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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