19013_001",
                    state = "NY", 
                    survey = "acs1",
                    year = .x), 
        .id = "year") %>% 
  mutate(year = as.integer(year)) %>% 
  ggplot(mapping = aes(x = year, y = estimate, group = year)) + 
  geom_boxplot(fill = "lightblue", alpha = 0.5, outlier.alpha = 0) + 
  geom_jitter(position = position_jitter(width = 0.1), shape = 1) +
  scale_y_continuous(labels = scales::label_dollar()) + 
  labs(x = "Year", y = "Dollars", 
  title = "Median Household Income by County in New York State, 2005-2019", 
  subtitle = "ACS 1-year estimates", 
  caption = "Data: U.S. Census Bureau.") + 
  theme_minimal()

Household income in New York State.

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