--- | |
title: Child Poverty and Race, USA 2016 | |
author: Ciaran | |
date: '2018-05-01' | |
slug: child-poverty-and-race-usa | |
categories: | |
- rstats | |
tags: | |
- tidy_tuesday | |
- inequality | |
- race | |
header: | |
caption: '' | |
image: '' | |
--- | |
```{r setup, echo=FALSE, include=FALSE} | |
knitr::opts_chunk$set(cache = TRUE, | |
echo = FALSE) | |
library(tidyverse) | |
library(cowplot) | |
library(kableExtra) | |
``` | |
This week's [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday) invited us to examine county-level census data from 2016 in the United States (sourced [here](https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml)). I have been poking around in the data and discovered an interesting visual artefact in the following otherwise unsurprising quick plot: | |
```{r poverty_unemployment} | |
county_data_with_region <- county_data %>% # Divide states by regions | |
mutate(Region = case_when(State == "Connecticut" | | |
State == "Maine" | | |
State == "Massachusetts" | | |
State == "New Hampshire" | | |
State == "Rhode Island" | | |
State == "Vermont" | | |
State == "New Jersey" | | |
State == "New York" | | |
State == "Pennsylvania" ~ "Northeast", | |
State == "Illinois" | | |
State == "Indiana" | | |
State == "Michigan" | | |
State == "Ohio" | | |
State == "Wisconsin" | | |
State == "Iowa" | | |
State == "Kansas" | | |
State == "Minnesota" | | |
State == "Missouri" | | |
State == "Nebraska" | | |
State == "North Dakota" | | |
State == "South Dakota" ~ "Midwest", | |
State == "Delaware" | | |
State == "Florida" | | |
State == "Georgia" | | |
State == "Maryland" | | |
State == "North Carolina" | | |
State == "South Carolina" | | |
State == "Virginia" | | |
State == "District of Columbia" | | |
State == "West Virginia" | | |
State == "Alabama" | | |
State == "Kentucky" | | |
State == "Mississippi" | | |
State == "Tennessee" | | |
State == "Arkansas" | | |
State == "Louisiana" | | |
State == "Oklahoma" | | |
State == "Texas" ~ "South", | |
State == "Arizona" | | |
State == "Colorado" | | |
State == "Idaho" | | |
State == "Montana" | | |
State == "Nevada" | | |
State == "New Mexico" | | |
State == "Utah" | | |
State == "Wyoming" | | |
State == "Alaska" | | |
State == "California" | | |
State == "Hawaii" | | |
State == "Oregon" | | |
State == "Washington" ~ "West", | |
TRUE ~ "Puerto Rico")) %>% | |
filter(Region != "Puerto Rico") | |
``` | |
```{r make_plot} | |
poverty_plot <- ggplot(county_data_with_region, aes(White, ChildPoverty)) + # Compare Child Poverty to total white people, by region | |
geom_point(na.rm = TRUE, alpha = 0.2, colour = "lightblue") + | |
geom_smooth(method = loess, na.rm = TRUE) + | |
scale_color_brewer(type = "seq", palette = "Blues") + | |
facet_grid(. ~ Region) + | |
background_grid(major = "xy", minor = "none") + | |
ggtitle("Child poverty falls as the proportion of white people increases.", subtitle = "Note the 'tipping point' at about 75White (%) in the South and Northeast") + | |
xlab("White (%)") + | |
ylab("% children in poverty") | |
poverty_plot | |
``` | |
I split the USA's `r prettyNum(nrow(county_data_with_region), big.mark = ",")` counties (excluding Puerto Rico) into regions, based on the US Census Bureau's [four statistical regions](https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf) (pdf). I excluded Puetro Rico because its racial breakdown does not fit the exercise here. And I chose to classify counties by White (%) because that seems the best way to capture the percentage of the population from racial minorities. No doubt this is a hugely crude way to go about the exercise. | |
Anyway, even with my basic familiarity with the United States I am hardly surprised to see child poverty correlate negatively with the proportion of the population that is white. What _is_ surprising I think is the way that child poverty seems higher in the Northeast and especially in the South in more homogeneous counties. | |
Still, we have to be careful. When we look at the Southern counties what we are seeing here _might_ relate to a combination of [rural poverty](https://www.prb.org/childpoverty/) and an uneven distribution of people by race. Indeed, it is very striking _how_ segregated the United States are. | |
```{r segregation} | |
county_data_south <- county_data_with_region %>% filter(Region == "South") | |
segregation <- county_data_south %>% | |
mutate(segregated = case_when(Hispanic >= 75 | #Test for any county having a 75% of more racial share | |
White >= 75 | | |
Black >= 75 | | |
Native >= 75 | | |
Asian >= 75 | | |
Pacific >= 75 ~ "Yes", TRUE ~ "No")) %>% | |
group_by(segregated, State) %>% # Group by whether segregated plus by state | |
summarise(Segregated2 = n()) %>% | |
tidyr::spread(., segregated, Segregated2) %>% # Spread to "yes" or "no" columns | |
mutate_all(funs(replace(., is.na(.), 0))) %>% # Replace NAs | |
mutate(Counties = (Yes+No)) %>% # Total number of counties | |
mutate(`Segregated (%)` = round((Yes/Counties)*100, digits = 0)) %>% #Get segregated % | |
select(State, Counties, Segregated = Yes, `Not Segregated` = No, `Segregated (%)`) %>% | |
arrange(desc(`Segregated (%)`)) | |
knitr::kable(segregation, format = "html", align = "l", caption = "Counties with population composed of 75% or more from one racial group") %>% | |
kable_styling(full_width = FALSE, bootstrap_options = "striped") | |
``` | |
But note also that each state (and I imagine at more granular levels than that) has its own population distributions: | |
```{r plot2, warning=FALSE} | |
south_plot <- ggplot(county_data_south, aes(White, ChildPoverty)) + | |
geom_point(na.rm = TRUE, alpha = 0.2, colour = "lightblue") + | |
geom_smooth(method = loess, na.rm = TRUE) + | |
facet_wrap(~ State, ncol = 4) + | |
scale_color_brewer(type = "seq", palette = "Blues") + | |
background_grid(major = "xy", minor = "none") + | |
xlab("White (%)") + | |
ylab("% children in poverty") + | |
theme(axis.ticks.y = element_blank(), | |
axis.text.y = element_blank()) | |
south_plot | |
``` | |
So, as always, more study required. | |
Gist [here](https://gist.github.com/cokelly/0f6a6cc73d372989408d0fab73c7de04). |
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