Kyle Walker walkerke

## autoloess.R
# autoloess.R: compute loess metaparameters automatically
# Kyle Gorman <gormanky@ohsu.edu>

aicc.loess <- function(fit) {
    # compute AIC_C for a LOESS fit, from:
    #
    # Hurvich, C.M., Simonoff, J.S., and Tsai, C. L. 1998. Smoothing
    # parameter selection in nonparametric regression using an improved
    # Akaike Information Criterion. Journal of the Royal Statistical
    # Society B 60: 271–293.

## code.R
#devtools::install("jeroenooms/V8")
library("V8")
library(pipeR)

ct = new_context("window")
# min.js gives me a call stack size error but non-min works fine
ct$source(
  "https://raw.githubusercontent.com/morganherlocker/turf/master/turf.js"
)
# one of the examples from turf API docs

## nyt_style_buildings.R
library(tidyverse)
library(sf)
library(tigris)

# start by picking a state from https://github.com/Microsoft/USBuildingFootprints
# WARNING: these files can be pretty big. using arizona for its copious subdivisions and reasoanable 83MB.
url_footprint <- "https://usbuildingdata.blob.core.windows.net/usbuildings-v1-1/Arizona.zip"
download.file(url_footprint, "Arizona.zip")
unzip("Arizona.zip")

## ahealth.md

      
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              June 30, 2019 12:09
            
              
                R+ggplot2 version of the "dumbbell" plot at http://census.gov/content/dam/Census/newsroom/releases/2015/cb15-158_graphic_acs_metro.jpg
              
          
    This hit #rstats today:
Has anyone made a dumbbell dot plot in #rstats, or better yet exported to @plotlygraphs using the API? https://t.co/rWUSpH1rRl
— Ken Davis (@ken_mke) October 23, 2015
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>
So, I figured it was worth a cpl mins to reproduce.
While the US gov did give the data behind the chart it was all the data and a pain to work with so I used WebPlotDigitizer to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.
It is possible to make the top "dumbbell" legend in ggplot2 (but not by using a guide) and color the "All Metro A

  
## population_pyramid.R
library(magrittr)
library(dplyr)
library(ggplot2)

population <- read.csv("https://raw.githubusercontent.com/andriy-gazin/datasets/master/ageSexDistribution.csv")

population %<>%
  tidyr::gather(sex, number, -year, - ageGroup) %>%
  mutate(ageGroup = gsub("100 і старше", "≥100", ageGroup),
         ageGroup = factor(ageGroup,

## sql.sql

--  Convert your states (or other geometries id'd by state) to
--    display as with Alaska, Hawaii, and Puerto Rico transcaled
--
--  @param g: input geometry
--  @param state: column identifying the state (name, postal abbreviation, state FP)
--
--  output: geometries of states in albers projections of the states
--
-- Projections:

## coord_proj.R
#' @export
coord_proj <- function(proj="+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
                       inverse = FALSE, degrees = TRUE,
                       ellps.default="sphere", xlim = NULL, ylim = NULL) {
  try_require("proj4")
  coord(
    proj = proj,
    inverse = inverse,
    ellps.default = ellps.default,
    degrees = degrees,

## README.md

      
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              February 7, 2017 11:36
            
              
                Mapbox GL JS choropleth with hover and sidebar tooltips
              
          
    Choropleth example using Mapbox GL JS

Choropleth using an external geojson (DC Census Tracts by Population Change - 2000), data-driven styling, and queryRenderedFeatures to show hovered polygon's features in a sidebar tooltip.

  
## index.html
<!DOCTYPE html>
<html>
<head>
  <meta charset='utf-8' />
  <title></title>
  <link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.26.0/mapbox-gl.css' rel='stylesheet' />
  <link href='https://www.mapbox.com/base/latest/base.css' rel='stylesheet' />
  <link href='site.css' rel='stylesheet' />
</head>
<body class='clip loading'>

## acsdownforeveryoneorjustme.r
purrr::map_df(2009:2014, function(i) {
  api_info <- jsonlite::fromJSON(sprintf("http://api.census.gov/data/%s/acs5/", i))
  api_info$dataset[, c("title", "c_unavailableMessage")]
})
	# autoloess.R: compute loess metaparameters automatically
	# Kyle Gorman <gormanky@ohsu.edu>

	aicc.loess <- function(fit) {
	# compute AIC_C for a LOESS fit, from:
	#
	# Hurvich, C.M., Simonoff, J.S., and Tsai, C. L. 1998. Smoothing
	# parameter selection in nonparametric regression using an improved
	# Akaike Information Criterion. Journal of the Royal Statistical
	# Society B 60: 271–293.
	#devtools::install("jeroenooms/V8")
	library("V8")
	library(pipeR)

	ct = new_context("window")
	# min.js gives me a call stack size error but non-min works fine
	ct$source(
	"https://raw.githubusercontent.com/morganherlocker/turf/master/turf.js"
	)
	# one of the examples from turf API docs
	library(tidyverse)
	library(sf)
	library(tigris)

	# start by picking a state from https://github.com/Microsoft/USBuildingFootprints
	# WARNING: these files can be pretty big. using arizona for its copious subdivisions and reasoanable 83MB.
	url_footprint <- "https://usbuildingdata.blob.core.windows.net/usbuildings-v1-1/Arizona.zip"
	download.file(url_footprint, "Arizona.zip")
	unzip("Arizona.zip")
	library(magrittr)
	library(dplyr)
	library(ggplot2)

	population <- read.csv("https://raw.githubusercontent.com/andriy-gazin/datasets/master/ageSexDistribution.csv")

	population %<>%
	tidyr::gather(sex, number, -year, - ageGroup) %>%
	mutate(ageGroup = gsub("100 і старше", "≥100", ageGroup),
	ageGroup = factor(ageGroup,

	-- Convert your states (or other geometries id'd by state) to
	-- display as with Alaska, Hawaii, and Puerto Rico transcaled
	--
	-- @param g: input geometry
	-- @param state: column identifying the state (name, postal abbreviation, state FP)
	--
	-- output: geometries of states in albers projections of the states
	--
	-- Projections:
	#' @export
	coord_proj <- function(proj="+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
	inverse = FALSE, degrees = TRUE,
	ellps.default="sphere", xlim = NULL, ylim = NULL) {
	try_require("proj4")
	coord(
	proj = proj,
	inverse = inverse,
	ellps.default = ellps.default,
	degrees = degrees,
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset='utf-8' />
	<title></title>
	<link href='https://api.tiles.mapbox.com/mapbox-gl-js/v0.26.0/mapbox-gl.css' rel='stylesheet' />
	<link href='https://www.mapbox.com/base/latest/base.css' rel='stylesheet' />
	<link href='site.css' rel='stylesheet' />
	</head>
	<body class='clip loading'>
	purrr::map_df(2009:2014, function(i) {
	api_info <- jsonlite::fromJSON(sprintf("http://api.census.gov/data/%s/acs5/", i))
	api_info$dataset[, c("title", "c_unavailableMessage")]
	})