Searching and Mapping the Census

Searching for the Asian Population via the Census

To use tidycensus, there are limitations imposed by the available tables. There is ACS – a survey of about 3 million people – and the two main decennial census files [SF1] and [SF2]. I will search SF1 for the Asian population.

library(tidycensus); library(kableExtra)
library(tidyverse); library(stringr)
v10 <- load_variables(2010, "sf1", cache = TRUE)
v10 %>% filter(str_detect(concept, "ASIAN")) %>% filter(str_detect(label, "Female")) %>% kable() %>% scroll_box(width = "100%")

There are still 268 to comb through but what seems useful are the bottom rows of the first set of results.

library(viridis)

## Loading required package: viridisLite

library(sf); library(ggthemes)

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

# I need supply the names of the variables to fetch from Census: column `name` above
vars10 <- c("P012D031", "P012D032", "P012D033", "P012D034", "P012D035", "P012D036")
# Get the census data with the relevant map by county; 2010 is the most recent
# I used the example from the help file to adapt by dropping the state to get them all and shifting Hawaii and Alaska for visibility.  They are not properly scaled.
MapDat <- get_decennial(geography = "county", variables = vars10, year = 2010,
                    geometry = TRUE, shift_geo = TRUE)

## Getting data from the 2010 decennial Census

## Using feature geometry obtained from the albersusa package

## Please note: Alaska and Hawaii are being shifted and are not to scale.

# Map the data with ggplot using geom_sf()
ggplot(MapDat, aes(fill = value, color = value)) + 
  geom_sf() + 
  theme_map() + 
  scale_fill_viridis_c(guide=FALSE)+ 
  scale_color_viridis_c(guide=FALSE) + 
  facet_wrap(~variable)

Totals of the Target Population

## `summarise()` ungrouping output (override with `.groups` argument)

A plotly

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

PLM <- ggplotly(PlotlyMe)
library(widgetframe)

## Loading required package: htmlwidgets

htmlwidgets::saveWidget(
  widgetframe::frameableWidget(PLM), here:::here('static/img/widgets/plm11map.html'))

To get rid of some of the messiness because LA is a huge outlier, perhaps it should be binned.

MapDatSum <- MapDatSum %>% mutate(Binned.Total = cut_number(Total, 5, ordered_result=TRUE))
PlotlyMe <- ggplot(MapDatSum, aes(fill = Binned.Total, text=NAME)) +
     geom_sf(size=0.02, color="white") + theme_map() + scale_fill_viridis_d() +         labs("Asian Females Ages 18 to 34", caption = "Census Table SF1")  
PlotlyMe

More than 1000 in the Relevant Age Group

MapDatSum <- MapDatSum %>% mutate(Total.1000 = as.factor(Total > 1000))
PlotlyMe <- ggplot(MapDatSum, aes(fill = Total.1000, text=NAME)) +
     geom_sf(size=0.02, color="white") + theme_map() + scale_fill_viridis_d() +         labs("Asian Females Ages 18 to 34", caption = "Census Table SF1", fill="More than 1000")  
PlotlyMe

More than 5000 in the Relevant Age Group

MapDatSum <- MapDatSum %>% mutate(Total.5000 = as.factor(Total > 5000))
PlotlyMe <- ggplot(MapDatSum, aes(fill = Total.5000)) +
     geom_sf(size=0.02, color="white") + theme_map() + scale_fill_viridis_d() +         labs("Asian Females Ages 18 to 34", caption = "Census Table SF1", fill="More than 5000")  
PlotlyMe

More than 10000 in the Relevant Age Group

MapDatSum <- MapDatSum %>% mutate(Total.10K = as.factor(Total > 10000))
PlotlyMe <- ggplot(MapDatSum, aes(fill = Total.10K)) +
     geom_sf(size=0.02, color="white") + theme_map() + scale_fill_viridis_d() +         labs("Asian Females Ages 18 to 34", caption = "Census Table SF1", fill="More than 10000")  
PlotlyMe

# This takes a really long time to run.  It is not evaluated.
library(cartogram)
Carto <- cartogram_cont(
MapDatSum,
"Total",
itermax = 15,
maxSizeError = 1.0001,
prepare = "adjust",
threshold = 0.05
)

Load the cartogram already cooked.

load(url("https://github.com/robertwwalker/DADMStuff/raw/master/Cartogram.RData"))
ggplot(Carto, aes(fill=Total)) + geom_sf(size=0.01) + theme_map() + labs(caption="A Cartogram")